PONE-D-24-49402Global Burden and Future Trends of Head and Neck Cancer: A Deep Learning-Based Analysis (1980 - 2030)PLOS ONE

Dear Dr.  Hu,

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Comments to the Author

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Reviewer #1: Yes

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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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: Thank you for submitting this interesting article examining the Global Burden and Future Trends of Head and Neck Cancer: A Deep Learning-Based Analysis (1980 - 2030).

The present study is a multicentric, retrospective study including data from 204 centers for head and neck cancer. Analyses focus on survival-standardized intra-person mortality and disability-adjusted life years (DALYs) for head and neck cancer. A Transformer-based model, HNCP-T, was used to predict trends up to 2030.

The study is well-presented and well-studied but needs a few minor corrections.

1. In the study, while analyzing the global changes in head and neck cancers, the use of the Transformer-based data model instead of LSTMs and RNNs is an appropriate and effective analytical method. The advantages of the Transformer-based model should be emphasized more clearly in the text.

2. When using predictor variables, Plasma Glucose Levels, High Body Mass Index (BMI), and Socio-Demographic Index (SDI) were included. However, given the increasing global burden of HPV and its significant association with head and neck cancers, HPV-related data should also be incorporated into the data packages.

3. Smoking and alcohol consumption undeniably influence the incidence of head and neck cancers. Figure 5 should include data on the impact of smoking and alcohol use, particularly concerning their correlation with socioeconomic levels. If this information is not available in the existing data, this point should be emphasized more clearly in the data analysis or discussion section.

4. Concrete recommendations derived from the study's findings, which can be utilized in public health policies, should be incorporated into the discussion section.

5.The term "the prediction model is consisted of" (Line 190) should be corrected to "the prediction model consists of.

6. The term "plays a important role in" on line 197 should be corrected to "plays an important role in.

7. The expression "gender-specific trends in HNC burden" (Line 374) should be revised to "gender-based trends," and the corresponding sections should be adjusted accordingly.

8. The term "By contrast" (Line 405) should be corrected to "In contrast."

9. The term "a decrease in incidence among older populations indicate" in line 429 should be corrected to "a decrease in incidence among older populations indicates."

10. The term "risks which are associated with aging" in line 433 should be corrected to "risks associated with aging."

11. The phrase "the need for personalized treatment approaches considering the unique challenges confronted by elderly patients" in line 437 should be replaced with "the need for personalized treatment approaches that address the unique challenges faced by elderly patients."

12. The term "where healthcare infrastructure may be less developed, could lead to underestimations of true disease burden in these areas" in lines 444-445 should be revised to "where healthcare infrastructure may be less developed, could lead to underestimating the true disease burden in these areas."

13. The phrase "especially highlighting significant regional and gender-specific disparities" in lines 477-478 is recommended to be revised to "especially highlighting significant regional and gender-based disparities."

Reviewer #2: In order to perform forecasting, the authors rely on the popular neural network based transformer architecture. As they correctly state in the manuscript, this architecture has many advantages compared to prior architectures such as RNNs. Moreover, since this is a deep learning based approach, it is advantageous in terms of modeling non-linear complex relationships. However, even though these are discussed verbally, they are not supported with results. More specifically, I believe that in addition to providing results for the proposed transformer architecture, the authors should provide results for alternative strategies. For instance, one alternative can be a classical statistical model, such as linear regression, and the other can be an alternative deep sequence learning based approach, such as LSTM. I think that this additional experiments are especially critical given that the data at hand is a time-series signal and categorical data. This is because, as opposed to natural language or vision tasks where transformers are absolute winners, in time series or tabular data, even classical methods are known to be able to outperform deep learning strategies frequently, especially when there are missing data or data is inherently noisy.

2) The idea for splitting the data based on the year, instead of random shuffling is very logical.

3) Could authors elaborate more on how the results on test set had guided them for better training?

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Reviewer #1: No

Reviewer #2: No

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Global Burden and Future Trends of Head and Neck Cancer: A Deep Learning-Based Analysis (1980 - 2030)

PONE-D-24-49402R1

Dear Dr. Qiongyuan Hu,

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,

Enes Erul, MD

Academic Editor

PLOS ONE

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 #2: 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 #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

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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 #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: Thank you for submitting this interesting article examining the Global Burden and Future Trends of Head and Neck Cancer: A Deep Learning-Based Analysis (1980 - 2030).

Your findings provide important insights into regional and demographic disparities in HNC burden and emphasize the need for data-driven decision-making in healthcare resource allocation and preventive strategies.

The use of transformer-based deep learning models for forecasting future trends (2022-2030) has been particularly noted as a valuable advancement in epidemiological research.

The changes suggested in the previous reviews have been implemented, and the manuscript is now deemed suitable for publication in the journal.

Reviewer #2: I sincerely appreciate your time and effort in reviewing our manuscript. Thank you for your valuable feedback and thoughtful comments. Your insights have been truly appreciated.

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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: No

Reviewer #2: No

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