The metabolic tumor volume (MTV) was calculated using a semi-automatic thresholding method and the average tracer uptake within the MTV was converted to a standard uptake value (SUV).When GB was confirmed on T2- and contrast-enhanced T1-weighted MRI, animals were randomized into a treatment group (n = 5) receiving MRI-guided 3D conformal arc micro-irradiation (20 Gy) with concomitant temozolomide, and a sham group (n = 5). Effect of treatment was evaluated by MRI and [18F]FDG PET (SUVmean x MTV) is superior to MTV only. Using (SUVmean x MTV), [18F]FDG PET detects treatment effect starting as soon as day 5 post-therapy, comparable to contrast-enhanced MRI. Importantly, [18F]FDG PET at delayed time intervals (240 min p.i.) was able to detect the treatment effect earlier, starting at day 2 post-irradiation. No significant differences were found at any time point for both the MTV and (SUVmean x MTV) of [18F]FCho PET.To detect treatment response, we found that for [18F]FDG PET were able to detect early treatment responses in GB rats, whereas, in this study this was not possible using [18F]FCho PET. Further comparative studies should corroborate these results and should also include (different) amino acid PET tracers.Both MRI and particularly delayed [ As a result, for newly diagnosed glioblastoma (GB) patients with a good performance status, the standard of care now includes maximal surgical resection followed by combined external beam RT (60 Gy in 30 fractions) and TMZ fluorodeoxyglucose ([18F]FDG), [18F]Fluoroethyltyrosine ([18F]FET), [18F]fluoroazomycin arabinoside ([18F]FAZA), 3,4-dihydroxy-6-[18F]-fluoro-l-phenylalanine ([18F]FDOPA) and [18F]Fluoromethylcholine ([18F]FCho) [18F]FDG and [18F]FET. [18F]FDG PET measures cellular glucose metabolism as a function of the hexokinase enzyme. However, due to its high uptake in normal brain parenchyma, the localization and the delineation of brain tumors is often difficult [18F]FDG and PET acquisition, the so called “dual phase imaging” FDG such as radiolabeled amino acids were developed showing an increased contrast between brain tumors and normal brain tissue. The diagnostic potential of [18F]FET PET in brain tumors is well documented and the RANO working group has recommended amino acid PET as an additional tool in the diagnostic assessment of brain tumors [18F]FET PET compared with MRI and a promising role for the distinction between tumor recurrence and aspecific post-therapeutic changes has been shown [18F]FAZA as a PET tracer may also have clinical relevance because tumor aggressiveness, failure to achieve local tumor control and an increased rate of tumor recurrence are all associated with hypoxia [18F]FAZA PET is that optimal imaging is performed a few hours post-injection and that the degree of hypoxia can theoretically fluctuate, influenced by therapy and the presence of acute versus chronic hypoxia [18F]FCho PET has been shown to be able to distinguish high-grade glioma, brain metastases and benign lesions and to identify the most malignant areas for stereotactic sampling [et al. concluded that [18F]FCho PET was able to differentiate WHO  grade IV from grade II and III tumors, whereas MR spectroscopy differentiated grade III/IV from grade II tumors [18F]FCho PET/CT in the intraoperative management or radio-surgical approaches for glioma has been suggested, including intraoperative guidance in conjunction with MR spectroscopy FCho) –24. Currifficult , 24. It imaging” –27. Alson tumors . Also, aen shown , 30. Hyp hypoxia , 31, 32. hypoxia . Finally hypoxia , 35. Recsampling . Grech-SI tumors . Recentltroscopy , 40, 41.®, Surrey, UK). Using the F98 GB rat model and the SARRP, we described and validated magnetic resonance imaging (MRI)-guided 3D conformal arc RT with concomitant chemotherapy to bridge the gap between radiation technology in the clinic and preclinical techniques [18F]FDG and [18F]FCho PET, compared to contrast-enhanced MRI, to detect the early effect of combined radiation and TMZ treatment in the F98 GB rat model. In addition, we also investigated which modality is best suited for the early detection of treatment response.Previously, our group used the orthotopic allograft F98 GB rat model to mimic GB treatment in patients. The GB F98 rat model exhibited features of human GB with regard to its aggressiveness, histological appearance and lack of immunogenicity . To enabchniques . In thisThe study was approved by the Ghent University Ethical Committee for animal experiments (ECD 09/23-A). All animals were kept under environmentally controlled conditions  with food and water ad libitum. Follow-up of all animals was done by monitoring their body weight, food, water intake and their activity and normal behavior. The method of euthanasia was a lethal dose of pentobarbital sodium (180 mg/kg). Euthanasia was performed prior to the experimental endpoint if a decline of 20% body weight was observed or when the normal behavior severely deteriorated (e.g. lack of grooming).®) . Full details of the protocol can be found in our previous publications FDG and [18F]FCho. [18F]FDG scans were performed 2, 5, 9 and 12 days after the start of treatment, while [18F]FCho scans were performed 1, 6, 8 and 13 days after the start of treatment. These time points were arbitrarily chosen because, empirically, GB rats survived approximately 14 days after the start of treatment [18F]FDG and [18F]FCho PET scanning was not possible on the same day. An overview of the complete imaging scheme is shown in The assessment of the biological response was evaluated by small animal PET using FDG and 39.55 ± 0.37 [18F]FCho (mean ± SE) dissolved in 200 μL saline). The total acquisition time was 20 min for [18F]FCho PET due the fast kinetics of [18F]FCho and 60 min for conventional [18F]FDG PET. In addition, a 30-min [18F]FDG PET scan was acquired 240 min after [18F]FDG administration (delayed imaging). All PET scans were reconstructed into a 200 × 200 × 64 matrix by a 2D Maximum Likelihood Expectation Maximization (MLEM) algorithm  using 60 iterations and a voxel size of 0.5 × 0.5 × 1.157 mm. Identical reconstruction parameters were applied for [18F]FDG and [18F]FCho PET. The dynamically acquired PET data were reconstructed into 6 × 20 s, 3 × 1 min, 3 × 5 min, 2 × 20 min time frames for [18F]FDG scans and 6 × 20 s, 3 × 1 min, 1 × 5 min, 1 × 10 min time frames for [18F]FCho scans.Dynamic PET images were acquired in list mode using a dedicated small animal PET scanner . MTV was defined on the last time frame of the dynamic [18F]FDG PET (40–60 min post-injection), the delayed [18F]FDG PET (240 min post-injection) and on the last time frame of the dynamic [18F]FCho scan (10–20 min post-injection). First, a circular VOI is manually placed over a region with an increased tracer uptake excluding non-specific uptake, such as uptake in the scalp. Within this VOI, MTV was defined as all voxels with an uptake ≥ 60% and ≥ 50% of the maximum uptake for [18F]FDG and [18F]FCho, respectively. The selection of the thresholds was done arbitrarily and based on visual inspection of the [18F]FDG PET scan 40–60 min post-injection, the delayed [18F]FDG PET scan 240–270 min post-injection and the [18F]FCho PET scan 10–20 min post-injection, see The metabolic tumor volume (MTV) was calculated based on a semi-automatic thresholding method using the PMOD software  and (MTV x SUVmean) were calculated and included in the analysis. The TBRmax was defined as the ratio of the SUVmax of the tumor MTV to the SUVmean of the reference VOI located in the contralateral occipital normal brain region.Injected activity was corrected for decay and residual activity in the syringe. In addition to the MTV, the SUV18F]FCho PET were data from [Clinical MRI and PET images used to compare clinical and preclinical [ata from . The scaata from .18F]FCho uptake due to blood–brain barrier (BBB) breakdown, we performed autoradiography and analyzed EB extravasation of a F98 GB rat tumor on day 16 after inoculation, as described in [®) dissolved in saline at a concentration of 4 mL per kg of body weight was injected intravenously (t = 0 min). [18F]FCho was injected . At t = 60 min, the rat was euthanized, and dissected rat brains were instantly frozen in isopentane (VWR®) cooled by liquid nitrogen for 2 min followed by 30 min incubation at -20°C. The brains were then cut into 20 μm serial sections using a cryostat , with alternating slides for fluorescent staining and hematoxylin and eosin (H&E) stain. The H&E sections were dried prior to fixation in 4% paraformaldehyde. The slices for autoradiography were placed on a Super Resolution storage phosphor screen (in red lighted room) and incubated for 2.5 h. The film was scanned using the PerkinElmer Cyclone Plus (600 dpi). A picture was taken of the frozen brain tissue (Sony®), and TRITC (tetramethylrhodamine isothiocyanate) fluorescently labeled sections were imaged with a BD pathway 435 automated imaging system (Becton Dickinson) equipped with a 10× objective. A montage of 20×15 images provided a complete overview of the brain section. Using the PMOD software, the HE and AR image were manually co-registered and the tumor volume of interest (VOI) was manually drawn on the HE image and transferred to the AR image. The normal brain VOI consists of a 5 x 5 mm square placed in the contralateral normal brain.To evaluate non-specific FDG, at conventional and delayed time point, and for [18F]FCho PET  for [18F]FDG (early and delayed) and [18F]FCho PET are shown in To eliminate the influence of the differences in tumor volumes between individual animals, also MTV values were normalized to the MTV pre-therapy. Evolution of the normalized MTV and (SUV18F]FDG PET was significantly different between both groups on day 5 (p = 0.016). Using delayed [18F]FDG PET imaging, significant differences in MTV were present between both groups on day 9 (p = 0.032) and 12 (p = 0.032). No significant MTV differences were found between control and therapy group for [18F]FCho PET at any time point.The MTV on conventional [18F]FDG PET,  was significantly different between the control and treated group on day 5 (p = 0.008) post-irradiation using the last time frame of the dynamic PET acquisition. On delayed [18F]FDG PET a significant difference  was found on day 2 (p = 0.032), day 9 (p = 0.032) and day 12 (p = 0.016) post-irradiation. No significant  differences were found between control and treated group for [18F]FCho PET at any time point.For conventional [18F]FDG, delayed [18F]FDG and [18F]FCho PET. Evolution of the normalized MTV and  for [18F]FDG  and [18F]FCho PET in a rat receiving control treatment are shown in In 18F]FCho uptake in the F98 GB tumor and very low uptake in