Dear Dr. Lee,

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.

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We look forward to receiving your revised manuscript.

Kind regards,

Eduardo Andrés-León

Academic Editor

PLOS ONE

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Additional Editor Comments:

Both reviewers have evaluated the article and believe it could be published in our journal. However, they have some concerns and questions about specific parts of the article. Therefore, I ask you to carefully read the reviewers’ comments and respond to each of their concerns, as well as revise the sections they consider necessary

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Reviewers' comments:

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This is an important and relatively well performed study. However, there is no new innovation in this study and no outstanding technical or medical contribution. There have been many studies using TCGA dataset for MSI prediction on histologic images using CNN models. The models authors used were relatively outdated and do not contain any novelty. There is no external validation on any other public dataset such as CPTAC or PAIP. Authors would want to consider applying their models to other public datasets or other dataset from SNU. Multiple instance learning or transformer-based learning can be considered for novel technical approach.

Reviewer #2: Multi-cancer analysis of histopathologic MSI screening based on digital histology image

This paper focuses on developing and evaluating deep learning models to detect microsatellite instability (MSI) using whole-slide images (WSI) from the TCGA dataset. The study targets three cancer types: colorectal cancer (CRC), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC), utilizing convolutional neural networks (CNNs) like EfficientNet, ResNet18, and VGG19 to differentiate between high microsatellite instability (MSI-H) and microsatellite stable (MSS) tumor tiles. The results show that models perform best when tested on tissue types that match their training data, while performance drops when models are tested on different tissue types. The EfficientNet models outperformed the others, and the study found that while multi-tissue trained models sometimes improved performance, they did not always outperform single-tissue models. The paper highlights the challenge of balancing model generality and specificity in MSI detection. Future work aims to incorporate more advanced deep learning models and validate them with external datasets. There are two questions for the author and after doing minor changes, the paper can be accepted by Plos One.

1. Expand the Discussion on CNN Architectures: The authors should provide a more in-depth discussion on the impact of different CNN architectures on model accuracy. Specifically, it would be beneficial to explain how the structural differences between EfficientNet, ResNet18, and VGG19 affect the model's ability to detect MSI. Including insights on how these architectures handle variations in histopathological features and why one may outperform the others in certain tissue types would enhance the technical understanding.

2. Add Experiments to Investigate Generalization Issues: The authors should conduct additional experiments to explore the underlying reasons for the model's limited generalization across tissue types. Investigating the role of tissue-specific features, differences in tumor microenvironments, or variations in data distribution could provide valuable insights. These experiments would help identify the factors that hinder the model's ability to generalize and offer potential solutions to improve its performance in cross-tissue predictions.

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

**********

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Dear Dr. Lee,

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.

Please submit your revised manuscript by Mar 09 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at 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.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Eduardo Andrés-León

Academic Editor

PLOS ONE

Additional Editor Comments :

A reviewer has requested a major revision of the manuscript. Please address all their comments thoroughly and respond to each point individually.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

**********

Reviewer #2: This research proposes a deep learning model for detecting microsatellite instability in cancer diagnoses using whole-slide images from different types of cancers, specifically colorectal, stomach, uterine corpus, and endometrial adenocarcinomas. Differentiating high MSI (MSI-H) cases from microsatellite stable (MSS) cases was the primary objective. Public dataset images were used in this study, which trained models specifically for each cancer type and evaluated them on different and corresponding tissue types, as well as created a multi-tissue model. Major findings were high accuracy in the models specific to the tissues, with the highest for colorectal cancer and slightly lower for stomach and uterine/endometrial cancer. Multi-tissue models performed differently, though they showed promise in terms of generalizability across the different cancers. Despite the potential of MSI as a therapeutic target, traditional diagnostic methods like PCR and immunohistochemistry are costly and time-consuming. The study suggests that deep learning, particularly through analysis of WSI, could offer a quicker, cost-effective alternative for MSI screening, potentially enhancing patient prognosis by facilitating earlier and more accurate diagnoses.

I have several questions for this article:

1. Which of these features does the model rank highest when distinguishing MSI-H from MSS? Is it possible to use interpretability tools such as LIME or SHAP to visualize and understand these features? I suggest the author writes more to extend the Section “Geographic visualization and comparative analysis of MSIprediction scores”.

2. I suggest that the author could Implement robust cross-validation techniques to ensure the models are not overfitting, such as k-fold cross-validation or stratified splits based on cancer types.

3. How do the diagnostic accuracies of deep learning models compare with those of traditional methods—such as PCR and immunohistochemistry—in terms of sensitivity, specificity, and overall diagnostic yield?

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

Dear Dr. Lee,

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.

Please submit your revised manuscript by Oct 05 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at 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.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

PLOS ONE

Journal Requirements:

1. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. 

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

Reviewer #3: Yes

**********

Reviewer #2: This is a well-structured and methodologically sound study addressing a clinically relevant problem: using deep learning to predict microsatellite instability (MSI) from standard histology slides across multiple cancer types. The paper is clearly written, the experiments are comprehensive, and the discussion is thoughtful and balanced. The authors systematically compare models trained on single cancer types, evaluate their cross-cancer generalizability, and test a combined multi-cancer model. The inclusion of an external validation cohort (CPTAC) significantly strengthens the findings. The conclusion that multi-tissue models can improve performance for some cancers (UCEC) while not for others (CRC, STAD) is a nuanced and important contribution to the field.

The manuscript is of high quality and suitable for publication, pending minor revisions to enhance clarity and address a few key points.

Throughout: The term "hypterparameters" is used several times (e.g., line 151, 154, 184); it should be "hyperparameters".

Line 90: "staomach" should be "stomach". (Also seen in Figure 1).

Line 101: "publicy" should be "publicly".

Line 149: "emplolyed" should be "employed".

Line 174: "arcituecture" should be "architecture".

Line 189: "cutomized models" should be "customized models".

Line 212: "demonstarate" should be "demonstrate". "calssifier" should be "classifier". "chracteriestic" should be "characteristic".

Line 221: "a overall accuracy" should be "an overall accuracy".

Line 237: "classifcation model" should be "classification model".

Line 269 (Table 2 Title): "performnace" should be "performance".

Line 351: "We thougth that is may have" could be rephrased for clarity, e.g., "We thought that this might have..."

Reviewer #3: Thanks for making these revisions. I am satisfied with the current version. The authors may consider including relevant citations on image-guided cancer research to strengthen the study's background, such as:"

*DOI: 10.1016/j.cpsurg.2025.101819*

*DOI: 10.1016/j.cpsurg.2025.101833*

*DOI: 10.1016/j.cpsurg.2025.101817*

*DOI: 10.1016/j.cpsurg.2024.101640*

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: No

Reviewer #3: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org

Multi-cancer analysis of histopathologic MSI screening based on digital histology image

PONE-D-24-10966R3

Dear Dr. Lee,

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.

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Kind regards,

Hao Zhang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: