Breast cancer classification based on the integration of diagnostic algorithms for calcifications and masses using a mixture of experts

Yuma Konaka; Takuya Ueda; Ryusei Inamori; Jumpei Sato; Keisuke Sugawara; Yuta Shiratori; Fumihito Hario; Eichi Takaya; Tomoya Kobayashi; Yoshikazu Okamoto

doi:10.1371/journal.pone.0331017

Peer Review History

Original SubmissionMay 19, 2025
15 Jul 2025 Decision Letter - Fahad Almutairi, Editor PONE-D-25-25798Breast cancer classification based on the integration of diagnostic algorithms for calcifications and masses using a mixture of expertsPLOS ONE Dear Dr. Takaya, 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 Aug 29 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. 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Please provide an amended statement that declares all the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now. Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement. Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf. 4. 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. 5. 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 If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references.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. [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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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: Yes Reviewer #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: Yes Reviewer #2: 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: This study presents a well-executed investigation into the application of Mixture of Experts for mammographic classification, rigorously adhering to methodological standards. The manuscript is clearly structured, and the experimental design - including dataset curation, model architecture, and statistical validation - is transparent and reproducible. While the field of medical imaging AI is saturated with incremental contributions, this work stands out by meticulously addressing methodological nuances often overlooked in similar studies. The integration of radiological domain knowledge into the MoE framework enriches the scientific discourse, offering a replicable template for future research. Minor feedback for the authors’ consideration: The reported performance gain is statistically significant but biologically marginal, given the conventional 5% error tolerance in clinical settings. While this does not diminish the study’s technical rigor, it invites reflection on whether such differences translate to tangible clinical utility. That said, the result remains an unequivocal scientific fact, and I commend the authors for their transparency in reporting it. Overall, this is a valuable contribution to the field, and I appreciate the opportunity to review this work. Reviewer #2: The article was reviewed under the title " Breast cancer classification based on the integration of diagnostic algorithms for calcifications and masses using a mixture of experts". Overall, the focus of the study is good. However, I would like to offer several recommendations that authors may find useful in the process of revising their manuscript: 1- Given the importance of the topic, it is necessary to include a review of previous studies in the article. 2- The phrase " in the supplementary analysis of each radiological finding, statistical testing was not applied, as the purpose was not formal comparison but rather descriptive exploration of model behavior " is used in the text of the article. Please provide a more detailed description of this phrase. 3- Abbreviations in the article are given in a paragraph titled "Abbreviations". It is better to introduce them in parentheses in the text of the article for the first time, and then only use the abbreviations. 4- Considering the results presented for the evaluation parameters, it is better to compare these results with the results presented by other researchers in other articles. 5- The conclusion section is very brief and needs to be rewritten. ****** 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: Artyom Borbat 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. Attachments Attachment Submitted filename: comments.docx https://doi.org/10.1371/journal.pone.0331017.r001
Revision 1
31 Jul 2025 Author Response Response to Reviewers Reviewer #1 1. The reported performance gain is statistically significant but biologically marginal, given the conventional 5% error tolerance in clinical settings. While this does not diminish the study's technical rigor, it invites reflection on whether such differences translate to tangible clinical utility. That said, the result remains an unequivocal scientific fact, and I commend the authors for their transparency in reporting it. Response: We sincerely appreciate the reviewer’s thoughtful observation and kind words regarding the scientific rigor of our study. We fully agree that while the performance improvement of MoRFSE over the conventional ResNet18 model was statistically significant, the absolute AUC difference (0.9616 vs. 0.9577) may seem marginal in the context of clinical error tolerance. In response to this valuable comment, we have added a discussion in the revised manuscript to explicitly address this point and to reflect on its clinical implications. Added to Discussion (pages 10 to 11, lines 252 to 259): “Although the performance difference between MoRFSE and Conventional ResNet18 was statistically significant, the absolute AUC improvement (0.9616 vs. 0.9577) may appear marginal in the context of clinical decision-making. In typical clinical practice, especially under a 5% error tolerance, such incremental gains may not immediately translate into tangible improvements in diagnostic outcomes. However, even minor improvements in classification performance can have meaningful implications when scaled across large screening populations, potentially reducing false-positive or false-negative cases. Furthermore, the MoRFSE model contributes beyond raw performance metrics by offering a more interpretable and radiologist-aligned framework, which may facilitate greater clinical acceptance and trust in AI-based tools.” We hope this addition appropriately addresses the reviewer’s concern and provides a more balanced interpretation of the results from both statistical and clinical perspectives. Reviewer #2 1. Given the importance of the topic, it is necessary to include a review of previous studies in the article. Response: We thank the reviewer for pointing out the need to include a more thorough review of related work, which we agree is essential to contextualize our contribution. In response, we have revised the Introduction section to incorporate a discussion of previous deep learning (DL) approaches that focused on lesion-specific modeling in mammography. Added to Introduction (page 3, lines 64 to 74): “Many existing DL models process mammographic findings such as calcifications and masses without explicitly distinguishing between them [11]. From our perspective, this lack of lesion-specific processing may contribute to limitations in diagnostic accuracy, as it diverges from the interpretive strategies of radiologists, who assess these lesion types using different criteria. We hypothesize that treating fundamentally different findings with a single, undifferentiated algorithm may hinder the model’s ability to fully capture their distinct characteristics. To address this issue, some studies have developed CNN-based approaches that focus on a single lesion type. For example, Chakravarthy et al. [12] focused on the detection and classification of microcalcifications, while Barnett et al. [13] developed an interpretable model targeting mass lesions. These studies highlight the benefits of radiological finding-specific modeling, yet they are limited in that each model is optimized for only one type of lesion, and thus cannot generalize to the other.” We believe this addition strengthens the manuscript by better situating our approach within the existing literature and clarifying how our work builds upon and extends prior research efforts. 