Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 0 Date: 29 Apr 2025
Reviewer:  Tomoyuki Nagai
Reviewer's report:
Summary:
This manuscript presents an explainable machine learning (ML) model to predict the response
to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC),
using clinical parameters rather than radiomics features. The study is well-structured, including
internal and external validation cohorts. However, there are several major concerns regarding
the model’s generalizability, clinical applicability, and the need to emphasize the novelty more
explicitly. Overall, I recommend major revision before acceptance.
1. Comments on the Field
The development of predictive models for treatment response in HCC, including TACE and
systemic therapies, has progressed rapidly. Most prior studies have utilized radiomics or
imaging-derived data. In this context, constructing an ML model based on readily available
clinical parameters is a practical and commendable approach that enhances potential clinical
applicability.
2. Novelty
The use of explainable machine learning (SHAP analysis) to interpret the prediction model adds
a significant advantage over traditional “black-box” models. However, given that numerous
ML-based predictive models for HCC already exist, the authors should more clearly highlight
what differentiates their model—such as the clinical parameters used, patient population
characteristics, or specific strengths of their approach compared to previous studies.
3. Study Design and Scientific Rigor
The study adopts a standard ML development framework, including training, internal testing,
and external validation sets. Nevertheless, HCC is highly heterogeneous, influenced by
etiologies such as HBV, HCV, and MASH, which affect tumor biology and treatment response.
A cohort size of 382 patients, from a single region, may limit the model’s generalizability.
Moreover, the clinical implication of predicting TACE non-responders remains unclear. The
manuscript should discuss whether alternative therapeutic options (e.g., systemic therapy,
immunotherapy) could be recommended for predicted non-responders, and whether such
prediction could realistically lead to improved patient outcomes.
4. Language Quality and Formatting
The English writing is generally clear and comprehensible. However, some minor issues
such as subject-verb agreement errors, redundant expressions, inconsistent terminology (e.g.,
LightGBM vs lightGBM), and overly lengthy figure captions should be corrected to improve
overall readability and consistency.
Major Revisions
1. Generalizability Discussion:
Add a discussion on the potential limitations regarding the generalizability of the model due to
the relatively small sample size and the restricted etiological diversity (HBV, HCV, MASH) of
the enrolled patients.
2. Clinical Applicability Discussion:
Clearly discuss how the predictive model would impact clinical decision-making. For example,
what therapeutic strategies should be considered for patients predicted to be non-responders? Is
there evidence that predictive stratification can improve survival outcomes?
3. Stronger Emphasis on Novelty:
Explicitly differentiate the current model from existing ML-based prediction models for HCC,
emphasizing its practical advantages (e.g., clinical data use, explainability) and positioning
within the current literature.
Minor Revisions
1. Language Polishing:
Conduct a thorough English proofreading, addressing minor grammatical errors and enhancing
sentence clarity.
2. Consistent Terminology:
Ensure consistent use of terminology (e.g., LightGBM instead of lightGBM, Machine Learning
[ML]).
3. Simplify Figure Captions:
Shorten and clarify figure legends for better readability.
4. Conclusion Refinement:
In the Conclusion section, state cautiously that the model has potential clinical utility but requires
further validation before being implemented in practice.
5. Limitation Expansion:
Briefly mention the small sample size of the external validation cohort (n=52) as a limitation
in the Discussion.
Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 0 Date: 29 Apr 2025
Reviewer:  Tomoyuki Nagai
Reviewer's report:
Summary:
This manuscript presents an explainable machine learning (ML) model to predict the response
to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC),
using clinical parameters rather than radiomics features. The study is well-structured, including
internal and external validation cohorts. However, there are several major concerns regarding
the model’s generalizability, clinical applicability, and the need to emphasize the novelty more
explicitly. Overall, I recommend major revision before acceptance.
1. Comments on the Field
The development of predictive models for treatment response in HCC, including TACE and
systemic therapies, has progressed rapidly. Most prior studies have utilized radiomics or
imaging-derived data. In this context, constructing an ML model based on readily available
clinical parameters is a practical and commendable approach that enhances potential clinical
applicability.
2. Novelty
The use of explainable machine learning (SHAP analysis) to interpret the prediction model adds
a significant advantage over traditional “black-box” models. However, given that numerous
ML-based predictive models for HCC already exist, the authors should more clearly highlight
what differentiates their model—such as the clinical parameters used, patient population
characteristics, or specific strengths of their approach compared to previous studies.
3. Study Design and Scientific Rigor
The study adopts a standard ML development framework, including training, internal testing,
and external validation sets. Nevertheless, HCC is highly heterogeneous, influenced by
etiologies such as HBV, HCV, and MASH, which affect tumor biology and treatment response.
A cohort size of 382 patients, from a single region, may limit the model’s generalizability.
Moreover, the clinical implication of predicting TACE non-responders remains unclear. The
manuscript should discuss whether alternative therapeutic options (e.g., systemic therapy,
immunotherapy) could be recommended for predicted non-responders, and whether such
prediction could realistically lead to improved patient outcomes.