normal brain. The background corrected mean tumor-to-mean normal brain ratio was 3,72 and the max tumor-to-mean normal brain ratio was 6.84  shows clearly necrosis in the tumor core, while this is not present in patient (B). However, both show a heterogeneous [18F]FCho uptake ranging from moderate to moderate intense  at the invasion front of the tumor  and 372.0 MBq (B) injected activity). Low uptake is noted in the normal brain tissue. In the F98 GB rat tumor, no gross central tumor necrosis is seen on contrast-enhanced T1-weighted MRI (C) and increased [18F]FCho uptake is present only in the upper left margin of the tumor . Surrounding extra-cranial organs, such as the salivary glands and the masticatory muscles, show intense [18F]FCho uptake (D). This is clearly visible on the pre-clinical PET, while in humans this uptake is not visible within the axial brain slice. In (B) and (C-D), the leakage pattern of the gadolinium contrast agent on MRI differs strongly from the [18F]FCho uptake pattern. Diffuse leakage of Gd in the entire tumor volume is seen on MRI (C), while a more localized choline uptake is seen in the upper left margin of the tumor just beneath the skull (D).18F]FDG and [18F]FCho PET, a threshold of ≥ 60% and ≥ 50% of the maximum uptake, respectively, was selected based on visual inspection  was superior to MTV alone in detecting early treatment effect  is also referred to as total lesion glycolysis (TLG), which is a well-known volumetric parameter that enables to capture the glycolytic phenotype and overall tumor burden. In this study, using (SUVmean x MTV), [18F]FDG PET acquired 40–60 minutes post-injection, was able to detect treatment response as early as 5 days post-therapy. Similar results were found when evaluating the changes in contrast-enhanced tumor volume on MRI. Importantly, [18F]FDG PET acquired 4 hours post-injection was able to detect the treatment response even earlier, namely at day 2 post-irradiation  and [18F]FET PET have been suggested to be better suited than [18F]FDG for brain tumor imaging and monitoring therapy response in brain tumor patients [18F]FCho PET, first introduced for PET imaging of brain tumors by DeGrado et al. Choline PET, a tumor-to-normal-brain ratio (TBR) ≤ 1.4 might predict a longer overall survival in patients with suspected recurrent glioma after treatment [et al. suggested that there was a good correlation between a change in SUVmax of the tumor volume during RT and response [18F]FCho PET study in childhood astrocytic tumors confirmed the added value of [18F]FCho SUVmax and functional MRI apparent diffusion coefficient values to monitor therapy response FCho PET might be able to detect a treatment-induced diminished cell proliferation rate because this choline PET analogue is a substrate for choline kinase, an enzyme commonly overexpressed in malignant lesions involved in the incorporation of choline into phospholipids, which is an essential component of all cell membranes. In cancer, an increased cellular transport and higher expression of choline kinase leads to an increased uptake of radiolabeled choline [18F]FDG, ameliorating the delineation of tumor boundaries [18F]FCho PET compared to state-of-the art conventional MRI using RANO criteria for early therapy response assessment in GB patients. We found that SUV values were not able to predict response, while (SUVmean x MTV) allowed prediction of therapy response one month after the completion of radiation therapy, however, not earlier than changes of tumor volume derived from contrast-enhanced MRI FCho PET between the control and the treatment group. Based on these results, in rats, [18F]FCho PET was not able to detect early combined radiation and chemotherapy effects after the completion of treatment. We can only speculate about an explanation.In this study, we did not find significant differences at any time point for MTV and  reached a maximum within a 5-min window following the injection [18F]FCho, several clinical reports performed emission scanning for 15 min, beginning 5–10 min after injection of the tracer [18F]FCho uptake by all types of brain lesions was rapid with minimal changes in uptake activity more than 6 min after administration, except for meningiomas [18F]FCho uptake also reached 80% and 90% of the total activity at 3±4 and 7±6 minutes post injection, respectively [18F]FET FET –72. Impo[18F]FET , 73, 74.18F]FDG (and particularly acquired 4 hours post-injection) is preferred over [18F]FCho. Further comparative studies should corroborate these results and should also include (different) amino acid PET tracers.Based on a preclinical rat model for GB and multimodal imaging using MRI and PET with two different tracers, to evaluate early treatment response after combined chemo-radiation therapy, we found that both MRI and PET can be used for this purpose. With regard to the choice of PET biomarker, [S1 Fig(TIF)Click here for additional data file. 5 Jan 2021PONE-D-20-36019Assessment of the effect of therapy in a rat model of glioblastoma using [18F]FDG and [18F]FCho PET compared to contrast-enhanced MRI.PLOS ONEDear Dr. Bolcaen,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.The article describes the results of a pre-clinical study in a rat model of glioblastoma using [18F]FDG and [18F]FCho PET compared to contrast-enhanced MRI"  for the early detection of treatment response. The objective of the study is interesting and may help future potential applications regard PET imaging in the field of primary brain tumors. However, the paper needs a revision as defined in the section of comments below.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Feb 13 2021 11:59PM. 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I agree with the comments of the two reviewers the paper needs a major revision.The results of choline PET and FDG PET have to be discussed carefully because both radiopharmaceutical agents have limited use in this field. For choline PET, despite the biodistribution of the tracer is fast compared to FDG other studies suggest a start acquisition rapidly after injection and a time between 20 and 50 minutes for late imaging in order to have a good balance of T/B ratio. A unique Cho-PET dynamic acquisition 5-20 minutes after the injection may affect negatively the quality of the images. Please discuss in the discussion and eventually in the limitation of the study.I suggest also to report some references missing of recent representative articles on humans: Eg.https://doi.org/10.1007/s40336-020-00398-6- Vetrano, I.G., Laudicella, R. & Alongi, P. Choline PET/CT and intraoperative management of primary brain tumors. New insights for contemporary neurosurgery. Clin Transl Imaging 8, 401–404 (2020). https://doi.org/10.1007/s40336-020-00389-7- Alongi, P., Quartuccio, N., Arnone, A. et al. Brain PET/CT using prostate cancer radiopharmaceutical agents in the evaluation of gliomas. Clin Transl Imaging 8, 433–448 (2020). - Fraioli F, Shankar A, Hargrave D, Hyare H, Gaze MN, Groves AM, Alongi P, Stoneham S, Michopoulou S, Syed R, Bomanji JB. 18F-fluoroethylcholine (18F-Cho) PET/MRI functional parameters in pediatric astrocytic brain tumors. Clin Nucl Med. 2015 Jan;40(1):e40-5. doi: 10.1097/RLU.0000000000000556. PMID: 25188640.Alongi P, Vetrano IG, Fiasconaro E, Alaimo V, Laudicella R, Bellavia M, Rubino F, Bagnato S, Galardi G. Choline-PET/CT in the Differential Diagnosis Between Cystic Glioblastoma and Intraparenchymal Hemorrhage. Curr Radiopharm. 2019;12(1):88-92. doi: 10.2174/1874471011666180817122427. PMID: 30117406.Journal requirements:When submitting your revision, we need you to address these additional requirements.1. 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Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: [Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: No**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorReviewer #1: In this study, the authors investigated the role of FDG and F-Choline PET compared to MRI for the early detection of treatment response in a glioblastoma rat model obtained with F98 cells. Rats were divided into two groups (control and treated with radiation and Temozolomide) and the response was monitored with MRI and PET with FDG and F-Choline performed at different times points.The text is well organized and methods and results are fully described but there are some points that need to be clarified.Major commentsAbstract1. Line 43, in the abstract authors indicate that F-Choline was performed on day 7 post-treatment but in M&M and in the text is indicated day 8 (line 218).Materials and methods1. Line 231, authors can edit the correct injected dose (mean ± SE) of FDG and F-Choline because in line 231 authors indicated 37 MBq and in the figure legends  authors indicated other specific doses.2. Line 274, the dose of FCho can be edited in MBq, please?3. Lines- 289-292, why these lines are under the paragraph “Autoradiography and Evans Blue (EB) staining”, can authors add another title, please?4. Figure 3, is the same animal? Because the images are different. If not, can authors use the same animal, please?Results5. In table 2 there are only the p-values, can also add the value of each parameter (mean ± SE), please?6. Figure 4, why FDG MTV values at d2 are so different between control and treated group whereas the volume measured using MRI is closer? The tumor volume of control animals measured using MRI increased along time whereas MTV (both FDG and Choline) remained stable or slight decrease, what is the hypothesis? Authors should discuss.7. On day 9, only 2 control animals performed FDG PET, how is it possible that both MTV and MTV+SUVmean values are significantly different between control and treated group (line 347 and 353)?8. In figure 5 there is represented only a control rat, authors can add a longitudinal figure with a representative treated rat so it is possible compared images of control and treated rats, please?9. Figure 5, what is the color scale for PET ? Can authors also add min and max values on the scale.10. Did authors evaluate post mortem staining for ki67, GFAP, choline kinase?DiscussionEdit discuss on the basis of results (point 6), please.Reviewer #2: The authors evaluate the role of FDG-PET and Cho-PET, compared to c.e. MRI, for the early detection of treatment response in murine model of GBM; 5 animals randomizedly received RT plus TMZ, while other 5 no. The treatment effect was evaluated with serial MRI and FDG-PET , and also Cho-PET . The metabolic tumor volume (MTV) was semi-automatically calculatedm and the average tracer uptake within the MTV was converted to a SUV. Using SUVmean x MTV, FDG-PET started to detect treatment's effects at 5 day post-treatment, comparable