Title: Deep Learning-based CT Radiomics Predicts Prognosis of Unresectable Hepatocellular
Carcinoma Treated with TACE-HAIC combined with PD-1 Inhibitors and Tyrosine Kinase
Inhibitors
Version: 3 Date: 31 Oct 2024
Reviewer's report:
This study presents a deep learning-based radiomics model for predicting treatment response
and PFS in unresectable HCC patients receiving TACE-HAIC combined with TKIs and PD-1
inhibitors. The retrospective analysis includes 172 patients, demonstrating the model's potential
with a median PFS of 14.0 months and OS of 26.2 months. The ResNet50 + MLP architecture
shows promising predictive performance. However, the study is limited by its retrospective
design and small sample size, reducing reliability. A prospective, multicenter study with a
larger sample is needed for validation. Overall, the author has answered the reviewer's questions
reasonably, with good reasoning and explanation, and the study provides a valuable tool
for clinical decision-making, warranting further investigation.  I would recommend it for
publication.
Title: Deep Learning-based CT Radiomics Predicts Prognosis of Unresectable Hepatocellular
Carcinoma Treated with TACE-HAIC combined with PD-1 Inhibitors and Tyrosine Kinase
Inhibitors
Version: 3 Date: 31 Oct 2024
Reviewer's report:
This study presents a deep learning-based radiomics model for predicting treatment response
and PFS in unresectable HCC patients receiving TACE-HAIC combined with TKIs and PD-1
inhibitors. The retrospective analysis includes 172 patients, demonstrating the model's potential
with a median PFS of 14.0 months and OS of 26.2 months. The ResNet50 + MLP architecture
shows promising predictive performance. However, the study is limited by its retrospective
design and small sample size, reducing reliability. A prospective, multicenter study with a
larger sample is needed for validation. Overall, the author has answered the reviewer's questions
reasonably, with good reasoning and explanation, and the study provides a valuable tool
for clinical decision-making, warranting further investigation.  I would recommend it for
publication.
Please review this paper.
user
Title: Deep Learning-based CT Radiomics Predicts Prognosis of Unresectable Hepatocellular
Carcinoma Treated with TACE-HAIC combined with PD-1 Inhibitors and Tyrosine Kinase
Inhibitors
Version: 3 Date: 31 Oct 2024
Reviewer's report:
This study presents a deep learning-based radiomics model for predicting treatment response
and PFS in unresectable HCC patients receiving TACE-HAIC combined with TKIs and PD-1
inhibitors. The retrospective analysis includes 172 patients, demonstrating the model's potential
with a median PFS of 14.0 months and OS of 26.2 months. The ResNet50 + MLP architecture
shows promising predictive performance. However, the study is limited by its retrospective
design and small sample size, reducing reliability. A prospective, multicenter study with a
larger sample is needed for validation. Overall, the author has answered the reviewer's questions
reasonably, with good reasoning and explanation, and the study provides a valuable tool
for clinical decision-making, warranting further investigation.  I would recommend it for
publication.
assistant