Evaluate the predictive performance of the model: ROC analysis of models on rad signature in pre-treatment US model (A) and post-treatment US model (B); Decision curve of in train and test cohort in pre-treatment US model (C and D) and post-treatment US (E and F).

<p>Evaluate the predictive performance of the model: ROC analysis of models on rad signature in pre-treatment US model (A) and post-treatment US model (B); Decision curve of in train and test cohort in pre-treatment US model (C and D) and post-treatment US (E and F).</p>

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Bibliographic Details
Main Author:	Yiran Li (1818355) (author)
Other Authors:	Zonghan Liu (12521164) (author), Yi Qian (466487) (author), Kang Wang (415359) (author), Yijun Gu (16620079) (author), Yan Chen (4308) (author), Haozheng Jiang (20666265) (author), Shuqun Cheng (151531) (author), Dong Jiang (159266) (author)
Published:	2025
Subjects:	Biochemistry Medicine Genetics Molecular Biology Pharmacology Biotechnology Marine Biology Cancer Infectious Diseases Plant Biology Virology unresectable hepatocellular carcinoma combined targeted therapy atezolizumab plus bevacizumab separate ur models predict treatment efficacy xlink "> validation group two groups train group therapeutic efficacy radiomics models particularly highlighting model performance effective treatment cutoff value assessment methods
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Evaluate the predictive performance of the model: ROC analysis of models on rad signature in pre-treatment US model (A) and post-treatment US model (B); Decision curve of in train and test cohort in pre-treatment US model (C and D) and post-treatment US (E and F).

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