to c.e. MRI. Moreover, delayed FDG-PET (240 min p.i.) earlier detect such effects (from day 2); on the other hand, no significant differences were found at any time point for both the MTV and (SUVmean x MTV) of Cho-PET. Therefore, the authors concluded that MRI and delayed FDG-PET detect early treatment responses in GB murine model of GBM, whereas these results were not obtained with Cho-PETThe topic is undoubtedly intriguing, but i have some issues:-INTRODUCTIONThe ref 1 is related to 2007 WHO classification; from an epidemiological point of view, it should be better to consider the last CBTRUS report .I suggest also to modify ref. 2 and 3, using a more up-to-date literature reference about glioma management  guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol. 2017 Jun;18(6):e315-e329).Moreover, the study by Stupp in 2005 that showed the role of combined RT-CMT was not the ref n° 5 but the one published in NEJM :987-96. doi: 10.1056/NEJMoa0433309).It shuld be better to update the references related to the clinical role of ChoPET in brain tumors , due to the increasing interest about such technique.Why the authors selected Cho-PET, instead of [18F]FAZA PET, for example? I think that clarifying the advantages and disadvantages os this choice could increase the informative role of the present work.How where the simple size selected?statistical analysis were performed to selected a population of 10 animals?Finally, the author disclosure a financial support by Lux Luka Foundation, but they must clearly state, according to Journal guidelines, who exactly received fundings, and the role of the sponsor in the study design and analysis.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  10 Feb 2021A. Additional Editor Comments:The article describes the results of a pre-clinical study in a rat model of glioblastoma using [18F]FDG and [18F]FCho PET compared to contrast-enhanced MRI" for the early detection of treatment response. The objective of the study is interesting and may help future potential applications regard PET imaging in the field of primary brain tumors. I agree with the comments of the two reviewers the paper needs a major revision.________________________________________________________________________Comment 1: The results of choline PET and FDG PET have to be discussed carefully because both radiopharmaceutical agents have limited use in this field. For choline PET, despite the biodistribution of the tracer is fast compared to FDG other studies suggest a start acquisition rapidly after injection and a time between 20 and 50 minutes for late imaging in order to have a good balance of T/B ratio. A unique Cho-PET dynamic acquisition 5-20 minutes after the injection may affect negatively the quality of the images. Please discuss in the discussion and eventually in the limitation of the study.The main reason for starting the acquisition early (5–10 min) after injection of the tracer is indeed because of the rapid and extensive clearance from the blood after intravenous injection. DeGrado et al. documented that the biodistribution of [18F]FCho changes very slowly after 10-min post-injection (DeGrado TR 2001 and 2002). In 2001, in prostate cancer patients, it was documented that the activity in the prostate  reached a maximum within a 5-min window following the injection (DeGrado TR 2001). This was also confirmed by our group investigating the blood kinetics of [18F]FCho in rats, as shown in Figure 8, confirming a fast metabolization with an availability of only 17.5% of intact tracer after 15 min . In multiple clinical reports, emission scanning of the brain was performed for 15 min, beginning 5–10 min after injection of the tracer . To confirm this, in a previous publication of our group, we did investigate the optimal timing for imaging brain tumours and other brain lesions with [18F]FCho PET . On the basis of the TACs, PET imaging with [18F]FCho starting within minutes after the administration of the tracer is preferred and uptake by all types of brain lesions was rapid, and minimal changes in uptake activity occurred more than 6 min after the administration of the tracer, except for meningiomas that showed decreasing activity after an early peak . Hence, if discrimination between meningioma and other brain tumours is of concern, both 'early' and 'late' PET imaging could be helpful. Recently, Grkovski M et al. performed a dynamic 40 min [18F]FCho PET in patients with brain metastasis. The percentage of activity due to [18F]FCho in plasma was more stable than in rats: 67±11%, 65±9%, 65±7% and 64±7% at 1, 5, 10 and 30 min after injection. However, intratumor [18F]FCho uptake reached 80% and 90% of the total activity at 3±4 and 7±6 minutes (median 1 and 6 minutes) post injection, respectively (Grkovski M 2020). This confirms other studies that radiolabeled choline uptake is rapid and sustained  and appears to reach a plateau faster than [18F]FET (Lohmann P 2015).In contrast with [18F]FDG brain imaging, where dual time point imaging has shown clear advantages to increase the T/B ratio , this is assumed to be less advantageous for [18F]FCho PET since the uptake in normal brain is already low . For the use of [18F]FCho PET for the detection of bone metastasis in prostate cancer patients, delayed imaging is recommended. A significant increase in [18F]FCho accumulation in bone metastases was documented using dual-time-point PET imaging .Based on the above-mentioned we selected a dynamic scan of 20 min for imaging the F98 GB tumor in rats assuming based on the literature that the maximal tumor uptake has been reached by then. Parts of this clarification were added to the discussion: line 567-583.● Mertens K, Bolcaen J, Ham H, Deblaere K, Van den Broecke C, Boterberg T, De Vos F, Goethals I. The optimal timing for imaging brain tumours and other brain lesions with 18F-labelled fluoromethylcholine: a dynamic positron emission tomography study. Nucl Med Commun. 2012;33:954-9.● Mertens K, Ham H, Deblaere K, Kalala JP, Van den Broecke C, Slaets D, et al. Distribution patterns of 18F-labelled fluoromethylcholine in normal structures and tumors of the head: a PET/MRI evaluation. Clin Nucl Med. 2012;37:e196-203.● Mertens K, Acou M, Van Hauwe J, De Ruyck I, Van den Broecke C, Kalala JP, et al. Validation of 18F-FDG PET at conventional and delayed intervals for the discrimination of high-grade from low-grade gliomas: a stereotactic PET and MRI study. Clin Nucl Med. 2013;38:495-500. ● Spence AM, Muzi M, Mankoff DA, O’Sullivan SF, Link JM, Lewellen TK, et al. 18F-FDG PET of gliomas at delayed intervals: improved distinction between tumor and normal gray matter. J Nucl Med. 2004;45:1653-9.● DeGrado TR, Coleman RE, Wang S, Baldwin SW, Orr MD, Robertson CN, et al. Synthesis and evaluation of 18F-labeled choline as an oncologic tracer for positron emission tomography: initial findings in prostate cancer. Cancer Res 2001;61:110-7.● DeGrado TR, Reiman RE, Price DT, Wang S, Coleman RE. Pharmacokinetics and radiation dosimetry of 18F-fluorocholine. J Nucl Med. 2002;43:92-6.● Kwee SA, Wei H, Sesterhenn I, Yun D, Coel MN. Localization of primary prostate cancer with dual-phase 18F-fluorocholine PET. J Nucl Med 2006;47:262-9.● Kwee SA, Coel MN, Lim J, Ko JP. Combined use of F-18 fluorocholine positron emission tomography and magnetic resonance spectroscopy for brain tumor evaluation. J Neuroimaging. 2004;14:285-9.● Kwee SA, Ko JP, Jiang CS, Watters MR, Coel MN. Solitary brain lesions enhancing at MR imaging: evaluation with fluorine 18fluorocholine PET. Radiology. 2007;244:557-65.● Husarik DB, Miralbell R, Dubs M, John H, Giger OT, Gelet A, et al. Evaluation of [(18)F]-choline PET/CT for staging and restaging of prostate cancer. Eur J Nucl Med Mol Imaging. 2008;35:253-63.● Grkovski M, Kohutek ZA, Schöder H, Brennan CW, Tabar VS, Gutin PH, et al. 18F-Fluorocholine PET uptake correlates with pathologic evidence of recurrent tumor after stereotactic radiosurgery for brain metastases. Eur J Nucl Med Mol Imaging. 2020;47:1446-57.● Schaefferkoetter JD, Wang Z, Stephenson MC, Roy S, Conti M, Eriksson L, et al. Quantitative 18F-fluorocholine positron emission tomography for prostate cancer: correlation between kinetic parameters and Gleason scoring. EJNMMI Res. 2017;7:25.● Grkovski M, Gharzeddine K, Sawan P, Schöder H, Michaud L, Weber WA, et al. 11C-Choline Pharmacokinetics in Recurrent Prostate Cancer. J Nucl Med. 2018;59:1672-8.● Sutinen E, Nurmi M, Roivainen A, Varpula M, Tolvanen T, Lehikoinen P, et al. Kinetics of [(11)C]choline uptake in prostate cancer: a PET study. Eur J Nucl Med Mol Imaging. 2004;31:317-24.● Lohmann P, Herzog H, Rota Kops E, Stoffels G, Judov N, Filss C, et al. Dual-time-point O-(2-[(18)F]fluoroethyl)-L-tyrosine PET for grading of cerebral gliomas. Eur Radiol. 2015;25:3017-24. ● Bolcaen J, Lybaert K, Moerman L, Descamps B, Deblaere K, Boterberg T, et al. Kinetic Modeling and Graphical Analysis of 18F-Fluoromethylcholine (FCho), 18F-Fluoroethyltyrosine (FET) and 18F-fluorodeoxyglucose (FDG) PET for the Discrimination between High-grade Glioma and Radiation Necrosis in Rats. PLoS One. 2016;11:e0161845.________________________________________________________________________Comment 2: I suggest also to report some references missing of recent representative articles on humans: Eg.https://doi.org/10.1007/s40336-020-00398-6- Vetrano, I.G., Laudicella, R. & Alongi, P. Choline PET/CT and intraoperative management of primary brain tumors. New insights for contemporary neurosurgery. Clin Transl Imaging 8, 401–4 (2020). https://doi.org/10.1007/s40336-020-00389-7- Alongi, P., Quartuccio, N., Arnone, A. et al. Brain PET/CT using prostate cancer radiopharmaceutical agents in the evaluation of gliomas. Clin Transl Imaging 8, 433–48 (2020). - Fraioli F, Shankar A, Hargrave D, Hyare H, Gaze MN, Groves AM, Alongi P, Stoneham S, Michopoulou S, Syed R, Bomanji JB. 18F-fluoroethylcholine (18F-Cho) PET/MRI functional parameters in pediatric astrocytic brain tumors. Clin Nucl Med. 2015 Jan;40(1):e40-5. doi: 10.1097/RLU.0000000000000556. PMID: 25188640.-Alongi P, Vetrano IG, Fiasconaro E, Alaimo V, Laudicella R, Bellavia M, Rubino F, Bagnato S, Galardi G. Choline-PET/CT in the Differential Diagnosis Between Cystic Glioblastoma and Intraparenchymal Hemorrhage. Curr Radiopharm. 