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: 02 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-One of the major weaknesses highlighted is the inconsistency in the reported performance
of Artificial Neural Networks (ANNs). Conflicting findings in different studies regarding the
effectiveness of ANNs in predicting ICI therapy outcomes could pose a challenge. While the
systematic review aims to address this, the presence of contradictory reports may undermine the
generalizability of the findings.
-The study notes that TMB is not fully validated as a reliable biomarker for GECs, despite its
potential. The lack of consensus on TMB’s predictive value for immunotherapy responses in
GICs represents a significant limitation, as TMB remains a central aspect of the review.
-While regression models and imputation methods are employed to handle missing data, these
techniques are not without limitations. The use of imputation methods to predict missing values
may introduce biases or inaccuracies if the underlying assumptions of the methods are not fully
met. It is important to consider how these methods may impact the results, especially when
dealing with clinical data, which can have significant variation across studies.
-The findings may be limited by the heterogeneity of the included studies in terms of patient
populations, tumor types, and the variety of AI models used. Variability in ANN architectures,
genetic mutation features, and immunotherapy protocols may make it challenging to draw
universally applicable conclusions.
-The inclusion of studies with missing data and potential biases in study selection (e.g., non-
English publications) could lead to publication bias, which might affect the validity of the pooled
estimates.
-The study does not seem to include an external validation set for the predictive models, which
limits its ability to generalize to other populations or healthcare settings. External validation is
crucial for confirming the reliability of AI-based prognostic models in diverse clinical settings.
-While the discussion cites several studies and their findings (e.g., AUC values), it could benefit
from including a more detailed synthesis of quantitative outcomes across the studies to better
showcase the comparative performance of the AI models. For example, while AUC values are
mentioned, there is no comprehensive discussion of sensitivity, specificity, or other performance
metrics that could provide a more rounded evaluation of the model’s efficacy.
-Although the challenges of data variability and overfitting are addressed, there is little
discussion about specific strategies or approaches to enhance the generalizability of AI models.
For example, exploring how cross-validation methods, external dataset validation, or transfer
learning could improve generalization would provide more insights into overcoming the
limitations of current AI systems.
-Ethical issues like data privacy and the potential misuse of genetic data are introduced but are
not discussed in depth. Given the sensitivity of genetic data and the potential for AI systems
to influence clinical decision-making, further exploration of these ethical challenges—such as
patient consent, algorithmic bias, and the transparency of AI models—would be beneficial.
-The discussion summarizes existing studies without including new primary data, experimental
results, or original insights. While this is acceptable in a systematic review, including more novel
perspectives or recommendations for improving AI applications in GI cancer immunotherapy
would provide greater value to the discussion.
-The review uses terms like "multi-omics data," "immune cell infiltration," and "AI-based
models" without consistently defining these terms for a broader audience. A brief explanation
or reference to how these concepts are defined and measured would ensure clarity for readers
who may not be familiar with the specialized terminology.
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: 02 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-One of the major weaknesses highlighted is the inconsistency in the reported performance
of Artificial Neural Networks (ANNs). Conflicting findings in different studies regarding the
effectiveness of ANNs in predicting ICI therapy outcomes could pose a challenge. While the
systematic review aims to address this, the presence of contradictory reports may undermine the
generalizability of the findings.
-The study notes that TMB is not fully validated as a reliable biomarker for GECs, despite its
potential. The lack of consensus on TMB’s predictive value for immunotherapy responses in
GICs represents a significant limitation, as TMB remains a central aspect of the review.
-While regression models and imputation methods are employed to handle missing data, these
techniques are not without limitations. The use of imputation methods to predict missing values
may introduce biases or inaccuracies if the underlying assumptions of the methods are not fully
met. It is important to consider how these methods may impact the results, especially when
dealing with clinical data, which can have significant variation across studies.
-The findings may be limited by the heterogeneity of the included studies in terms of patient
populations, tumor types, and the variety of AI models used. Variability in ANN architectures,
genetic mutation features, and immunotherapy protocols may make it challenging to draw
universally applicable conclusions.
-The inclusion of studies with missing data and potential biases in study selection (e.g., non-
English publications) could lead to publication bias, which might affect the validity of the pooled
estimates.
-The study does not seem to include an external validation set for the predictive models, which
limits its ability to generalize to other populations or healthcare settings. External validation is
crucial for confirming the reliability of AI-based prognostic models in diverse clinical settings.
