Causal debiasing for unknown bias in histopathology—A colon cancer use case

Ramón L. Correa-Medero; Rish Pai; Kingsley Ebare; Daniel D. Buchanan; Mark A. Jenkins; Amanda I. Phipps; Polly A. Newcomb; Steven Gallinger; Robert Grant; Loic Le marchand; Imon Banerjee

doi:10.1371/journal.pone.0303415

Peer Review History

Original SubmissionApril 29, 2024
27 May 2024 Decision Letter - Guanghui Liu, Editor PONE-D-24-15861Causal Debiasing for Unknown Bias in Histopathology - A Colon Cancer Use CasePLOS ONE Dear Dr. Banerjee, 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 Jul 11 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. 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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: http://journals.plos.org/plosone/s/supporting-information. [Note: HTML markup is below. Please do not edit.] 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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 3. 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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: Yes ****** 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: The manuscript titled "Causal Debiasing for Unknown Bias in Histopathology - A Colon Cancer Use Case": provides a valuable contribution to the field of digital pathology and AI, addressing a critical issue of bias in AI models. Addressing the points below may enhance the manuscript's clarity, impact, and scientific rigor. Major Concerns: 1. While the causal modeling approach is a strong point of the paper, the explanation of the model and its components could be improved for better clarity. Specifically, the description of how the causal model integrates with the existing AI frameworks could be elaborated. Recommendation: Provide a more detailed step-by-step explanation of the causal model implementation, possibly complemented by pseudocode or a more detailed schematic diagram. 2. The manuscript could benefit from a more detailed statistical analysis section. Details about the statistical tests used to validate the model's performance and the rationale behind choosing these tests should be clearly stated. Recommendation: Enhance the statistical analysis section by detailing the types of tests performed, including any assumptions made and the justification for the choice of these tests. 3. Discussion of Limitations: The manuscript briefly mentions the limitations; however, a more thorough exploration of these limitations would strengthen the paper. It is particularly important to discuss the potential impacts of these limitations on the study's findings and how they might be addressed in future work. Recommendation: Expand the limitations section to include a discussion on how these limitations could affect the study's generalizability and what future research could address these issues. 4. Broader Implications: The paper would benefit from a discussion on the broader implications of this research, particularly in how it might influence future developments in AI for medical imaging, policy-making, and clinical practices. Recommendation: Include a section that explores the broader implications of your findings for the field of digital pathology and AI, considering both technological and ethical aspects. Minor Points: 1. Figure and Table Quality: Some figures and tables could be clearer. Specifically, the graphical representations of the model's performance across different sites are somewhat difficult to interpret. Recommendation: Improve the quality of these graphical elements to enhance clarity. Consider using color contrasts or different chart types if necessary. 2. Editing for Language and Grammar: Minor grammatical errors and typos should be corrected to maintain the manuscript's professionalism. Recommendation: Perform a thorough proofread to correct these issues before final submission. Here are some grammatical corrections and suggestions for improvement to enhance the clarity and precision of the writing: 1. Verb Tense Consistency: - Original: "The study have shown..." - Suggested Correction: "The study has shown..." - Explanation: The verb "has" should be used for singular subjects like "study" to maintain subject-verb agreement. 2. Article Usage: - Original: "We apply a novel method to address issue." - Suggested Correction: "We apply a novel method to address the issue." - Explanation: Use of the definite article "the" is necessary before "issue" to specify which issue is being discussed. 3. Prepositional Phrases: - Original: "This is critical for ensuring that AI applications is robust." - Suggested Correction: "This is critical for ensuring that AI applications are robust." - Explanation: The verb "are" correctly agrees with the plural noun "applications." 4. Punctuation and Compound Sentences: - Original: "The model performs well in testing environments, however, it requires further validation." - Suggested Correction: "The model performs well in testing environments; however, it requires further validation." - Explanation: A semicolon is more appropriate before "however" when it's used to connect two independent clauses in a compound sentence. 5. Redundancy and Wordiness: - Original: "The data was gathered from a variety of different sources." - Suggested Correction: "The data was gathered from a variety of sources." - Explanation: The word "different" is redundant with "variety" and can be omitted for conciseness. 6. Consistency in Technical Terms: - Original: "de-biasing techniques can help in reduction of errors" - Suggested Correction: "debiasing techniques can help reduce errors" - Explanation: Maintain consistency in hyphenation across the document ("debiasing" not "de-biasing"), and simplify the phrase "help in reduction of" to "help reduce" for clarity. Reviewer #2: This study proposed the Causal Survival model leverages causal reasoning to mitigate unknown biases, achieving comparable performance to traditional models and enhancing generalizability in histopathology applications.The topic holds significant importance; however, there are several questions that need to be addressed. 1. Do you perform k-fold cross validation during the model training? Please show the results with k-fold cross validation. 2. Please show the demographic details of cohort in various participating sites in tables. 