2019;12(1):88-92. doi: 10.2174/1874471011666180817122427. PMID: 30117406. We agree that these recent references are important and included these in the manuscript. This section was added to the introduction line 150-157:The metabolic information acquired by [18F]FCho PET has been shown to be able to distinguish high-grade glioma, brain metastases and benign lesions and to identify the most malignant areas for stereotactic sampling . Grech-Sollars et al. concluded that [18F]FCho PET was able to differentiate WHO  grade IV from grade II and III tumours, whereas MR spectroscopy differentiated grade III/IV from grade II tumours [Grech-Sollars et al. 2019]. Recently, the potential use of [18F]FCho PET/CT in the intraoperative management or radio-surgical approaches for glioma has been suggested, including intraoperative guidance in conjunction with MR spectroscopy .The reference to Fraioli et al. was added in the discussion Line 518 and 523-525: Finally, a [18F]FCho PET study in childhood astrocytic tumors confirmed the added value of [18F]FCho SUVmax and functional MRI apparent diffusion coefficient values to monitor therapy response [Fraioli et al. 2015].Also these recent reference were included in the revised manuscript: - Villena Martín M, Pena Pardo FJ, Jiménez Aragón F, Borras Moreno JM, García Vicente AM, et al. Metabolic targeting can improve the efficiency of brain tumor biopsies. Semin Oncol. 2020;47:148-54.- Grech-Sollars M, Ordidge KL, Vaqas B, Davies C, Vaja V, Honeyfield L, et al. Imaging and Tissue Biomarkers of Choline Metabolism in Diffuse Adult Glioma: 18F-Fluoromethylcholine PET/CT, Magnetic Resonance Spectroscopy, and Choline Kinase α. Cancers. 2019;11:1969._______________________________________________________________________B. When submitting your revision, we need you to address these additional requirements.Comment 3: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found athttps://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf andhttps://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdfUpon submission of the revised manuscript, extra care was taken to meet the PLOS ONE style requirements, including those for file naming. Referring to supplemental materials was adapted and author titels were deleted. The reference style of the added references was adapted in the final manuscript. 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This content is published under CC BY 4.0 license. To the best of our knowledge, an additional permission request is not required. Please let us know if we understood wrong. We changed the caption to clarify the reuse of the figure (line 589-590). The original figures were uploaded as ‘other’ in the online submission. ________________________________________________________________________http://journals.plos.org/plosone/s/supporting-information.Comment 5: Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: The guidelines were applied for the supporting information, including an in-text citation: S1 Fig.The name of the supporting information figure was matched with the supporting information captions within the manuscript (line 396). A caption was added at the end of the manuscript, including a title (line 819-820).________________________________________________________________________C. REVIEWER 1In this study, the authors investigated the role of FDG and F-Choline PET compared to MRI for the early detection of treatment response in a glioblastoma rat model obtained with F98 cells. Rats were divided into two groups (control and treated with radiation and Temozolomide) and the response was monitored with MRI and PET with FDG and F-Choline performed at different times points.The text is well organized and methods and results are fully described but there are some points that need to be clarified.Major commentsAbstractComment 1. Line 43, in the abstract authors indicate that F-Choline was performed on day 7 post-treatment but in M&M and in the text is indicated day 8 (line 218).This is indeed an error in the abstract and has been modified (line 52). [18F]FCho PET was performed on day 1-6-8-13 as mentioned in M&M line 247 and in Fig 2, Table 1 and Table 2.________________________________________________________________________Comment 2. Line 231, authors can edit the correct injected dose (mean ± SE) of FDG and F-Choline because in line 231 authors indicated 37 MBq and in the figure legends  authors indicated other specific doses.-4We agree to include the mean injected activity of all [18F]FDG and [18F]FCho scans in the M&M. The mean injected activity for all [18F]FDG scans was 37.89 ± 0.35 MBq and for all [18F]FCho scans it was 39.55 ± 0.37 MBq (mean ± SE). This was added to the manuscript line 263-264. ________________________________________________________________________Comment 3. Line 274, the dose of FCho can be edited in MBq, please?The activity (0.55 mCi) was changed to MBq at line 313.________________________________________________________________________Comment 4. Lines- 289-292, why these lines are under the paragraph “Autoradiography and Evans Blue (EB) staining”, can authors add another title, please?We agree that these lines do not fit under that paragraph. These lines were moved to lines 302-305. ________________________________________________________________________Comment 5. Figure 3, is the same animal? Because the images are different. If not, can authors use the same animal, please?The axial PET images used in Figure 3 were indeed of 3 different rats. We made a new figure including coronal images of an [18F]FDG (early and late) and a [18F]FCho PET of the same rat with a F98 GB tumor. Different thresholds (≥40-50-60-70 %) are contoured. The figure legend was adapted (line 294-300). _______________________________________________________________________ResultsComment 6. In table 2 there are only the p-values, can also add the value of each parameter (mean ± SE), please?The mean ± SE was added to table 2 (page 13-14).________________________________________________________________________Comment 7. Figure 4, why FDG MTV values at d2 are so different between control and treated group whereas the volume measured using MRI is closer? The tumor volume of control animals measured using MRI increased along time whereas MTV (both FDG and Choline) remained stable or slight decrease, what is the hypothesis? Authors should discuss.The following paragraph has been added to the Discussion (line 503-510):In Figure 4 can be observed that tumor volumes measured using contrast-enhanced MRI clearly increase over time in the control group, while metabolic tumor volumes remain more or less stable and also show larger variability. Our hypothesis is that the fast growing tumors, as observed on MRI, are becoming metabolically more heterogeneous tumors. This results in a more heterogeneous tracer uptake within these tumours (e.g. necrotic core vs. viable tumour proliferation and infiltration), which gives rise to higher variations of the measured MTV-values in the control group and that might also explain why the MTV-values are not increasing over time.In addition (not added to the manuscript):For example, the FDG MTV at day 2 is indeed very different between the control and therapy group whereas the difference of the MRI Gd tumor volume is smaller. When taken a closer look to the data, this can be clarified because 2 rats of the DMSO group showed a 12x increase in FDG MTV between pre-therapy and d2, whereas there was only a 2 to 3 fold increase of the MR Gd volume. The 3 other rats of the DMSO group showed a 2 to 3 fold increase of both the MR Gd volume and FDG MTV between pre-therapy and d2. As a result, the SE-values are higher.________________________________________________________________________Comment 8. On day 9, only 2 control animals performed FDG PET, how is it possible that both MTV and MTV+SUVmean values are significantly different between control and treated group (line 347 and 353)?Thank you for this very valuable comment. After re-checking the data and statistical analysis, we observed a mistake in Table 1. On day 9 FDG PET was performed on 4 control animals and not on 2 control animals. We changed this in the table on page 10 and the total scans included was also changed: line 391.________________________________________________________________________Comment 9. In figure 5 there is represented only a control rat, authors can add a longitudinal figure with a representative treated rat so it is possible compared images of control and treated rats, please?Longitudinal FDG and FCho PET/MRI images of treated rats were added to figure 5. The figure legend was adapted. Mean ± SE injected activity was added of all FDG and FCho PET scans included in the new figure (see line 424-430).________________________________________________________________________Comment 10. Figure 5, what is the color scale for PET ? Can authors also add min and max values on the scale.The images in Figure 5 were created using the PMOD software and the color scale of the images is in kBq/cc (kBq/mL) before extracting data. Only after VOI (MTV) delineation, the uptake values in kBq/cc were extracted in excel to calculate SUV and TBR values. The contrast range of the images selected in Figure 5 was (0-850 kBq/cc) for early FDG, (0-220 kBq/cc) for delayed FDG and (0-350 kBq/cc) for FCho. These were selected manually to obtain an optimal image of the brain and GB tumor uptake. Hence we prefer not to add the different ranges to the color scale.________________________________________________________________________10. Did authors evaluate post mortem staining for ki67, GFAP, choline kinase?These stainings were not performed in this study. We did make a lot of efforts to optimize IHC for staining the choline transporter (CTL1) to correlate with the [18F]FCho PET uptake. However, after mulitple unsuccesfull attempts, the antibody we purchased seemed to only bind human brain tissue and not rat brain tissue .________________________________________________________________________DiscussionEdit discussion on the basis of results (point 6), please.The following paragraph has been added to the Discussion (line 503-510).D. REVIEWER 2Reviewer #2: The authors evaluate the role of FDG-PET and Cho-PET, compared to c.e. MRI, for the early detection of treatment response in murine model of GBM; 5 animals randomizedly received RT plus TMZ, while other 5 no. The treatment effect was evaluated with serial MRI and FDG-PET , and also Cho-PET . The metabolic tumor volume (MTV) was semi-automatically calculatedm and the average tracer uptake within the MTV was converted to a SUV. Using SUVmean x MTV, FDG-PET started to detect treatment's effects at 5 day post-treatment, comparable to c.e. MRI. Moreover, delayed FDG-PET (240 min p.i.) earlier detect such effects (from day 2); on the other hand, no significant differences were found at any time point for both the MTV and (SUVmean x MTV) of Cho-PET. Therefore, the authors concluded that MRI and delayed FDG-PET detect early treatment responses in GB murine model of GBM, whereas these results were not obtained with Cho-PETThe topic is undoubtedly intriguing, but i have some issues:-INTRODUCTION1. The ref 1 is related to 2007 WHO classification; from an epidemiological point of view, it should be better to consider the last CBTRUS report .Reference 1 was changed to the recent publication of Ostrom et al. We included numbers from this work in the introduction, see line 72-74:‘In the US, 84,170 new cases of primary brain and other central nervous system tumors are estimated to be diagnosed in 2021. Glioblastoma (GB) has the highest number of cases of all malignant tumors, with 12,970 cases projected in 2021 [1].’