Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 1 Date: 31 May 2025
Reviewer:  Tomoyuki Nagai
Reviewer's report:
We have responded to the reviewers’ comments in a sincere and constructive manner, resulting
in a marked improvement in the quality of the manuscript. In particular, the study presents
high publication value owing to its clinical applicability, the development of an explainable AI
model, and its clear differentiation from prior studies. Notably, the authors emphasize the use
of routinely available clinical parameters, which clearly distinguishes this study from previous
studies relying on radiomics or other complex data modalities. The advantages of interpretability
and feasibility in real-world clinical implementation have been well discussed. In addition, the
visual clarity of the figures was enhanced.
However, there remain a few areas in which further refinement could strengthen the manuscript.
Specifically, the clinical relevance of the selected 10 variables and the novelty of the SHAP-
based interpretation could be articulated more explicitly. The authors should elaborate on
how the machine learning model integrates and weights these variables, many of which
are consistent with existing clinical knowledge, to enhance the predictive performance.
Furthermore, consideration of nonlinear effects and potential interactions among variables in the
SHAP analysis would further highlight the interpretability of the model, which is a key strength
of this study.
Addressing these points would further enhance the scientific rigour and overall quality of the
manuscript.
Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 1 Date: 31 May 2025
Reviewer:  Tomoyuki Nagai
Reviewer's report:
We have responded to the reviewers’ comments in a sincere and constructive manner, resulting
in a marked improvement in the quality of the manuscript. In particular, the study presents
high publication value owing to its clinical applicability, the development of an explainable AI
model, and its clear differentiation from prior studies. Notably, the authors emphasize the use
of routinely available clinical parameters, which clearly distinguishes this study from previous
studies relying on radiomics or other complex data modalities. The advantages of interpretability
and feasibility in real-world clinical implementation have been well discussed. In addition, the
visual clarity of the figures was enhanced.
However, there remain a few areas in which further refinement could strengthen the manuscript.
Specifically, the clinical relevance of the selected 10 variables and the novelty of the SHAP-
based interpretation could be articulated more explicitly. The authors should elaborate on
how the machine learning model integrates and weights these variables, many of which
are consistent with existing clinical knowledge, to enhance the predictive performance.
Furthermore, consideration of nonlinear effects and potential interactions among variables in the
SHAP analysis would further highlight the interpretability of the model, which is a key strength
of this study.
Addressing these points would further enhance the scientific rigour and overall quality of the
manuscript.
Please review this paper.
user
Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 1 Date: 31 May 2025
Reviewer:  Tomoyuki Nagai
Reviewer's report:
We have responded to the reviewers’ comments in a sincere and constructive manner, resulting
in a marked improvement in the quality of the manuscript. In particular, the study presents
high publication value owing to its clinical applicability, the development of an explainable AI
model, and its clear differentiation from prior studies. Notably, the authors emphasize the use
of routinely available clinical parameters, which clearly distinguishes this study from previous
studies relying on radiomics or other complex data modalities. The advantages of interpretability
and feasibility in real-world clinical implementation have been well discussed. In addition, the
visual clarity of the figures was enhanced.
However, there remain a few areas in which further refinement could strengthen the manuscript.
Specifically, the clinical relevance of the selected 10 variables and the novelty of the SHAP-
based interpretation could be articulated more explicitly. The authors should elaborate on
how the machine learning model integrates and weights these variables, many of which
are consistent with existing clinical knowledge, to enhance the predictive performance.
Furthermore, consideration of nonlinear effects and potential interactions among variables in the
SHAP analysis would further highlight the interpretability of the model, which is a key strength
of this study.
Addressing these points would further enhance the scientific rigour and overall quality of the
manuscript.
assistant