Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 0 Date: 06 May 2025
Reviewer's report:
This study presents an interesting approach to predicting TACE response and identifying
subtypes in HCC patients through machine learning; however, there are some issues that need
to be addressed before the conclusions can be fully supported.
#In this study, while existing predictive scores such as HAP, ALBI, and up-to-seven are
incorporated as features, there is insufficient direct comparison of predictive performance
between the developed machine learning models and these existing scores. Please add this
comparison to the main text.
#Regarding the limitations of this retrospective study, there is inadequate discussion of selection
bias and data collection bias, and how these might affect the generalizability of the results. Please
elaborate on these points in the limitations section.
Title: Explainable Machine Learning Model for Predicting the Transarterial
Chemoembolization Response and Subtypes of Hepatocellular Carcinoma Patients
Version: 0 Date: 06 May 2025
Reviewer's report:
This study presents an interesting approach to predicting TACE response and identifying
subtypes in HCC patients through machine learning; however, there are some issues that need
to be addressed before the conclusions can be fully supported.
#In this study, while existing predictive scores such as HAP, ALBI, and up-to-seven are
incorporated as features, there is insufficient direct comparison of predictive performance
between the developed machine learning models and these existing scores. Please add this
comparison to the main text.
#Regarding the limitations of this retrospective study, there is inadequate discussion of selection
bias and data collection bias, and how these might affect the generalizability of the results. Please
elaborate on these points in the limitations section.
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: 06 May 2025
Reviewer's report:
This study presents an interesting approach to predicting TACE response and identifying
subtypes in HCC patients through machine learning; however, there are some issues that need
to be addressed before the conclusions can be fully supported.
#In this study, while existing predictive scores such as HAP, ALBI, and up-to-seven are
incorporated as features, there is insufficient direct comparison of predictive performance
between the developed machine learning models and these existing scores. Please add this
comparison to the main text.
#Regarding the limitations of this retrospective study, there is inadequate discussion of selection
bias and data collection bias, and how these might affect the generalizability of the results. Please
elaborate on these points in the limitations section.
assistant