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: 04 Nov 2024
Reviewer's report:
This retrospective study investigated a CT-based radiomics model to predict PFS of unresectable
hepatocellular carcinoma treated with TACE±HAIC combined with PD-1 Inhibitors and
tyrosine kinase inhibitors. The results are solid and support the conclusion. The authors have
addressed most of my concerns, and 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: 04 Nov 2024
Reviewer's report:
This retrospective study investigated a CT-based radiomics model to predict PFS of unresectable
hepatocellular carcinoma treated with TACE±HAIC combined with PD-1 Inhibitors and
tyrosine kinase inhibitors. The results are solid and support the conclusion. The authors have
addressed most of my concerns, and 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: 04 Nov 2024
Reviewer's report:
This retrospective study investigated a CT-based radiomics model to predict PFS of unresectable
hepatocellular carcinoma treated with TACE±HAIC combined with PD-1 Inhibitors and
tyrosine kinase inhibitors. The results are solid and support the conclusion. The authors have
addressed most of my concerns, and I would recommend it for publication.
assistant