18-January-2026

Dear Lorenzo Faggioni, M.D., Ph.D. Academic Editor, PLOS ONE

Thank you very much for the opportunity to revise our original article entitled “Accuracy and Reproducibility of Tumor Size Measurement Using a Deep-Learning–Based CDSS in Resected Lung Cancer (PONE-D-25- 39492).” After carefully reading the reviewer’ and editor’s comments, we have tried to improve the quality and legibility of the manuscript according to the points raised.

In the revised version, changes are indicated by highlighting. Individual points (E-editor’s comments and R-#1 = point 1 made by reviewer) are indicated in red.

PONE-D-25-39492

The diagnostic performance of deep-learning based clinical decision support system (CDSS) to predict tumor size for patients with resected lung cancer.

PLOS One

Dear Dr. Kim,

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Kind regards,

Lorenzo Faggioni, M.D., Ph.D.

Academic Editor

PLOS One

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� Yes, we did.

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Financial disclosure

This research was supported by a grant of the Korean Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2022-KH129742). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

With Best Regards,

Jong-Yeup Kim

Department of Biomedical Informatics, Konyang University College of Medicine, Daejeon, Republic of Korea

jykim@kyuh.ac.kr

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none

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� Yes, we did.

The authors have declared that no competing interests exist.

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� Thank you for raising this important point regarding Figures 1 and 2.

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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: No

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?

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Reviewer #1: No

Reviewer #2: Yes

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

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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: The study addresses an important clinical task; however, several methodological issues need clarification to ensure the soundness of the findings.

1. Title and aim: “predict” vs “measure”.

The manuscript repeatedly refers to “predicting tumour size” and “diagnostic performance”, but the study actually evaluates measurement accuracy and reproducibility of CT-based tumour size compared with pathology and radiologists. Since the reported ICCs show similar agreement between MONCAD LCT and routine reports, the emphasis on “prediction” seems conceptually misleading. I suggest reframing the aim toward measurement accuracy/consistency or defining more precisely what “predict” means in this context.

�We sincerely appreciate your thoughtful feedback. The manuscript has been revised to reflect your suggestion.

2. Description of the deep learning–based system.

The Methods section provides very limited information about MONCAD LCT despite describing it as a deep learning–based CDSS. Key functions such as nodule detection are not mentioned in Methods, although detection rates appear in Results. Likewise, the approach to delineating tumour margins, handling part-solid nodules, and determining the maximum solid-component diameter (2D plane vs 3D volumetric search) is not described. Some concise explanation of these components—within the limits of a commercial product—is necessary to understand how the system achieves its reported reproducibility.

� Thank you for your insightful comment regarding the need to clarify the technical functions of the MONCAD LCT system. We have revised the <Methods section> and added a supplementary appendix (S1 table) to provide a more detailed description of the system’s architecture and core functionalities, including nodule detection, segmentation, and tumor size measurement.

3. Extraction and definition of radiology report measurements.

The process by which tumour size values were retrieved from routine radiology reports is insufficiently described, especially for part-solid nodules where both total and solid-component diameters may be documented. Without a clear extraction protocol, it is difficult to interpret whether the “report” measurements used for comparison reflect the original clinical assessment or choices made during retrospective data collection.

� Thank you for your insightful comment.

We have clarified the process by which tumor size was extracted from the original radiology reports. Specifically, we now state that in part-solid nodules, the solid component’s maximum diameter was used, following the IASLC 8th edition TNM guidelines. We also confirm that no remeasurement was performed and all values reflect the original clinical interpretation. This clarification has been added to the Methods section of the revised manuscript

4. Use of the term CDSS.

Although MONCAD LCT may be marketed as a CDSS, in this study it supports only a narrow component of NSCLC evaluation (size assessment for T staging). It may be helpful to clarify this limited scope or use a more precise term.

Clarifying these points would significantly strengthen the methodological transparency and interpretation of the work.

�Thank you for pointing out the need to clarify the scope of the CDSS function in our study.

We agree that, although MONCAD LCT is marketed as a CDSS, in this study it was applied solely for its tumor size measurement function on preoperative CT, relevant to T-staging in NSCLC. To avoid confusion, we have clarified this point in the revised

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