Title: Artificial Intelligence Networks for Assessing the Prognosis of Gastrointestinal Cancer to
Immunotherapy Based on Genetic Mutation Features: A Systematic Review and Meta-Analysis
Version: 2 Date: 04 Mar 2025
Reviewer's report:
This paper explores the application of artificial intelligence (AI) models based on genetic
mutation signatures for prognostic prediction in immunotherapy for gastrointestinal cancers
through a systematic review and meta-analysis. The study design adheres to PRISMA
guidelines, and the data analysis methods demonstrate rigor. The results highlight the potential
clinical value of AI models. However, certain methodological details, data interpretation aspects,
and adherence to writing standards require further refinement. Specific comments are as follows:
1. The author is required to supplement the PRISMA checklist, typically in the form of an
appendix.
2. Although the study employed random-effects models and regression analyses to address
heterogeneity, certain subgroup analysis results (e.g., pancreatic cancer AUC=0.52) were
notably lower than those of other cancer types. Further discussion is warranted to explore
potential underlying reasons, such as small sample sizes, distinct genetic mutation profiles, or
biological heterogeneity specific to pancreatic cancer.
3. The text mentions the use of regression models and multiple imputation to handle missing
values, but does not specify the implementation details of the imputation method (such as the
number of imputations, the basis for variable selection). It is recommended to supplement the
methodological description to enhance transparency and reproducibility.
4. There is a discrepancy in the gender ratio between the training and test sets: the test set
comprises 46.7% males and 38.2% females, totaling 84.9%, whereas the training set consists
of 27.4% males and 25.7% females, summing up to 53.1%. It is essential to confirm the
completeness of the data or provide an explanation for the remaining unreported proportion,
such as any missing data.
5. Some figures are blurry, please improve the clarity of the pictures in the paper.
Title: Artificial Intelligence Networks for Assessing the Prognosis of Gastrointestinal Cancer to
Immunotherapy Based on Genetic Mutation Features: A Systematic Review and Meta-Analysis
Version: 2 Date: 04 Mar 2025
Reviewer's report:
This paper explores the application of artificial intelligence (AI) models based on genetic
mutation signatures for prognostic prediction in immunotherapy for gastrointestinal cancers
through a systematic review and meta-analysis. The study design adheres to PRISMA
guidelines, and the data analysis methods demonstrate rigor. The results highlight the potential
clinical value of AI models. However, certain methodological details, data interpretation aspects,
and adherence to writing standards require further refinement. Specific comments are as follows:
1. The author is required to supplement the PRISMA checklist, typically in the form of an
appendix.
2. Although the study employed random-effects models and regression analyses to address
heterogeneity, certain subgroup analysis results (e.g., pancreatic cancer AUC=0.52) were
notably lower than those of other cancer types. Further discussion is warranted to explore
potential underlying reasons, such as small sample sizes, distinct genetic mutation profiles, or
biological heterogeneity specific to pancreatic cancer.
3. The text mentions the use of regression models and multiple imputation to handle missing
values, but does not specify the implementation details of the imputation method (such as the
number of imputations, the basis for variable selection). It is recommended to supplement the
methodological description to enhance transparency and reproducibility.
4. There is a discrepancy in the gender ratio between the training and test sets: the test set
comprises 46.7% males and 38.2% females, totaling 84.9%, whereas the training set consists
of 27.4% males and 25.7% females, summing up to 53.1%. It is essential to confirm the
completeness of the data or provide an explanation for the remaining unreported proportion,
such as any missing data.
5. Some figures are blurry, please improve the clarity of the pictures in the paper.
Please review this paper.
user
Title: Artificial Intelligence Networks for Assessing the Prognosis of Gastrointestinal Cancer to
Immunotherapy Based on Genetic Mutation Features: A Systematic Review and Meta-Analysis
Version: 2 Date: 04 Mar 2025
Reviewer's report:
This paper explores the application of artificial intelligence (AI) models based on genetic
mutation signatures for prognostic prediction in immunotherapy for gastrointestinal cancers
through a systematic review and meta-analysis. The study design adheres to PRISMA
guidelines, and the data analysis methods demonstrate rigor. The results highlight the potential
clinical value of AI models. However, certain methodological details, data interpretation aspects,
and adherence to writing standards require further refinement. Specific comments are as follows:
1. The author is required to supplement the PRISMA checklist, typically in the form of an
appendix.
2. Although the study employed random-effects models and regression analyses to address
heterogeneity, certain subgroup analysis results (e.g., pancreatic cancer AUC=0.52) were
notably lower than those of other cancer types. Further discussion is warranted to explore
potential underlying reasons, such as small sample sizes, distinct genetic mutation profiles, or
biological heterogeneity specific to pancreatic cancer.
3. The text mentions the use of regression models and multiple imputation to handle missing
values, but does not specify the implementation details of the imputation method (such as the
number of imputations, the basis for variable selection). It is recommended to supplement the
methodological description to enhance transparency and reproducibility.
4. There is a discrepancy in the gender ratio between the training and test sets: the test set
comprises 46.7% males and 38.2% females, totaling 84.9%, whereas the training set consists
of 27.4% males and 25.7% females, summing up to 53.1%. It is essential to confirm the
completeness of the data or provide an explanation for the remaining unreported proportion,
such as any missing data.
5. Some figures are blurry, please improve the clarity of the pictures in the paper.
assistant