Fraction of Genome Altered, Age, Microsatellite Instability Score, Tumor Mutational Burden, Cancer Type, Metastasis Status, and Choice of Cancer Therapy Predict Overall Survival in Multiple Machine Learning Models

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Please see the attachment.

Comments for author File: Comments.pdf

Author Response

I thank reviewer 1 for her/his comments, the explanations and advice. I revised the wording as suggested, only saying that the treatment variable is one of the predictors in the binary classification problem and the RandomSurvivalForest approach. I also removed the regression analysis and only focused on the binary and RandomSurvivalForest approaches as suggested.

Reviewer 2 Report

Comments and Suggestions for Authors

Regarding this manuscript entitled "FGA, Age, MSI Score, TMB, Cancer Type, Metastasis Status and Choice of Cancer Therapy Predict Overall Survival in Multiple Machine Learning Models"

I would like to inform you that this manuscript is methodologically acceptable. The presented materials are well organized and there is no problem in terms of content. Just one thing that bothers me. Self-citation is too much. For this issue, I suggest that you add a section to the introduction based on the applications of machine learning in the field of health care. For this field, you can refer to various studies. Therefore, I suggest that without this part, your manuscript has a great weakness. For this purpose, you can also refer to these studies.

"Artificial intelligence in drug discovery and development against antimicrobial resistance: A narrative review"

 

"Mobile apps for COVID-19 detection and diagnosis for future pandemic control: Multidimensional systematic review"

Using these studies will help enrich your manuscript. It will also provide different sources for those interested in this study.

Also, a definition of machine learning and a limited focus on its applications in the field of health care by mentioning the suggested sources can add to the quality of this study.

Author Response

I thank reviewer 2 for her/his comments. I am sorry, maybe it was not correctly uploaded in the last round of revision on the journal website but I added the following paragraph with new references compared to the initial submission in Cancers:

 

‘The rapid progress of artificial intelligence (AI) in healthcare has further opened new avenues for improving patient care and clinical decision-making. AI-based methods such as machine learning and deep learning have been employed across a wide range of healthcare applications, including image analysis, diagnostics, and personalized treatment strategies. For example, AI has been used to develop tools for early cancer detection through radiographic imaging [10], to predict disease progression in chronic conditions [11], and to identify biomarkers for various diseases [12]. In oncology, AI models have shown promise in analyzing genomic and clinical data to predict treatment outcomes, providing physicians with decision-support tools for personalized therapies [1–3,7,8]. These advancements in AI are not only revolutionizing the way healthcare providers approach patient care but also advancing the precision of predictive models, particularly in cancer prognosis.’

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I thank the author for his effort revising the paper. I have no other suggestions and recommending acceptance with cosmetic revision. Fig 5 now should be Fig 3. Please double check. 

Thanks

Author Response

Thank you, figure 5 is no figure 3. Best

Author Response File: Author Response.docx