3. Please add the github link with the code for your model in this paper. Reviewer #3: Introduction and Background: The introduction provides a solid context for the study, emphasizing the challenges of bias in AI models for histopathology. The references to prior work and the identification of gaps in existing methods are well-articulated. Methodology: The methodology is thorough and includes detailed descriptions of the datasets, feature extraction processes, and the proposed model. The incorporation of latent shift adjustment and auxiliary losses is innovative and well-explained. Results: The results are clearly presented, with appropriate statistical measures and visualizations. The comparative performance analysis with existing models is comprehensive and shows the advantages of the proposed model. Discussion: The discussion is insightful, highlighting the implications of the findings and acknowledging the limitations of the study. The authors provide a balanced view of their contributions and the areas that need further research. Data Availability: While the data privacy constraints are understandable, providing more detailed guidance on how researchers can access the data would be beneficial. Additionally, sharing synthetic datasets or detailed simulation setups could enhance reproducibility. Technical Depth: The technical depth of the paper is commendable. However, a more detailed explanation of certain complex concepts, such as the latent shift causal framework, could be beneficial for readers less familiar with these methods. Figures and Tables: The figures and tables are well-designed and effectively convey the key findings. Ensuring that all figures have descriptive captions would improve clarity. Overall, this manuscript makes a significant contribution to the field of AI in histopathology by addressing the critical issue of unknown bias. The proposed model is innovative, and the results are promising. With minor revisions to improve accessibility and reproducibility, this paper would be a valuable addition to the literature. ****** 6. PLOS authors have the option to publish the peer review history of their article (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 #1: Yes: Peng Wang Reviewer #2: No Reviewer #3: Yes: Yang Zhang ******** [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. https://doi.org/10.1371/journal.pone.0303415.r001
Revision 1
14 Aug 2024 Author Response Reviewer #1: The manuscript titled "Causal Debiasing for Unknown Bias in Histopathology - A Colon Cancer Use Case": provides a valuable contribution to the field of digital pathology and AI, addressing a critical issue of bias in AI models. Addressing the points below may enhance the manuscript's clarity, impact, and scientific rigor. We are glad that the reviewer found this manuscript to add a valuable contribution to the field of digital pathology. Major Concerns: 1. While the paper's causal modeling approach is a strong point, the explanation of the model and its components could be improved for better clarity. Specifically, the description of how the causal model integrates with the existing AI frameworks could be elaborated on. Recommendation: Provide a more detailed step-by-step explanation of the causal model implementation, possibly complemented by pseudocode or a more detailed schematic diagram. We thank the reviewer for the suggestion to make the description of the causal model easier for readers to understand. We have modified the manuscript in two ways. First, we have modified the introduction to establish how the proposed causal framework fits into existing AI learning methodologies. Second, we also updated the method section to include training details for each module composing our network to make it more understandable. Instead of pseudocode, we added a GitHub link to the actual model training code used for the manuscript to improve the reproducibility for readers. 2. The manuscript could benefit from a more detailed statistical analysis section. Details about the statistical tests used to validate the model's performance and the rationale behind choosing these tests should be clearly stated. Recommendation: Enhance the statistical analysis section by detailing the types of tests performed, including any assumptions made and the justification for the choice of these tests. We thank the reviewer for highlighting our limited discussion on statistics used in the manuscript. We added a ‘Model Evaluation’ section to include an explanation of the metrics for model evaluation. Furthermore, we have also included an explanation of the bootstrapping procedure used to derive the confidence intervals shown in the paper. We also included more descriptive sentences justifying using the log-rank test and Pearson correlation when discussing dataset statistics. 3. Discussion of Limitations: The manuscript briefly mentions the limitations; however, a more thorough exploration of these limitations would strengthen the paper. It is particularly important to discuss the potential impacts of these limitations on the study's findings and how they might be addressed in future work. Recommendation: Expand the limitations section to include a discussion on how these limitations could affect the study's generalizability and what future research could address these issues. We thank the reviewer for discussing the limitations of our study to properly ground our findings. We have included an additional discussion on the fact that our model's success largely depends on being able to reliably learn C and W variables during training. However, these variables are subject to noisy labels or cannot be quickly discovered by a network. We refrain from discussing how to mitigate such a limitation as it falls under a limitation of all existing supervised learning methods, which would distract from main focus of the manuscript. 4. Broader Implications: The paper would benefit from a discussion on the broader implications of this research, particularly in how it might influence future developments in AI for medical imaging, policy-making, and clinical practices.Recommendation: Include a section that explores the broader implications of your findings for the field of digital pathology and AI, considering both technological and ethical aspects. We thank the reviewer for their suggestion. We have expanded the discussion to include a statement on the implications of our model. In particular, we highlight that by using the latent variable as the target for adaptation, we move onto a more diverse space not limited by features that may not be available in all datasets. We further discuss how this work can be applied to other medical imaging AI applications, which are subject to domain and label shift, such as chest x-ray pathology screening and breast cancer prediction. 5. Figure and Table Quality: Some figures and tables could be clearer. Specifically, the graphical representations of the model's performance across different sites are somewhat difficult to interpret. Recommendation: Improve the quality of these graphical elements to enhance clarity. Consider using color contrasts or different chart types if necessary. We thank the reviewer for their recommendation. We have improved the quality of our figure by introducing more contrasting colors, larger points, and reference lines for easier interpretation. The added discussion of the methods section will assist readers in interpreting the tables. 