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: 03 Mar 2025
Reviewer's report:
The manuscript presents a comprehensive systematic review and meta-analysis on the use of
artificial intelligence (AI) networks in predicting immunotherapy response in gastrointestinal
cancers based on genetic mutation features. The study adheres to PRISMA guidelines, employs
a well-defined methodology, and provides insightful discussions on AI’s role in precision
oncology. The statistical analysis is robust, and the manuscript is generally well-structured.
However, several issues need to be addressed before publication.
1. In the statement, “gastrointestinal cancers (GICs) represent the most common types
of malignancies worldwide, characterized by significant mortality rates.”, the term
"Gastrointestinal" should be capitalized
2. The manuscript states that regression models and multiple imputation methods were applied
but does not specify which regression models were implemented.
Please briefly mention whether logistic regression, random forest imputation, or another method
was used to handle missing data.
3. The manuscript reports pooled AUC values for different cancer types (e.g., gastric cancer:
AUC = 0.87, pancreatic cancer: AUC = 0.52), but it does not adequately discuss why AI
models performed poorly for pancreatic cancer. Please expand on possible reasons, such as
pancreatic cancer being an immunologically "cold" tumor with a highly immunosuppressive
tumor microenvironment, low TMB, and minimal PD-L1 expression, all of which reduce its
responsiveness to immunotherapy.
4. Some statements in the limitations section require citations, particularly those discussing
the lack of external validation for AI models in clinical practice. For example, the statement
“Validation and Benchmarking: External validation of AI models is often lacking.” should be
supported by a reference. Please ensure appropriate citations are included to strengthen these
claims.
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: 03 Mar 2025
Reviewer's report:
The manuscript presents a comprehensive systematic review and meta-analysis on the use of
artificial intelligence (AI) networks in predicting immunotherapy response in gastrointestinal
cancers based on genetic mutation features. The study adheres to PRISMA guidelines, employs
a well-defined methodology, and provides insightful discussions on AI’s role in precision
oncology. The statistical analysis is robust, and the manuscript is generally well-structured.
However, several issues need to be addressed before publication.
1. In the statement, “gastrointestinal cancers (GICs) represent the most common types
of malignancies worldwide, characterized by significant mortality rates.”, the term
"Gastrointestinal" should be capitalized
2. The manuscript states that regression models and multiple imputation methods were applied
but does not specify which regression models were implemented.
Please briefly mention whether logistic regression, random forest imputation, or another method
was used to handle missing data.
3. The manuscript reports pooled AUC values for different cancer types (e.g., gastric cancer:
AUC = 0.87, pancreatic cancer: AUC = 0.52), but it does not adequately discuss why AI
models performed poorly for pancreatic cancer. Please expand on possible reasons, such as
pancreatic cancer being an immunologically "cold" tumor with a highly immunosuppressive
tumor microenvironment, low TMB, and minimal PD-L1 expression, all of which reduce its
responsiveness to immunotherapy.
4. Some statements in the limitations section require citations, particularly those discussing
the lack of external validation for AI models in clinical practice. For example, the statement
“Validation and Benchmarking: External validation of AI models is often lacking.” should be
supported by a reference. Please ensure appropriate citations are included to strengthen these
claims.
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: 03 Mar 2025
Reviewer's report:
The manuscript presents a comprehensive systematic review and meta-analysis on the use of
artificial intelligence (AI) networks in predicting immunotherapy response in gastrointestinal
cancers based on genetic mutation features. The study adheres to PRISMA guidelines, employs
a well-defined methodology, and provides insightful discussions on AI’s role in precision
oncology. The statistical analysis is robust, and the manuscript is generally well-structured.
However, several issues need to be addressed before publication.
1. In the statement, “gastrointestinal cancers (GICs) represent the most common types
of malignancies worldwide, characterized by significant mortality rates.”, the term
"Gastrointestinal" should be capitalized
2. The manuscript states that regression models and multiple imputation methods were applied
but does not specify which regression models were implemented.
Please briefly mention whether logistic regression, random forest imputation, or another method
was used to handle missing data.
3. The manuscript reports pooled AUC values for different cancer types (e.g., gastric cancer:
AUC = 0.87, pancreatic cancer: AUC = 0.52), but it does not adequately discuss why AI
models performed poorly for pancreatic cancer. Please expand on possible reasons, such as
pancreatic cancer being an immunologically "cold" tumor with a highly immunosuppressive
tumor microenvironment, low TMB, and minimal PD-L1 expression, all of which reduce its
responsiveness to immunotherapy.
4. Some statements in the limitations section require citations, particularly those discussing
the lack of external validation for AI models in clinical practice. For example, the statement
“Validation and Benchmarking: External validation of AI models is often lacking.” should be
supported by a reference. Please ensure appropriate citations are included to strengthen these
claims.
assistant