Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 0 Date: 07 Sep 2024
Reviewer's report:
Comment on the manuscript by Zhang, et al.
This paper compared several machine learning algorithm for predicting TACE responder, and
found that CatBoost model is the most excellent for dividing patients undergoing TACE into
responders and nonresponders. SHAP method is applied for explication of the overall model
framework. Machine learning algorithm is one of the trend for developing clinical prediction
model, however, there are several concerns to be addressed.
1) Abstract should be rewritten.
Background; It is too long, and should be composed of 1 or 2 sentences.
Methods; This study is a retrospective, so it should be written based on STROBE statement. In
the criteria, patient number does not appear in Methods, but in Results.
Results; Data should be shown concretely and entirely. The current Results are very
nonobjective.
Conclusions; It should be reached from Results.
2) Introduction is redundant. Especially, description of TACE should be much simpler, or could
be removed.
3) The indication of TACE is nor described.
4) The number of TACE should be included in Table 1, which affects the survivals of patients
with HCC.
5) Machine learning algorithm can retrospectively cluster the patients undergoing TACE,
however, it can’t predict the prognosis of patients with HCC before TACE. This is my major
concern in this study. What is the clinical importance the hepatologists can obtain from this
study?
6) Despite of the complicated methods, the variables selected by machine learning algorithm is
quite commonplace (Fig. 6) and have been reported so many times. What is the novel finding
by this data?
Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 0 Date: 07 Sep 2024
Reviewer's report:
Comment on the manuscript by Zhang, et al.
This paper compared several machine learning algorithm for predicting TACE responder, and
found that CatBoost model is the most excellent for dividing patients undergoing TACE into
responders and nonresponders. SHAP method is applied for explication of the overall model
framework. Machine learning algorithm is one of the trend for developing clinical prediction
model, however, there are several concerns to be addressed.
1) Abstract should be rewritten.
Background; It is too long, and should be composed of 1 or 2 sentences.
Methods; This study is a retrospective, so it should be written based on STROBE statement. In
the criteria, patient number does not appear in Methods, but in Results.
Results; Data should be shown concretely and entirely. The current Results are very
nonobjective.
Conclusions; It should be reached from Results.
2) Introduction is redundant. Especially, description of TACE should be much simpler, or could
be removed.
3) The indication of TACE is nor described.
4) The number of TACE should be included in Table 1, which affects the survivals of patients
with HCC.
5) Machine learning algorithm can retrospectively cluster the patients undergoing TACE,
however, it can’t predict the prognosis of patients with HCC before TACE. This is my major
concern in this study. What is the clinical importance the hepatologists can obtain from this
study?
6) Despite of the complicated methods, the variables selected by machine learning algorithm is
quite commonplace (Fig. 6) and have been reported so many times. What is the novel finding
by this data?
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: 07 Sep 2024
Reviewer's report:
Comment on the manuscript by Zhang, et al.
This paper compared several machine learning algorithm for predicting TACE responder, and
found that CatBoost model is the most excellent for dividing patients undergoing TACE into
responders and nonresponders. SHAP method is applied for explication of the overall model
framework. Machine learning algorithm is one of the trend for developing clinical prediction
model, however, there are several concerns to be addressed.
1) Abstract should be rewritten.
Background; It is too long, and should be composed of 1 or 2 sentences.
Methods; This study is a retrospective, so it should be written based on STROBE statement. In
the criteria, patient number does not appear in Methods, but in Results.
Results; Data should be shown concretely and entirely. The current Results are very
nonobjective.
Conclusions; It should be reached from Results.
2) Introduction is redundant. Especially, description of TACE should be much simpler, or could
be removed.
3) The indication of TACE is nor described.
4) The number of TACE should be included in Table 1, which affects the survivals of patients
with HCC.
5) Machine learning algorithm can retrospectively cluster the patients undergoing TACE,
however, it can’t predict the prognosis of patients with HCC before TACE. This is my major
concern in this study. What is the clinical importance the hepatologists can obtain from this
study?
6) Despite of the complicated methods, the variables selected by machine learning algorithm is
quite commonplace (Fig. 6) and have been reported so many times. What is the novel finding
by this data?
assistant