-While the discussion cites several studies and their findings (e.g., AUC values), it could benefit
from including a more detailed synthesis of quantitative outcomes across the studies to better
showcase the comparative performance of the AI models. For example, while AUC values are
mentioned, there is no comprehensive discussion of sensitivity, specificity, or other performance
metrics that could provide a more rounded evaluation of the model’s efficacy.
-Although the challenges of data variability and overfitting are addressed, there is little
discussion about specific strategies or approaches to enhance the generalizability of AI models.
For example, exploring how cross-validation methods, external dataset validation, or transfer
learning could improve generalization would provide more insights into overcoming the
limitations of current AI systems.
-Ethical issues like data privacy and the potential misuse of genetic data are introduced but are
not discussed in depth. Given the sensitivity of genetic data and the potential for AI systems
to influence clinical decision-making, further exploration of these ethical challenges—such as
patient consent, algorithmic bias, and the transparency of AI models—would be beneficial.
-The discussion summarizes existing studies without including new primary data, experimental
results, or original insights. While this is acceptable in a systematic review, including more novel
perspectives or recommendations for improving AI applications in GI cancer immunotherapy
would provide greater value to the discussion.
-The review uses terms like "multi-omics data," "immune cell infiltration," and "AI-based
models" without consistently defining these terms for a broader audience. A brief explanation
or reference to how these concepts are defined and measured would ensure clarity for readers
who may not be familiar with the specialized terminology.
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: 02 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-One of the major weaknesses highlighted is the inconsistency in the reported performance
of Artificial Neural Networks (ANNs). Conflicting findings in different studies regarding the
effectiveness of ANNs in predicting ICI therapy outcomes could pose a challenge. While the
systematic review aims to address this, the presence of contradictory reports may undermine the
generalizability of the findings.
-The study notes that TMB is not fully validated as a reliable biomarker for GECs, despite its
potential. The lack of consensus on TMB’s predictive value for immunotherapy responses in
GICs represents a significant limitation, as TMB remains a central aspect of the review.
-While regression models and imputation methods are employed to handle missing data, these
techniques are not without limitations. The use of imputation methods to predict missing values
may introduce biases or inaccuracies if the underlying assumptions of the methods are not fully
met. It is important to consider how these methods may impact the results, especially when
dealing with clinical data, which can have significant variation across studies.
-The findings may be limited by the heterogeneity of the included studies in terms of patient
populations, tumor types, and the variety of AI models used. Variability in ANN architectures,
genetic mutation features, and immunotherapy protocols may make it challenging to draw
universally applicable conclusions.
-The inclusion of studies with missing data and potential biases in study selection (e.g., non-
English publications) could lead to publication bias, which might affect the validity of the pooled
estimates.
-The study does not seem to include an external validation set for the predictive models, which
limits its ability to generalize to other populations or healthcare settings. External validation is
crucial for confirming the reliability of AI-based prognostic models in diverse clinical settings.
-While the discussion cites several studies and their findings (e.g., AUC values), it could benefit
from including a more detailed synthesis of quantitative outcomes across the studies to better
showcase the comparative performance of the AI models. For example, while AUC values are
mentioned, there is no comprehensive discussion of sensitivity, specificity, or other performance
metrics that could provide a more rounded evaluation of the model’s efficacy.
-Although the challenges of data variability and overfitting are addressed, there is little
discussion about specific strategies or approaches to enhance the generalizability of AI models.
For example, exploring how cross-validation methods, external dataset validation, or transfer
learning could improve generalization would provide more insights into overcoming the
limitations of current AI systems.
-Ethical issues like data privacy and the potential misuse of genetic data are introduced but are
not discussed in depth. Given the sensitivity of genetic data and the potential for AI systems
to influence clinical decision-making, further exploration of these ethical challenges—such as
patient consent, algorithmic bias, and the transparency of AI models—would be beneficial.
-The discussion summarizes existing studies without including new primary data, experimental
results, or original insights. While this is acceptable in a systematic review, including more novel
perspectives or recommendations for improving AI applications in GI cancer immunotherapy
would provide greater value to the discussion.
-The review uses terms like "multi-omics data," "immune cell infiltration," and "AI-based
models" without consistently defining these terms for a broader audience. A brief explanation
or reference to how these concepts are defined and measured would ensure clarity for readers
who may not be familiar with the specialized terminology.
assistant