PONE-D-22-31819Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18 F-FDG PET/CTPLOS ONE

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Academic Editor

PLOS ONE

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Thank you for submitting your valuable manuscript to PLOS ONE. We would like to inform you that the peer review process of your manuscript is complete and requires "Major Revision". Although it has several useful points, it needs Major Revision to be reviewed again. To assist you in making your alterations, we are enclosing the comments below.

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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: Partly

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: N/A

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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: Yes

Reviewer #2: Yes

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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: No

Reviewer #2: Yes

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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: The aim of this study was to predict pCR after NAC by DL method based on FDG-PET/CT image. The author concluded that the DL method can predict breast cancer treatment.

The abstract is poor, please rewrite more detail to be able to understand the contents of this study.

The weak point of this study was

1. examined by a small number of cohorts with large variation.

2. no case was used for validation in the DL algorism (DLA)

The author stated that “ the study was conducted with rather a small number of patients. Future prospective and large database studies should be used to further validate and investigate our results.”

The state indicated that the algorism created by the author is not assured the reliability and reproductivity. The author should add analysis with each index (SUVmax, TLG, MTV, T and N grade,,,,) which have been assessed for the prediction of pCR in previous studies, and compared the result with the performance obtained by the DLA.

3. Unknown indication for performing surgery after NAC

4. Unknown the target lesion or residual lesion for the assessment of the effect of NAC.

I think the author analyzed the FDG-PET/CT image performed before the indication of NAC (please state it clearly). The author selected baseline FDG-PET/CT images in the cohort that performed surgical resection after NAC. How can this method be used in a clinical situation? If DLA can suggest the possibility of pCR after the NAC, it does not assure the indication of operation after the NAC.

Following the study condition, DLA may predict the pCR only in the case having an indication of surgical operation after NAC, but it can be definitely clear based on the pathological diagnosis soon after. It is not clear the significance of the result of this study toward the clinical situation.

Others

Please explain by what modality the author confirmed the radiological T and N grade, tumor size and LN lymph node level. The author stated that the inclusion criteria for the study were the clinical status of II to III according to the 8th Edition tumor-node-metastasis (TNM) classification of the American Joint Committee on Cancer Staging. Therefore, the cancer staging should be decided with consistency.

Please explain the DLA used for the detection of the primary breast tumor. Did the author validate the image picked up by DLA as correct?

Did the author measure the SUVmax of the primary breast tumor by DICOM format image before converting it to JPEG? It seems confusing from the author's description.

Digital Imaging and Communication in Medicine should be DICOM as an abbreviation.

Please explain why the author selected 5-fold for cross-validation. Considering the number of cohorts, 10 times or leave one out cross-validation is generally adopted.

Reviewer #2: Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18 F-FDG PET/CT

The presented manuscript evaluates if data derived from 18F-FDG PET/CT implemented in a deep learning algorithm are predictive for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC). The authors evaluate an interesting thesis because histopathologic workup is needed by now to clarify pCR after NAC. A deep learning algorithm that uses PET/CT data to identify patients who achieve a pathologic complete response after NAC could have a therapeutic impact.

The English language is acceptable but needed further proof reading. The repetition of same sentence beginnings, especially in the introduction part of the manuscript, disturb the flow of reading for the reader. In addition, it should be standardized in the manuscript whether a space comes after/before mathematical characters. Generally, there is a space before/after a mathematical character except for percent signs.

There are further limitations in the manuscript:

- Introduction part: “Breast cancer is the most common form of cancer and the second most common cause of cancer death amongst women.”

� The author is asked to provide a suitable citation.

- The author is asked to indicate in the material and methods part who has performed the data analysis and what experience exists.

- 2.3: Please give a citation for the definition of pCR.

- 2.4.: SUVmax values are measured by using ROI´s in the data evaluation. This could lead to a missing of the true SUVmax value. However, implementing image morphological markers such as tumor size, SUVmax, HU might improve the deep learning process.

- The major limitation is the reduced number of participants included in this study. Does every patient receive the same number of PET/CT examinations to avoid bias?

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Reviewer #1: No

Reviewer #2: No

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Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18 F-FDG PET/CT

PONE-D-22-31819R1

Dear Dr. Bulut,

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,

Erdal Karavaş, M.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear Author,

The manuscript has been accepted as a result of the revision.

Kind regards.

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: All comments have been addressed

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

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

Reviewer #1: Yes

Reviewer #3: Yes

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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

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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: (No Response)

Reviewer #3: The recommendations are satisfyingly performed. The final version of the artile is sound and can be accepted.

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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: Yes: SONAY AYDIN

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