PONE-D-23-30061Ensemble Deep Neural Networks (EDCNN) integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.PLOS ONE

Dear Dr. Kurt,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. 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: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

The 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.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: No

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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: Yes

Reviewer #2: Yes

Reviewer #3: No

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you very much for sharing this interesting work that could add to the body of existing works on the study of multimodal data for prediction of patient diagnosis, and outcomes. The authors applied AI methods to investigate the use of three types of genomic data, and histopathology images for predicting cancer stage, and survival risk in colon cancer patients.

Introduction:

“According to World Health Organization 2020 report, cancer is the leading cause of death accounting for over ten million deaths”. Please provide a citation for this statement.

“The complex features comprising the tumor size, the extent at which the cancer has spread to nearby lymph node, and whether the cancer has metastasized, which is the extent the cancer has spread to other parts of the body from the primary tumor still require the development of an efficient and effective model for accurate and improved classification and effective treatment management.” Please provide more clarity to this statement to make it obvious to a wider audience that the features mentioned are components of TNM staging. Since staging is the focus of this work.

Genomic Datasets preprocessing:

“Then an imputation function in python is used to fill out the missing values.” Please clarify if the missing values are filled with zero, or otherwise.

H&E image dataset preprocessing

"we build a sub python function called MX to filter out patches with less than 30% cellular tumor content." Please, how does this function work to select the patches with >30% cellular content.

Ensemble Deep Convolution Neural Network (EDCNN) Implementation:

The steps described in this section seem more like features integration than ensemble of models output. Also, this applies to the title of the manuscript.

Classification model:

“We test the validity of our hypothesis after reducing the dimension and consequently learn a higher-dimensional and compressed representation from the genomics and histopathological images.” Please provide more clarity about the higher-dimensional representation referred to here.

The described CNN architecture in this section seem more like an ANN.

Survival risk stratification with extracted features:

“As shown in Fig 3, the wide gap between the two functions is an indicator that we can confidently argue that 68% of the extracted features conveniently group the samples into low or high survival class.” Fig 3 only shows the result of 10 of 1836 features.

Is it possible to find out how many genomic, or image features are part of the 1836 for stratifying patients into risk groups?

Table 5:

Please explain more on the confusion matrix analysis. The number of samples in the table are not the same as those in tables 1, and 2.

Reviewer #2: This article has established an integrated deep neural network for the staging classification of colon cancer,stratifying samples into low or high-risk survival groups.There is unexplained diverse variation within the predefined stages of colon cancer. This variation can be observed when utilizing only features from either genomics or histopathological whole slide images as prognostic factors. The unraveling of this variation is expected to lead to enhanced staging and improved treatment outcomes.

Here are my suggestions:

1.This article has a certain degree of novelty.The novelty of colon cancer group can be made by the data of mRNA, miRNA and DNA methylation make,but if the practical application of this group is further elaborated, this paper will be more perfect.

2.If there is more clear introduction on how the randomness of the data is assured,the article will be better.

3.The model established in this article was applied data from open databases without external data validation. It is desirable that more data are used to verify the accuracy of the model in further research,the application scope of this model will be wider.

Reviewer #3: The authors have presented an important study integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer. The manuscript is well written. I have the following comments:

1. The methodology section needs to be put in small headings to allow a proper understanding of the methodology. This will also allow for the reproducibility of your approach.

2. The authors mentioned that they have used 5 different sets of data from the Cancer Genome Atlas. These sets should be mentioned. Was the data from the same source? I mean if the data is from XXX Centre, does it contain all the datatypes (Clinical, DNA methylation, miRNA, mRNA, Hematoxylin, and Eosin (H&E) stained histopathological images). Hence, the specific source from TCGA should be mentioned since it is a database with numerous data.

3. The ethical permission statement should be mentioned.

4. The genomics resulted in 448 samples of clinical records with 80 features. What was the source of this data?

5. Also, 328 samples of mRNA expression with 20,502 features, 261 samples of miRNA expression with 1,870 features, and 353 samples of DNA methylation with 20,759 features. What was the specific source?

6. Regarding this statement, “We proceeded to get the Hematoxylin and Eosin (H&E) stained histopathological images for the 255 samples that have data in the genomics but got only 177 equivalent samples as in the genomics with images datasets”. Does it mean that all 177 samples were considered for clinical, DNA methylation, miRNA, mRNA, Hematoxylin, and Eosin (H&E) stained histopathological images?

7. Does it mean that the deep learning model development was based on data from Table 2?

8. This is unclear: (i) First, a biological feature (in any of methylation or mRNA or miRNA data) is removed, if more than 20% of the patients have a 0 value for it.

9. “Then an imputation function in python is used to fill out the missing values.” What imputation function was used? How many of the 177 were based on imputed data? How effective is the imputation approach. Perhaps, it may be better to remove the rows that contain missing values.

10. We input 36,926 merged features from miRNA, mRNA and DNA Methylation into autoencoder (AE) neural network design specifically to encode its input. How does the author arrive at 36, 926 features?

11. Were the 652 extracted features combined with 2,048 salient features from deep learning extraction?

12. The manuscript can also benefit from professional English Language proofreading.

13. The programming code for these analyses should be inserted as an Appendix/Supplementary for reproducibility.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachments

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Submitted filename: Review.docx

PONE-D-23-30061R1Deep Neural Network (DNN) integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancerPLOS ONE

Dear Dr. Kurt,

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 Jun 17 2024 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.

Please include the following items when submitting your revised manuscript:

• 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,

John Adeoye

Academic Editor

PLOS ONE

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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.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

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2. 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: Yes

Reviewer #3: Yes

**********

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

Reviewer #1: Yes

Reviewer #3: N/A

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

The 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.

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. 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: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you very much for your response to the previous comments, and for the changes made.

As regards the the confusion matrix on table 5, the table shows a total of 112,161 samples that were predicted by the models. This applies to each of genomic, images, and the integrated data. However, only image samples contain a total of 112,161 tiles. On the patient level, the genomic data contain 177 samples, and same for image data too. If this is correct, I would expect at least the the number of genomic, and integrated samples to be 177. Please, can you clarify more on these? Were the image tiles eventually aggregated to patient level? what methods was used for the aggregation?

Reviewer #3: The authors have addressed my comments. I have no further comments. I wish the author the best in their research.

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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 #1: No

Reviewer #3: No

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[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.

Deep Neural Networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer

PONE-D-23-30061R2

Dear Dr. Kurt,

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.

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Kind regards,

John Adeoye

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: (No Response)

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2. 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: Yes

**********

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

Reviewer #1: Yes

**********

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

The 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.

Reviewer #1: Yes

**********

5. 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: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you very much for the clarification about many to one method of concatenation used. About table 5, to make it clearer, it may be better if the number of genomic sample is presented as 35, and not 22432 as it is with image, and integrated data. Currently, it seems like the same genomic samples were repeated many times in both the training and test data.

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Reviewer #1: No

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