-->PONE-D-25-63592-->-->Voxel-wise deep learning segmentation of hydroxyapatite and iodine in spectral photon-counting CT: A quantitative phantom study-->-->PLOS One

Dear Dr. Francis,

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Kind regards,

Abdel-Razzak M. Al-Hinnawi, Ph.D.

Academic Editor

PLOS One

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Additional Editor Comments:

1-) Please shorten the abstract. Try to make it more concise. It is not intelleigibly presented. It exceeds the world limit. I think authors can shorten it and put it into a more "concise" and "Fluent" structure.

2-) A concern by a reviewer is that if there are public datasets, how could the findings from this manuscript be generalized to public datasets? A short paragraph in the discussion section is recommended. You may add references in this context or use teh available references.

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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: Partly

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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?

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.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.-->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.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

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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

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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: This manuscript presents a technically sound and carefully executed phantom study investigating voxel-wise, concentration-aware classification of hydroxyapatite and iodine using spectral photon-counting CT and deep learning. The authors propose a spectral-preserving 3D UNet variant (SPFF–UNet) and benchmark it against multiple established architectures under a unified training and evaluation protocol. However, points for improvement / clarification

1) Data availability and reproducibility

To fully align with PLOS ONE’s data-sharing policy, the authors should include a clear Data Availability Statement specifying whether datasets, trained models, or code will be made publicly available, and where.

2) Clinical claims and framing

While the discussion is generally cautious, a few statements could be further softened to avoid any implication of near-term clinical applicability. Explicitly reiterating that no clinical claims are made and that in vivo validation is required would strengthen alignment with the journal’s scope.

3) Justification of classification vs. regression

Although implicit in the motivation, a brief explicit discussion explaining why discrete concentration-aware classification was favored over continuous regression (commonly used in spectral CT) would improve clarity for a broad readership.

4) Statistical generalizability

The authors may wish to emphasize that the external test set consists of a single phantom scan and that reported variability reflects training stochasticity rather than population-level variance. This is a limitation rather than a flaw, but making it explicit would preempt reviewer concerns.

Reviewer #2: must be compare the used dataset with global used dataset to be a strong reference and a good scientific methodology used and continue compare with another standard data set from the outside to be more accuracy and confidential

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Reviewer #1: No

Reviewer #2: Yes: Dr Mohammad Abosaaleek isra universityDr Mohammad Abosaaleek isra universityDr Mohammad Abosaaleek isra universityDr Mohammad Abosaaleek isra university

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Voxel-wise deep learning segmentation of hydroxyapatite and iodine in spectral photon-counting CT: A quantitative phantom study

PONE-D-25-63592R1

Dear Dr. Francis,

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,

Abdel-Razzak M. Al-Hinnawi, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments :

Typo recommendation:

Please, shift the statement in lines 398-399-400 (first sentence in the discussion) to the end of the conclusion section : "This is a phantom-based proof-of-concept study and that no near-term clinical claims are made; in vivo validation across scanners, protocols, and patient populations will be required before clinical translation can be considered." (i.e., It is recommended that this statement to be the last statement in the Discussion section) . Thank you.