________________________________________________________________________2. I suggest also to modify ref. 2 and 3, using a more up-to-date literature reference about glioma management  guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol. 2017 Jun;18(6):e315-e329).We agree that this is an important and more up to date reference, hence we changed previous references 2 and 3 to Weller et al. 2017_______________________________________________________________________3. Moreover, the study by Stupp in 2005 that showed the role of combined RT-CMT was not the ref n° 5 but the one published in NEJM :987-96. doi: 10.1056/NEJMoa0433309).Reference 5 was changed to this reference of Stupp et al. from NEJM.________________________________________________________________________4. It should be better to update the references related to the clinical role of ChoPET in brain tumors , due to the increasing interest about such technique.We added more recent references related to the clinical role of ChoPET in brain tumors to the manuscript. An extra section was added to the introduction, see line 150-157. The reference to Fraioli et al. was added in the discussion Line 518 and 523-525. We included the following references:- Vetrano IG, Laudicella R, Alongi P. Choline PET/CT and intraoperative management of primary brain tumors. New insights for contemporary neurosurgery. Clin Transl Imaging. 2020;8:401-4. - Alongi, P, Quartuccio, N, Arnone, A, Kokomani A, Allocca M, Nappi G, et al. Brain PET/CT using prostate cancer radiopharmaceutical agents in the evaluation of gliomas. Clin Transl Imaging. 2020;8:433-48. - Fraioli F, Shankar A, Hargrave D, Hyare H, Gaze MN, Groves AM, et al. 18F-fluoroethylcholine (18F-Cho) PET/MRI functional parameters in pediatric astrocytic brain tumors. Clin Nucl Med. 2015;40:e40-5.- Alongi P, Vetrano IG, Fiasconaro E, Alaimo V, Laudicella R, Bellavia M, et al. Choline-PET/CT in the Differential Diagnosis Between Cystic Glioblastoma and Intraparenchymal Hemorrhage. Curr Radiopharm. 2019;12:88-92. - Villena Martín M, Pena Pardo FJ, Jiménez Aragón F, Borras Moreno JM, García Vicente AM, et al. Metabolic targeting can improve the efficiency of brain tumor biopsies. Semin Oncol. 2020;47:148-54.- Grech-Sollars M, Ordidge KL, Vaqas B, Davies C, Vaja V, Honeyfield L, et al. Imaging and Tissue Biomarkers of Choline Metabolism in Diffuse Adult Glioma: 18F-Fluoromethylcholine PET/CT, Magnetic Resonance Spectroscopy, and Choline Kinase α. Cancers. 2019;11:1969.______________________________________________________________5. Why the authors selected Cho-PET, instead of [18F]FAZA PET, for example? I think that clarifying the advantages and disadvantages of this choice could increase the informative role of the present work.For many years, the focus of our research group has been the role of various F-18 labeled PET-tracers in neuro-oncology. In comparison with other PET tracers, FDG and non-FDG tracers alike, [18F]FCho has certain advantages of which a very low uptake in normal white and grey matter of the brain is of major interest because it enhances the contrast between tumour and normal brain tissue. Secondly, it is – at least in Europe - widely available and it is still the tracer of choice in the management of castration resistant prostate cancer for those centers that do not have access to PSMA PET.Although [18F]FCho PET has been studied for glioma imaging before by other groups, the number of studies is still limited compared to the number of publications on amino acid and hypoxia PET biomarkers.Since hypoxia is associated with tumor aggressiveness, radiation resistance and poor prognosis, it is possible that changes in [18F]FAZA or [18F]FMISO uptake between pre- and post-treatment can be used to monitor treatment response. However, only few studies have been performed. One downside is that the degree of hypoxia can theoretically fluctuate, influenced by therapy and the presence of acute versus chronic hypoxia which can influence the reproducibility of hypoxia PET . In 2013, [18F]FAZA and [18F]FDG uptake in the F98 GB rat model was investigated by Belloli et al., however, not specifically for therapy response assessment. It is also noteworthy that there is no current consensus on which tracer is the best for hypoxia imaging. [18F]FAZA has advantages compared to [18F]FMISO due to better pharmacokinetic properties but [18F]FMISO can cross the blood–brain barrier because of its lipophilic nature while [18F]FAZA and [18F]DiFA can not . Another downside of [18F]FAZA is that imaging is optimal 2-3 hours post-injection (with [18F]FMISO up to 4h), making it less convenient to work with. However, we studied [18F]FET and [18F]FAZA PET in another in vivo study by our group with a focus on the feasibility of PET-guided irradiation . Because hypoxia is directly related to radiation resistance, [18F]FAZA PET could be used to guide an additional boost on hypoxic tumor regions as a strategy to overcome radioresistance and increase therapy effectiveness. [18F]FET has already been studied in depth for imaging glioma, however, tumor-to-normal brain contrast is less optimal compared to [18F]FCho.The underlined parts have been included in the introduction (line 142-144). - Verhoeven J, Bolcaen J, De Meulenaere V, Kersemans K, Descamps B, Donche S, et al. Technical feasibility of [18F]FET and [18F]FAZA PET guided radiotherapy in a F98 glioblastoma rat model. Radiat Oncol. 2019;14(1):89.- Mapelli P, Zerbetto F, Incerti E, Conte GM, Bettinardi V, Fallanca F, et al. 18F-FAZA PET/CT hypoxia imaging of high-grade glioma before and after radiotherapy. Clin Nucl Med 2017;42:e525-26.- Belloli S, Brioschi A, Politi LS, Ronchetti F, Calderoni S, Raccagni I,et al. Characterization of biological features of a rat F98 GBM model: a PET-MRI study with [18F]FAZA and [18F]FDG. Nucl Med Biol. 2013;40:831-40.- Hirata K, Yamaguchi S, Shiga T, Kuge Y, Tamaki N. The Roles of Hypoxia Imaging Using 18F-Fluoromisonidazole Positron Emission Tomography in Glioma Treatment. J Clin Med. 2019;8:1088. - Mönnich D, Troost EG, Kaanders JH, Oyen WJ, Alber M, Thorwarth D. Modelling and simulation of the influence of acute and chronic hypoxia on [18F]fluoromisonidazole PET imaging. Phys Med Biol. 2012;57:1675-84.________________________________________________________________________6. How where the simple size selected?statistical analysis were performed to selected a population of 10 animals?Using statistical power analysis based on ANOVA  using 2 group, 5 repeated measurements, an alpha-value of 0.05, a power of 0.80 and an effect size of 0.4, a total sample size of 10 animals was calculated.________________________________________________________________________7. Finally, the author disclosure a financial support by Lux Luka Foundation, but they must clearly state, according to Journal guidelines, who exactly received fundings, and the role of the sponsor in the study design and analysis.Lux Luka Foundation did indeed support this study financially. Funds were received by Prof. I Goethals and Prof. T Boterberg. The sponsor did not play a role in study design and analysis. The funding source was not included in the Acknowledgements section in the manuscript, according to the Journal’s guidelines. However, this information was added in the cover letter.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 22 Feb 2021Assessment of the effect of therapy in a rat model of glioblastoma using [18F]FDG and [18F]FCho PET compared to contrast-enhanced MRI.PONE-D-20-36019R1Dear Dr. Bolcaen,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Pierpaolo AlongiAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 23 Feb 2021PONE-D-20-36019R1 18F]FDG and [18F]FCho PET compared to contrast-enhanced MRI. Assessment of the effect of therapy in a rat model of glioblastoma using [Dear Dr. Bolcaen:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Pierpaolo Alongi Academic EditorPLOS ONE


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A step-by-step protocol of how to implement Bayesian multilevel model analysis with social data and how to interpret the result is presented. The article used a dataset regarding religious teachings and behaviors of lying and violence as an example. An analysis is performed using R statistical software and a bayesvl R package, which offers a network-structured model construction and visualization power to diagnose and estimate results.  Specifications tableIn social sciences, the persistence of 'stargazing', p-hacking, and HARKing issues has currently led to a severe reproducibility crisis in which 70% of researchers have failed to reproduce the experiments of other scientists The analysis was done using the bayesvl R package (version 0.8.5) in the R statistical software (version 3.6.2) R> data(Legends345)R> data1 <- Legends345R> head(data1)Hereafter, we use one of our latest research studies as an example for performing Bayesian multilevel analysis with social data •"Lie": whether the main character lies•"Viol": whether the main character employs violence•"VB": whether the main characters' behaviors express the value of Buddhism•"VC": whether the main characters' behaviors reflect the value of Confucianism•"VT": whether the main characters' behaviors express the value of Taoism•"Int1": whether there are interventions from the supernatural world•"Int2": whether there are interventions from the human world•"Out": whether the outcome of a story is favorable for its main charactersEven though there are 25 binary variables, of which only eight variables are employed in this article:First, we establish three different directed acyclic graphs (DAGs), or so-called "relationship trees," from simple to more complex ones, based on the dataset mentioned above.The first and the most straightforward "relationship tree" exemplified examines the determinants of the behaviors of lying and violence on the outcome of the main character see .Fig. 1ThR> library(bayesvl)R> model1 <- bayesvlR> model1 <- bvl_addNodeR> model1 <- bvl_addNodeR> model1 <- bvl_addNodeTo construct the "relationship tree" in Because