PONE-D-24-15770Study on Microwave Ablation Temperature Prediction Model Based on Grayscale Ultrasound Texture and Machine LearningPLOS ONE

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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: No

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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.

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: Yes

Reviewer #2: No

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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: 1.Is the selection of temperature measurement points related to the subsequent machine learning model? Why not select the vertical cross-section of the antenna as the temperature measurement point to predict the ablation radius? What is the necessity of vertically arranging 7 points? What is the significance of different distances between temperature measurement points in Figure 1?

2.On line 264, does Sv represent the cavitation area? It's best to have a unified statement

3.On line 278, x represents whether it represents Sv or Sp respectively. It is suggested to clarify the meaning of x

4.Comparing Figure 6 with Figure 7, it should be that the temperature is low, but there is an abnormal phenomenon at temperature measurement points 2 # and 3 #. How to explain this?

5.In the analysis of the results on line 305, did we conclude that the larger the prediction range, the greater the error? Or is it just a case study?

6.Figures 10 and 11 show Scatter plots of representative features with temperature at different power levels, with different conclusions. Therefore, whether there is a certain pattern for the important features commonly used in clinical microwave power levels of 60-100W or different heating times, or whether all the processes in the article need to be repeated, please provide the universality and significance of this study in the main text

7.There is too little introduction to the content of machine learning, which makes it difficult to fully understand the author's model, especially regarding how the first three features of importance ranking in section 4.4 were obtained?

Reviewer #2: General Comments:

MRI and CT treatment monitoring currently offer the greatest potential for thermal therapies to emerge into clinical practice. That said, given the associated high cost and limited accessibility, there is a need for cost-effective treatment monitoring tools like what is presented in this manuscript. Overall the temperature prediction method and results are interesting, but the image analysis results are somewhat confusing. A number of deficiencies need to be addressed.

First of all, it is not clear what treatment outcomes are being measured and compared in Fig 8. Throughout the manuscript the authors refer to ablation zone, vaporization zone, coagulation zone and cavitation area. The authors need to clearly state the tissue characteristics for these regions (what is going on) and if these regions of the thermal lesion are distinct or overlapping. It is clear that at the end of the heating (Fig. 8d,h) there is a region of tissue charring at the ablation needle surrounded by tissue coagulation. Without some description of the mechanisms for thermal lesion formation, it is difficult to interpret the data and analysis. For example, it is not clear in Tables 1 and 2 which data sets are to be compared. The microbubble zone is consistently larger than the tissue coagulation zone, but is this expected? In addition, the ML method consistently underestimates the coagulation area compared to the ImageJ data (used as a gold standard), but no explanation is provided. This needs to be discussed.

I'd also recommend to not refer to the formation of microbubbles as cavitation, as the mechanism here is MW heating, and is different from US induced cavitation. I found this confusing.

The images Fig 8d,h, clearly show tissue charring (temperatures in excess of 100 C, leading to water vaporization - microbubble formation). This is typically to be avoided during thermal therapy, due to the associated smoke production and the resulting change in tissue permittivity which alters antennae performance and power delivery. So a better test would be using MW powers that result in a maximum tissue temperature of 90 C.

In the Discussion (p.10), the author's state "The results indicate that this method can more effectively assess the ablation area.", but offer no justification, i.e., compared to what other method(s).

Specific Comments/Questions:

1. p.4, It is not clear what is meant by "texture information changes", please add a brief explanation.

2. What is the US frequency? The authors refer to a sampling frequency of 15 MHz, but that is not US frequency.

3. p.6, What type of thermocouple is used, Type-K, Type-T?

4. p.6, What are the dimensions of the mold (e.g what volume of liver is used in each experiment).

5. Fig 1B, It appears that the thermocouples are all not in the same plane. Please clarify. I'd also suggest changing in the caption “theoretical” to “desired” and include text to describe the temperature probes (blue) and ablation needle (red).

6. Fig 3 caption needs to be expanded to include a description of the individual process steps.

7. Fig 4 caption needs to be expanded to describe the image, e.g. An US frame showing the ablation needle and……..

8. Fig 5 caption needs to be more descriptive.

9. Fig 8 image, “width” not “wideth” and the caption needs to be more descriptive.

10. p. 10, The radiometric texture features used in the analysis are only referenced. These need to be described at least briefly in the manuscript for the reader to understand their utility.

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Reviewer #1: No

Reviewer #2: No

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Study on Microwave Ablation Temperature Prediction Model Based on Grayscale Ultrasound Texture and Machine Learning

PONE-D-24-15770R1

Dear Dr. Yan Huang,

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.

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Kind regards,

Carlos Alberto Antunes Viegas, DVM; MSc; PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear authors

Your article is ready to be published, but you must consider the first reviewer's suggestion regarding the second round of revisions in the last version.

Best 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: (No Response)

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

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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: Comparing Figure 6 with Figure 7 and Fig.1 , I think the positions of thermocouples and microwave antennas are fixed in every experiments, so there is no ” However, due to the different distances of the seven measurement points from the high-temperature center“ as author explained. On the contrary, if the positions of the two are not fixed, is the temperature measurement point data in machine learning reliable?

Reviewer #2: (No Response)

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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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