2. The phrase "in the supplementary analysis of each radiological finding, statistical testing was not applied, as the purpose was not formal comparison but rather descriptive exploration of model behavior" is used in the text of the article. Please provide a more detailed description of this phrase. Response: We appreciate the reviewer’s comment pointing out the need for greater clarity in describing the purpose and rationale of the supplementary analysis. In response, we have revised the corresponding section in the Materials and methods / Statistical analysis to provide a more detailed and precise explanation of the intent behind the analysis and the reason for omitting statistical testing. Added to Materials and methods / Statistical analysis (page 7 to 8, lines 183 to 186): “In the supplementary analysis of each radiological finding, statistical testing was not applied. This is because the analysis was not intended for direct statistical comparison between models or lesion types. Instead, it aimed to descriptively explore the behavior and characteristics of the models when applied to calcification- and mass-specific lesions, offering insights into the model’s diagnostic tendencies from a radiological perspective.” We hope this revision adequately clarifies the purpose and scope of the supplementary analysis and addresses the reviewer’s concern. 3. Abbreviations in the article are given in a paragraph titled "Abbreviations". It is better to introduce them in parentheses in the text of the article for the first time, and then only use the abbreviations. Response: We thank the reviewer for the helpful suggestion regarding the use of abbreviations. In accordance with the reviewer’s recommendation, we revised the manuscript to introduce each abbreviation at its first occurrence in the main text, followed by the abbreviated form in parentheses. Thereafter, only the abbreviations are used throughout the manuscript. In particular, we removed the list of abbreviations from the caption of Figure 2 (page 6, lines 138 to 140) and revised the corresponding text in the Materials and methods / Mixture of Radiological Findings Specific Experts (MoRFSE) section (page 5, lines 126 to 129) to define all abbreviations appropriately upon their first mention. We believe this change improves the readability and clarity of the manuscript and appreciate the reviewer’s attention to this detail. 4. Considering the results presented for the evaluation parameters, it is better to compare these results with the results presented by other researchers in other articles. Response: We appreciate the reviewer’s suggestion to compare our evaluation results with those from previous studies. However, as our study was conducted using a proprietary dataset not commonly used in the existing literature, there were no directly comparable studies available. To address this limitation, we have added a statement to the Discussion section to acknowledge this issue and to highlight the need for future research using publicly available benchmark datasets. Added to Discussion (page 12, lines 295 to 298): “First, our study was conducted using a single dataset, which limited the ability to make direct comparisons with previous studies and may affect the generalizability of the findings. To validate the performance of MoRFSE in a broader and comparative context, future studies using publicly available benchmark datasets that encompass diverse populations and imaging conditions are warranted.” We hope this revision clarifies the scope of our current work and appropriately addresses the reviewer’s concern regarding comparative evaluation. 5. The conclusion section is very brief and needs to be rewritten. Response: We thank the reviewer for this valuable feedback. In response, we have substantially revised the Conclusion section to provide a more comprehensive summary of our study's contributions, findings, and future directions. The new conclusion emphasizes the significance of our proposed method, its alignment with radiologists’ diagnostic processes, and the potential for broader application. Revised Conclusion (pages 12 to 13, lines 307 to 313): “In our study, we proposed MoRFSE, a deep learning framework that integrates specialized algorithms for calcifications and masses to better mimic radiologists’ diagnostic strategies. MoRFSE achieved significantly higher classification performance compared to conventional models, indicating the effectiveness of radiological finding-specific expert networks and a dynamic gate mechanism. Our results suggest that incorporating domain-specific knowledge into model architecture can improve diagnostic accuracy in mammography-based breast cancer detection. Future work should aim to validate these findings across diverse datasets and investigate the potential integration of additional radiological findings such as architectural distortion.” We believe this revision enhances the clarity and impact of the conclusion and better reflects the significance of our work. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0331017.r002
10 Aug 2025 Decision Letter - Fahad Almutairi, Editor Breast cancer classification based on the integration of diagnostic algorithms for calcifications and masses using a mixture of experts PONE-D-25-25798R1 Dear Dr. Takaya, 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. For questions related to billing, please contact billing support . 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 onepress@plos.org. Kind regards, Fahad Farhan Almutairi, PhD Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0331017.r003
Formally Accepted
Acceptance Letter - Fahad Almutairi, Editor PONE-D-25-25798R1 PLOS ONE Dear Dr. Takaya, 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 You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days 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. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. 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. Fahad Farhan Almutairi Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0331017.r004

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