6. Editing for Language and Grammar: Minor grammatical errors and typos should be corrected to maintain the manuscript's professionalism. Recommendation: Perform a thorough proofread to correct these issues before final submission. Here are some grammatical corrections and suggestions for improvement to enhance the clarity and precision of the writing: 1. Verb Tense Consistency: - Original: "The study have shown..." - Suggested Correction: "The study has shown..." - Explanation: The verb "has" should be used for singular subjects like "study" to maintain subject-verb agreement. 2. Article Usage: - Original: "We apply a novel method to address issue." - Suggested Correction: "We apply a novel method to address the issue." - Explanation: Use of the definite article "the" is necessary before "issue" to specify which issue is being discussed. 3. Prepositional Phrases: - Original: "This is critical for ensuring that AI applications is robust." - Suggested Correction: "This is critical for ensuring that AI applications are robust." - Explanation: The verb "are" correctly agrees with the plural noun "applications." 4. Punctuation and Compound Sentences: - Original: "The model performs well in testing environments, however, it requires further validation." - Suggested Correction: "The model performs well in testing environments; however, it requires further validation." - Explanation: A semicolon is more appropriate before "however" when it's used to connect two independent clauses in a compound sentence . 7. 5. Redundancy and Wordiness: - Original: "The data was gathered from a variety of different sources." - Suggested Correction: "The data was gathered from a variety of sources." - Explanation: The word "different" is redundant with "variety" and can be omitted for conciseness. 6. Consistency in Technical Terms: - Original: "de-biasing techniques can help in reduction of errors" - Suggested Correction: "Debiasing techniques can help reduce errors" - Explanation: Maintain consistency in hyphenation across the document ("debiasing" not "de-biasing"), and simplify the phrase "help in reduction of" to "help reduce" for clarity. We would like to thank the reviewer for thoroughly checking the draft. We have updated the manuscript with the proper corrections. Reviewer #2: This study proposed the Causal Survival model leverages causal reasoning to mitigate unknown biases, achieving comparable performance to traditional models and enhancing generalizability in histopathology applications.The topic holds significant importance; however, there are several questions that need to be addressed. We thank the reviewer for considering our manuscript to be important for the field. We replied to the questions below and addressed the changes in the main manuscript. 1. Do you perform k-fold cross-validation during the model training? Please show the results with k-fold cross-validation We thank the reviewer for suggesting using k-fold cross-validation. We did not perform k-fold cross-validation during model training since the main focus of the manuscript is improving the model performance on external test data that is never seen during training. Instead, we provided an auto-bootstrap evaluation of our model on each test set, which allowed us to derive confidence intervals and comprehend the robustness of the model performance on each site. 2. Please show the demographic details of the cohort in various participating sites in tables. Unfortunately, we don’t have access to the demographic details of the patients per site. 3. Please add the GitHub link with the code for your model in this paper. We apologize to the reviewer for this oversight. We have updated the manuscript to include the GitHub link of the training code used for the manuscript. Reviewer #3: Introduction and Background: The introduction provides a solid context for the study, emphasizing the challenges of bias in AI models for histopathology. The references to prior work and the identification of gaps in existing methods are well-articulated. 1. Data Availability: While the data privacy constraints are understandable, providing more detailed guidance on how researchers can access the data would be beneficial. Additionally, sharing synthetic datasets or detailed simulation setups could enhance reproducibility. We thank the reviewer for their interest in data availability. The data is available upon reasonable request from the colon cancer family registry. QuantCRC features can also be available upon data usage agreement. We have updated the manuscript to include the GitHub link of the training code used for the manuscript. 2. Technical Depth: The technical depth of the paper is commendable. However, a more detailed explanation of certain complex concepts, such as the latent shift causal framework, could be beneficial for readers less familiar with these methods. We thank the reviewer for commenting on improving the approachability of our manuscript. We have updated our manuscript and added a supplemental section with more in-depth material to further assist readers in understanding the concepts discussed in our work. Attachments Attachment Submitted filename: PlosOne_Manuscript_comments.docx https://doi.org/10.1371/journal.pone.0303415.r002
16 Sep 2024 Decision Letter - Guanghui Liu, Editor Causal Debiasing for Unknown Bias in Histopathology - A Colon Cancer Use Case PONE-D-24-15861R1 Dear Dr. Banerjee, 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. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. 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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: All comments have been addressed Reviewer #2: All comments have been addressed ******** 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 #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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 Reviewer #2: 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 #2: 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: The authors have thoroughly addressed all of my concerns. I have no further questions and recommend proceeding with the acceptance process according to the journal's guidelines. Reviewer #2: I am satified with the improvement the authors made on the reearch paper. I have no further comments on the current version of paper draft. ****** 7. PLOS authors have the option to publish the peer review history of their article (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 #1: Yes: peng wang Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0303415.r003
Formally Accepted
8 Oct 2024 Acceptance Letter - Guanghui Liu, Editor PONE-D-24-15861R1 PLOS ONE Dear Dr. Banerjee, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, 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 onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Guanghui Liu Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0303415.r004

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