the statistical distribution of all employed variables is binomial, we set "binom" in the function. Besides binomial distribution, the package also provides various types of statistical distribution for the types of data, namely: normal distribution – "norm", categorical distribution – "cat", Bernoulli distribution – "bern", Student's t-distribution – "student", Poisson distribution – "pois", and so on.R> model1 <- bvl_addArcR> model1 <- bvl_addArcAfter loading all the variables into the "relationship tree", the next step is to grant the regression type to the connection between the independent variables "Lie" and "Viol" and the dependent variable "O" by employing the function bvl_addArc. The model can be set as the fixed effect type by adding a "slope" into the command:R> bvl_stanPriors(model1)a_O ~ normalb_Viol_O ~ normalb_Lie_O ~ normalIn Bayesian inference, the posterior probability is estimated from a prior probability and a "likelihood function" derived from a statistical model for the observed data. Therefore, setting prior distribution is critical before fitting the model. The prior distribution can be determined based on previous empirical findings, researcher's past experience and personal intuition, or expert opinion R> bvl_bnPlot(model1)Since the prior distribution was not set in bvl_addArc, the package automatically set prior distribution of b_Viol_O as default distribution which is normal. Eventually, the function bvl_bnPlot can help produce the graphical network of the constructed model see .R> bvl_bR> summary(model1)Model Info:nodes: 3arcs: 2scores: NAO + b_Lie_O * Lie + b_Viol_O * Violformula: O ~ a_Estimates:model is not estimated.To check the structure and mathematical form of the model, one can use the function summary:R> model2 <- bayesvlR> model2 <- bvl_addNodeR> model2 <- bvl_addNodeR> model2 <- bvl_addNodeR> model2 <- bvl_addNodeR> model2 <- bvl_addNodeR> model2 <- bvl_addNodeR> model2 <- bvl_addNodeR> model2 <- bvl_addNodeThe second "relationship tree" is designed to estimate the impact of violent behavior and its interaction effect with religious values on the outcome of the main character see . SimilarThe variables "B_and_Viol", "C_and_Viol", and "T_and_Viol" are the interaction variables between the act of violence and the value of Buddhism, Confucianism, and Taoism, respectively. The independent interaction variables, represented by the green nodes, can be subsequently created from two normal independent variables, represented by the blue nodes. Unlike the normal variable "Viol" defined as "binom", or binomial, the interaction variables are defined as "trans", or interaction/transformed. It is noteworthy that the "trans" variable does not have a particular distribution but depends on the interaction of two normal variables through applying " * " or " + " operator. To standardize, we call normal independent variables as observation data and interaction variables as transformed data from now on.R> model2 <- bvl_addArcR> model2 <- bvl_addArcThe dash-line arrow demonstrates the relation between the transformed data and the observation data see . The valR> model2 <- bvl_addArcR> model2 <- bvl_addArcR> model2 <- bvl_addArcR> model2 <- bvl_addArcThe model can be set as the fixed effect type by adding "slope" into the command:a_O ~ normalb_Viol_O ~ normalb_B_and_Viol_O ~ normalb_C_and_Viol_O ~ normalb_T_and_Viol_O ~ normalThe prior distributions of model 2 are also set as default:R> bvl_bnPlot(model2)R> summary(model2)Model Info:nodes: 8arcs: 9scores: NAO + b_B_and_Viol_O * VB*Viol + b_C_and_Viol_O * VC*Viol + b_T_and_Viol_O * Viol*VTformula: O ~ a_Estimates:model is not estimated.Eventually, the function bvl_bnPlot and summary can help produce the graphical network see  and the One can create a much more complex model of multilevel regression analysis, while only following a similar procedure with two models mentioned above and employing some additional functions. The primary purpose of the third exemplary "relationship tree" is to explore the impacts of lying and violence behaviors, their interaction with religious values, and intervention from the supernatural or human world on the outcome of the main character see .Fig. 5ThInt1_or_Int2 = (Int1 + Int2 > 0 ? 1: 0)To construct the "relationship tree" illustrated in R> model3 <- bvl_addNode", out_type = "int", lower = 0, test = c) fun = "({0}> 0 ? 1: 0)" is equivalent to the conditional algorithm shown above, while out_type stands for the property of the output, such as "int" (integer) and "real" . The parameter test = c helps to insert the code computing “fixed predicted outcome” when Int1_or_Int2 = 0 and Int1_or_Int2 = 1. The value of transformed data "Int1_or_Int2" is defined based on the values of observational data "Int1" and "Int2" through the mathematical operator " + ":R> model3 <- bvl_addArcR> model3 <- bvl_addArcTherefore, the command to create the node of "Int1_or_Int2" is augmented as follows:R> model3 <- bvl_addArcR> model3 <- bvl_addArcR> model3 <- bvl_addArc", "sigma_ ~ normal"))+ priors = For completing the "relationship tree" construction, the last step is to connect two observational data "Lie" and "Viol" as well as other transformed data to the outcome "O". Like previous commands, the function bvl_addArc is used, but "trans" is replaced by "slope" (fixed effect) or "varint" (varying intercept), to convert the relationships between "O" and other nodes into regression relationships. There are four fundamental types of statistical model integrated in the bayesvl package: fixed-effect model ("slope"), varying-intercept model ("varint"), varying-slope model ("varslope"), and mixed-effect model ("varpars").c", "sigma_ ~ normal") into the function bvl_addArc. Similarly, this method can be applied to change the prior distribution of other relationships by using the prefix a0_, b_, or sigma_, depending on the relationship type. Besides normal distribution, other kinds of distribution can also be implemented for setting up prior distribution by replacing "normal" by the name of the designated distribution . The prior distribution of each path can be checked by typing:R> bvl_stanPriors(model3)b_B_and_Viol_O ~ normalb_C_and_Viol_O ~ normalb_T_and_Viol_O ~ normalb_Viol_O ~ normalb_B_and_Lie_O ~ normalb_C_and_Lie_O ~ normalb_T_and_Lie_O ~ normalb_Lie_O ~ normala0_Int1_or_Int2 ~ normalsigma_Int1_or_Int2 ~ normalu_Int1_or_Int2 ~ normalThe first and second commands are to create the regression relationships of the outcome with observational and transformed data, respectively, employing a fixed-effect model, while the third command is to create the regression relationship between the outcome and transformed data employing a varying-intercept model. In model 3, the prior distribution of all the paths from observational and transformed nodes to the outcome node is set as default, except for the path from "Int1_or_Int2" to "O". The prior distributions of the relationship between "Int1_or_Int2" and "O" is set by adding the code priors = R> bvl_bnPlot(model3)Eventually, the function bvl_bnPlot can help produce the graphical network of the constructed model see .R> bvl_bR> bvl_formulaB_and_Lie ~ VB*LieR> bvl_formulaInt1_or_Int2 ~ (Int1+Int2 > 0 ? 1: 0)One can also check the mathematical construct of each transformed data in the "relationship tree" above by using the function bvl_formula, like the following examples:R> summary(model3)Model Info:nodes: 15arcs: 23scores: NAb_C_and_Viol_O * VC*Viol + b_T_and_Viol_O * VT*Viol + b_Viol_O * Viol + b_B_and_Lie_O * VB*Lie + b_C_and_Lie_O * VC*Lie + b_T_and_Lie_O * VT*Lie + b_Lie_O * Lie + a_Int1_or_Int2[(Int1+Int2 > 0 ? 1: 0)]formula: O ~ b_B_and_Viol_O * VB*Viol + To check the structure and mathematical form of the model, one can use the function summary:Estimates: model is not estimated!R> model_string <- bvl_model2Stan(model3)R> cat(model_string)Before fitting the model using MCMC simulation, one needs to generate the Stan code in R. Because the bayesvl package provides an automatic generation of Stan code, one can use the following commands:R> model3 <- bvl_modelFitR> summary(model3)Model Info:nodes: 15arcs: 23scores: NAb_C_and_Viol_O * VC*Viol + b_T_and_Viol_O *VT*Violformula: O ~ b_B_and_Viol_O * VB*Viol + b_Viol_O * Viol + b_B_and_Lie_O * VB*Lie + b_C_and_Lie_O * VC*Lie + b_T_and_Lie_O * VT*Lie+ b_Lie_O * Lie + a_Int1_or_Int2[(Int1+Int2 > 0 ? 1: 0)]+ Estimates:Inference for Stan model: d4bbc50738c6da1b2c8e7cfedb604d80.4 chains, each with iter=5000; warmup=2000; thin=1;post-warmup draws per chain=3000, total post-warmup draws=12,000.The model created from the "relationship tree" can be fitted with MCMC simulation using the function bvl_modelFit. The structure of the function bvl_modelFit is partly dissimilar with other currently existent Bayesian analysis packages because it does not require users to construct conventional mathematical relationships among variables as well as set up the prior distribution for each relationship. One only need to input the name of constructed "relationship tree", the dataset, and mandatory set-up for MCMC simulation. As the bayesvl package was coded utilizing the No-U-Turn Sampler (NUTS) sampler  The model is fitted using four chains, each with 5000 iterations of which the first 2000 are for warmup, resulting in a total of 12,000 post-warmup posterior samples. In general, the model's simulated results show a good convergence based on two standard diagnostics of MCMC simulation, n_eff, and Rhat. The n_eff represents the effective sample size, which is the number of iterations needed for effective independent samples W is the within-sequence variance.Where One can aesthetically visualize the convergence diagnostics, posterior distribution, and estimated results. The function bvl_plotTrace can generate the trace plots of the constructed model.R> bvl_plotTrace(model3)R> bvl_plotAcfstx is the sampled value of x at iteration t, T represents the total number of sampled values, and The mathematical formula for the autocorrelation parameter for lag = L is displayed below:R> bvl_plotGelmans (model3)Measuring how much variance there is between chains relative to how much variance there is within chains is another idea to check the convergence. If the average difference between chains is similar to average difference within chains (when Rhat = 1.0), the chains are well convergent. Nevertheless, the relative value might increase (when Rhat > 1.0) and indicates the less convergent tendency between chains, if there appears at least on orphaned or stuck chain R> bvl_plotParams Besides the mean and standard deviation of the posterior distribution summarized in the model fit above, one can visually present the estimated posterior distribution