4. Language Quality and Formatting
The English writing is generally clear and comprehensible. However, some minor issues
such as subject-verb agreement errors, redundant expressions, inconsistent terminology (e.g.,
LightGBM vs lightGBM), and overly lengthy figure captions should be corrected to improve
overall readability and consistency.
Major Revisions
1. Generalizability Discussion:
Add a discussion on the potential limitations regarding the generalizability of the model due to
the relatively small sample size and the restricted etiological diversity (HBV, HCV, MASH) of
the enrolled patients.
2. Clinical Applicability Discussion:
Clearly discuss how the predictive model would impact clinical decision-making. For example,
what therapeutic strategies should be considered for patients predicted to be non-responders? Is
there evidence that predictive stratification can improve survival outcomes?
3. Stronger Emphasis on Novelty:
Explicitly differentiate the current model from existing ML-based prediction models for HCC,
emphasizing its practical advantages (e.g., clinical data use, explainability) and positioning
within the current literature.
Minor Revisions
1. Language Polishing:
Conduct a thorough English proofreading, addressing minor grammatical errors and enhancing
sentence clarity.
2. Consistent Terminology:
Ensure consistent use of terminology (e.g., LightGBM instead of lightGBM, Machine Learning
[ML]).
3. Simplify Figure Captions:
Shorten and clarify figure legends for better readability.
4. Conclusion Refinement:
In the Conclusion section, state cautiously that the model has potential clinical utility but requires
further validation before being implemented in practice.
5. Limitation Expansion:
Briefly mention the small sample size of the external validation cohort (n=52) as a limitation
in the Discussion.
Please review this paper.
user
Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 0 Date: 29 Apr 2025
Reviewer:  Tomoyuki Nagai
Reviewer's report:
Summary:
This manuscript presents an explainable machine learning (ML) model to predict the response
to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC),
using clinical parameters rather than radiomics features. The study is well-structured, including
internal and external validation cohorts. However, there are several major concerns regarding
the model’s generalizability, clinical applicability, and the need to emphasize the novelty more
explicitly. Overall, I recommend major revision before acceptance.
1. Comments on the Field
The development of predictive models for treatment response in HCC, including TACE and
systemic therapies, has progressed rapidly. Most prior studies have utilized radiomics or
imaging-derived data. In this context, constructing an ML model based on readily available
clinical parameters is a practical and commendable approach that enhances potential clinical
applicability.
2. Novelty
The use of explainable machine learning (SHAP analysis) to interpret the prediction model adds
a significant advantage over traditional “black-box” models. However, given that numerous
ML-based predictive models for HCC already exist, the authors should more clearly highlight
what differentiates their model—such as the clinical parameters used, patient population
characteristics, or specific strengths of their approach compared to previous studies.
3. Study Design and Scientific Rigor
The study adopts a standard ML development framework, including training, internal testing,
and external validation sets. Nevertheless, HCC is highly heterogeneous, influenced by
etiologies such as HBV, HCV, and MASH, which affect tumor biology and treatment response.
A cohort size of 382 patients, from a single region, may limit the model’s generalizability.
Moreover, the clinical implication of predicting TACE non-responders remains unclear. The
manuscript should discuss whether alternative therapeutic options (e.g., systemic therapy,
immunotherapy) could be recommended for predicted non-responders, and whether such
prediction could realistically lead to improved patient outcomes.
4. Language Quality and Formatting
The English writing is generally clear and comprehensible. However, some minor issues
such as subject-verb agreement errors, redundant expressions, inconsistent terminology (e.g.,
LightGBM vs lightGBM), and overly lengthy figure captions should be corrected to improve
overall readability and consistency.
Major Revisions
1. Generalizability Discussion:
Add a discussion on the potential limitations regarding the generalizability of the model due to
the relatively small sample size and the restricted etiological diversity (HBV, HCV, MASH) of
the enrolled patients.
2. Clinical Applicability Discussion:
Clearly discuss how the predictive model would impact clinical decision-making. For example,
what therapeutic strategies should be considered for patients predicted to be non-responders? Is
there evidence that predictive stratification can improve survival outcomes?
3. Stronger Emphasis on Novelty:
Explicitly differentiate the current model from existing ML-based prediction models for HCC,
emphasizing its practical advantages (e.g., clinical data use, explainability) and positioning
within the current literature.
Minor Revisions
1. Language Polishing:
Conduct a thorough English proofreading, addressing minor grammatical errors and enhancing
sentence clarity.
2. Consistent Terminology:
Ensure consistent use of terminology (e.g., LightGBM instead of lightGBM, Machine Learning
[ML]).
3. Simplify Figure Captions:
Shorten and clarify figure legends for better readability.
4. Conclusion Refinement:
In the Conclusion section, state cautiously that the model has potential clinical utility but requires
further validation before being implemented in practice.
5. Limitation Expansion:
Briefly mention the small sample size of the external validation cohort (n=52) as a limitation
in the Discussion.
assistant