of every variable coefficient through histograms. The visualization can be made using the function bvl_plotParams. We visualize the estimated posterior distribution of every variable in the constructed model in four rows and three columns with the Highest Posterior Distribution Intervals (HPDI) at 89% see . The defR> bvl_plotIntervals)R> bvl_plotDensity)There are also other built-in alternatives to visually present the estimated results after simulation, such as bvl_plotIntervals and bvl_plotDensity. The bvl_plotIntervals function helps visualize the coefficients and their interval, while the bvl_plotDensity function helps plot the posterior probability density of coefficients. The results can be plotted "all-in-one" or selectively by both functions. The following commands are to visualize the interval see  and the R> bvl_plotDensity2dThe comparison between two different coefficients' distribution of posteriors can be plotted by the following code see :R> bvl_pbayesvl R package for social data analysis also provides the opportunity to construct a "relationship tree" among variables intuitively and graphically visualize simulated posterior, especially in the age of Big Data Recently, the reproducibility crisis and the problems of 'stargazing', p-hacking, or HARKing in statistical analysis have required the scientific community to be more rigorous in conducting research and find solutions for the persistent statistical issues. Thus, the method paper proposes Bayesian analysis as a substitution for the conventional frequentist approach. Bayesian statistics have the advantages of treating all unknown quantities probabilistically and incorporating prior knowledge or belief of scientists into the model as an alternative approach for frequentist analysis in social sciences. The usage of the The authors declare that they have no known competing for financial interests or personal relationships that could have appeared to influence the work reported in this paper.


A second monoclonal antibody—ATA 842—by Atara Biotherapeutics increased muscle mass and strength, as well as insulin sensitivity in old mice over a period of 4 weekshttps://www.pfizer.com/news/press‐release/press‐release‐detail/pfizer_terminates_domagrozumab_pf_06252616_clinical_studies_for_the_treatment_of_duchenne_muscular_dystrophy). However, in the mouse DMD mdx model, the mouse analogue of domagrozumab—mRK35—significantly increased body weight, muscle weights, grip strength, and ex vivo force production in the extensor digitorum longus (EDL) muscle.Myostatin‐targeting antibodies and soluble ActRIIB to block atrophic signalling in skeletal muscle have been studied extensively in animal models and human trials with varying success. In a progeric mouse model, the soluble ActRIIB‐Fc  improved muscle mass and delayed morbidity.Journal of Cachexia, Sarcopenia and Muscle, Rooks et al.In this issue of the In general, there seems to be no direct relationship between muscle mass and strength,The authors have no conflict of interest regarding the subject of this editorial.


In this article, a Siamese network is applied to the drill wear classification problem. For furniture companies, one of the main problems that occurs during the production process is finding the exact moment when the drill should be replaced. When the drill is not sharp enough, it can result in a poor quality product and therefore generate some financial loss for the company. In various approaches to this problem, usually, three classes are considered: green for a drill that is sharp, red for the opposite, and yellow for a tool that is suspected of being worn out, requiring additional evaluation by a human expert. In the above problem, it is especially important that the green and the red classes not be mistaken, since such errors have the highest probability of generating financial loss for the manufacturer. Most of the solutions analysing this problem are too complex, requiring specialized equipment, high financial investment, or both, without guaranteeing that the obtained results will be satisfactory. In the approach presented in this paper, images of drilled holes are used as the training data for the Siamese network. The presented solution is much simpler in terms of the data collection methodology, does not require a large financial investment for the initial equipment, and can accurately qualify drill wear based on the chosen input. It also takes into consideration additional manufacturer requirements, like no green-red misclassifications, that are usually omitted in existing solutions. Drill wear state recognition belongs to the larger group of problems called tool condition monitoring, which deals with the evaluation of different machine parts’ condition, as well as determining how long they can be used in the production process. Depending on the properties of each tool, as well as the requirements of the final product, different signals can be recorded and later tested using various methods, to obtain the final evaluation. Quite a few procedures in this direction also deal with the main topic of this paper, which is drill wear state recognition. From the manufacturer’s point of view, when the drill starts to become dull, it should be replaced as quickly as possible. Extending the use time of such a tool can result in poor product quality and therefore generate financial loss for the company. Manual evaluation of the drill state is possible and was initially done during the production process, but this is very time consuming, resulting in the prolongation of the entire procedure. A faster and more automated approach was needed, which resulted in extensive research on this subject. For example, one of the existing solutions focuses on measuring tool wear using two approaches: conventional methods and estimation with a customized software combining artificial neural networks and flank wear image recognition . In thisExisting solutions vary greatly in their approach, especially the data collection methodology. As is often the case, especially when it comes to the usage of specialized equipment, the most visible advancements have been made in medicine. For example, in , the autThe solution presented in this paper takes into account different approaches to deep learning in general  single-lens reflex digital camera with a 35.9 × 24.0 mm CMOS image sensor. The entire process was performed in cooperation with the Institute of Wood Sciences and Furniture at Warsaw University of Life Sciences, Poland. For test purposes, a standard CNC vertical machine centre  was used. Drilling was performed on a standard, melamine-faced chipboard , which is typically used in the furniture industry. The dimensions of the test piece were 300 × 35 × 18 mm. A regular, Faba WP-01 double-blade drill for through drilling  equipped with a tungsten carbide tip was used. The drill’s overall length was 70 mm, with a shank length equal to 25 mm, a flute length of 40 mm, a shank diameter of 10 mm, and a 12 mm drill diameter. The clearance angle on the drill face was −15.45 degrees. The rake angle was equal to 0 degrees, and the helix angle for the tool used was 15 degrees. The images of the equipment used are presented in The data set used in the current experiments was similar to that in the previous works ,15,16,17Usually, three classes are used in drill wear state recognition: red, green, and yellow. In this case, the obtained samples were divided and labelled manually, using the drill wear rate. For the manual evaluation of the drill state, external corner wear (W(mm)) was adopted as the main condition indicator and was periodically monitored using a standard workshop microscope . Based on the obtained values, three classes for drill wear were selected: green for W < 0.2 mm, yellow for W in the range between 0.2 and 0.35 mm, and red for W > 0.35 mm. Those classes were also used for the drill wear definition in the current, automated approach. In the presented case, the yellow class was used mainly as a buffer for the manufacturer. In the case of the furniture industry, depending on the type of elements produced, different hole qualities can be acceptable. In this case, depending on the manufacturer’s preferences, the yellow class can later be assigned either to the green or red classes in the final production, hence expanding the overall method’s customizability. Example images representing different drill wear classes are presented in The original data set contained significantly more examples for the green class than the remaining two (yellow and red). Since CNN was used as a base network for the presented solution, it was not desirable for the data set to be imbalanced in such a way . To correct this, data augmentation methodologies were used, to ensure an even representation of each class . With the data balanced, the training process should not favour any of the classes. Initial operations performed on the data samples also included resizing each of the images on the fly to a size equal to 64 × 64 × 3 pixels. The training input was also normalized by dividing each value by 255, to ensure that they were in the  range. Since 5-fold cross-validation was used, the input data were split between the 5 folds, and each of them was additionally divided into two subsets, the first for training and the second one for validation. The structure of each fold is shown in Siamese networks are novel algorithms used in image recognition. The first approaches with this type of procedure focused specifically on face recognition. One of the first applications was a verification system for identifying workers in a building. When it comes to this problem in general, there are two main areas to consider: verification of whether a person in the current image is one stored in a database under a specified ID and recognizing if the person from the input image is one of those stored in the original database. Especially in systems with large amounts of users , accuracy is a very important factor. While having a 99% recognition rate might be acceptable for other applications, it is not the case here. Even if such a system has a 1% error rate, with 100 people in the database, the possibility of not recognizing the current person correctly is still quite high. Additionally, for most cases with face recognition, the algorithm needs to be able to recognize the person while using a single image (the one-shot learning problem). Using CNN for such an approach is not good enough, since firstly, the amount of training data is minimal, and secondly, each time a new person is added to the system, the network would require retraining. This is where the approach used in Siamese networks has the advantage.--f. In that case, if the difference between two images is greater than the set threshold, the person would be classified as different, and as the same in case of this value being below that threshold. In general, it can be described as:To be able to calculate this distance between the input images, both of them are encoded using identical CNN networks. They are then represented as feature vectors instead of the usual classification. In general, what is done at this point is that instead of using the final classifier from the CNN (or other network), the entire process stops at one of the embedding layers . By using this approach, two different, comparable encodings of the images can be obtained, and the distance between them can be measured . The idea of first launching two identical CNN networks to produce feature vectors and secondly using those vectors to calculate the difference measure between images is the basis of the Siamese network architecture .The Siamese network is a good example of a solution that can distinguish between instances of different classes and specifically determine if the image that is provided as the input is the same as the one representing the original class. In terms of face recognition, it would determine if the same person is in the picture. In the case of the solution presented here, with some additional modifications, it should point to which drill wear class the provided example belongs.-α is a margin that is enforced between positive and negative pairs-τ is the set of all possible triplets in the image set and has cardinality N.To train networks used for such recognition, a few steps are required, as well as a definition of the function used to distinguish between positive and negative examples of each class. The presented solution needs to be able to do two things: recognize the same class in two different images and notice that the presented class is different than the one to which it is compared. To achieve that, the following images are required: the first one, containing the element representing a single class , the positive image example, containing the same class , and the negative image, with a different class . When the distance between those images is calculated (from the obtained image representation), the ideal outcome would produce results for which the distance between the images containing the element with the same class is lower than in the case of the images containing elements representing different classes. What is more important for this approach to work accurately is that this distance needs to be significant, reaching at least some predefined margin . The above relation can be described using the following equations:At this point, the loss that needs to be minimized  has theAlgorithm 1: Siamese network training.Step 1: Generating the training setfor each input sample do Using knowledge about the data set to find a set of samples such that each sample  similar to  Pair sample  are similar, end forCombine all the pairs to form the labelled training set.Step 2: Trainingwhile Convergence not reached dofor each pair do   if   if end for end whileThe margin parameter (α) is a hyperparameter that needs to be manually adjusted to each classification problem. In the case of the topic described in this paper, one type of positive pair (with label y = 1) and two types of negative pairs were used in that process . Since Siamese networks were originally for the face recognition problem, using a similar approach for the drill wear evaluation considered in this work, some adjustments were required. During the training process, first, the set of examples was created, where each example would contain two samples: anchor  and either a positive (P) or a negative (N) example. In this case, instead of a single image that can either be the same or different , a total of three classes were considered. First would be the positive example, with the same class as the anchor. In this case, two negative examples can be generated, containing either of the remaining classes.The approach first divides the entire data set into positive and negative pairs. In the case of negative examples, to increase the diversity of the training set, the class is randomly chosen from the two that are different than the one to which the anchor belongs. Each of the initial images will generate two pairs used for training: one positive, where the anchor is paired with the image of the same class, and one negative, in which the anchor will be paired with an example from a different class. To calculate the distance between the images in consecutive samples, the contrastive loss function is used (see ).Algorithm 2: Network training algorithm used for the drill wear recognition problem.Create positive and negative pairs:for each of initial D = 3 classes dofor all images in each class do   A = current image with index = i  P = next image from the same class with index = i + 1  PairPositive =  with label = 1  Randomly choose one of the remaining classes (different than the current one)  Choose negative image N with index = i  PairNegative =  with label = 0end for end forCalculate the contrastive loss function:for each created pair do Contrastive loss = end forReturn classificationAs a base network for the learning process, CNN is used, with three convolutional layers. The detailed structure of the model prepared for the problem chosen as a main topic of this paper is outlined in Listing 1Model: “CNN”_________________________________________________________________Layer (type) Output Shape Param #=================================================================conv2d_1 (Conv2D)  1792_________________________________________________________________activation_1 (Activation)  0_________________________________________________________________max_pooling2d_1  0_________________________________________________________________dropout_1 (Dropout)  0_________________________________________________________________conv2d_2 (Conv2D)  36,928_________________________________________________________________activation_2 (Activation)  0_________________________________________________________________max_pooling2d_2  0_________________________________________________________________dropout_2 (Dropout)  0_________________________________________________________________conv2d_3 (Conv2D)  18,464_________________________________________________________________activation_3 (Activation)  0_________________________________________________________________max_pooling2d_3  0_________________________________________________________________dropout_3 (Dropout)  0_________________________________________________________________flatten_1 (Flatten)  0_________________________________________________________________dense_1 (Dense)  147,584_________________________________________________________________dropout_4 (Dropout)  0_________________________________________________________________dense_2 (Dense)  6450=================================================================Total params: 211,218Trainable params: 211,218Non trainable params: 0_________________________________________________________________During previous experiments ,15,16,17In the current approach, the Siamese network, adjusted to the presented classification problem, was used. To evaluate the obtained results, additional algorithms were implemented, choosing procedures that were successful in previous experiments, but similarly adjusting them to include the new requirement of minimizing the number of misclassifications between the red and green classes.Algorithm 3: Accuracy function used for algorithm evaluation. Require:   Compute classification accuracy with a fixed threshold on image distances  Return K.mean))  model.compileDuring the experiments, five-fold cross-validation was used , an early stopping mechanism was used. The patience parameter was used, set at five epochs, meaning that if during that time, there was no improvement to the solution accuracy, the training process was stopped, and the best obtained model was saved. Since the data used during the experiments were stored in a time series manner , it was additionally incorporated in the overall approach, to try and improve the algorithm’s accuracy. Instead of a single image, sets of consecutive images of different lengths were used for the training process. With such an approach, the algorithm should be able to learn how hole edges change, while the drill is steadily dulling. The window parameter was incorporated into the solution, testing sequences of 5, 10, 15, and 20 images, with no window approach used as the baseline . The first solution, which did not use any window, obtained an overall accuracy of 67% , as well as the classification methodology . Since in previous experiments, some of the algorithms were tested and performed well for the overall accuracy parameter, the same algorithms were used for the current comparison. The final set contained the VGG19 pretrained network, 2 ensemble algorithms, the first using 5 and the second using 10 random VGG16 networks, the CNN model trained from scratch, and 5 random initializations of this model. All of those algorithms were trained using the window methodology, starting with no window and finishing at a window size of 20. The accuracy results obtained are presented in Next, the Siamese network approach using different windows was tested, and it achieved the best accuracy results for a window size of 20 (82%). Although for smaller windows, it showed poorer results than some algorithms, it quickly outperformed them for larger windows. While the no window approach produced to many critical errors . Additionally, it is able to accurately distinguish between the red and green classes, with a total number of 37 misclassifications between them (22 red-green and 15 green-red errors). To the best of authors’ knowledge, this is the first application of this methodology to the wood industry. The presented approach is highly adjustable, since in the case of changes in the samples , transfer learning can be used to retrain the previous model for a new application, without the need to start from scratch.To summarize, the presented solution achieved an overall accuracy and misclassification rate that fit into the initial acceptable ranges. With the simplified data collection methodology and low initial costs, it is readily applicable to the actual work environment, with very positive, initial feedback from the manufacturer. Furthermore, the Siamese network approach seems very promising, and while further research is still required, it is believed that additional improvement of both the accuracy and critical error rate is still possible.