What question did this study set out to answer?

To investigate the effectiveness of Rad-ITF scores in classifying intratumoral fibrosis and its association with angiogenesis in hepatocellular carcinoma.

May 14, 2026Radiology0 citations

CT Radiomics–based Machine Learning to Identify Intratumoral Fibrosis and Underlying Angiogenesis in Hepatocellular Carcinoma

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TWTian-Cheng WangCentral South University NWNan WeiHeidelberg University YBYan BaoCentral South University

Key Points

To investigate the effectiveness of Rad-ITF scores in classifying intratumoral fibrosis and its association with angiogenesis in hepatocellular carcinoma.
Utilized CT radiomics and machine learning algorithms to analyze images.
Correlated Rad-ITF scores with angiogenesis and prognosis in patients undergoing TACE.
Rad-ITF scores effectively classified intratumoral fibrosis grades.
Scores showed significant correlation with angiogenesis in both TCGA test and genomic test cohorts.

Abstract

< .001). High Rad-ITF scores correlated with increased angiogenesis in the TCGA test and genomic test cohorts. Conclusion The Rad-ITF score effectively classified ITF grade and its underlying angiogenesis in HCC and stratified prognosis in patients undergoing TACE. © RSNA, 2026

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CT Radiomics–based Machine Learning to Identify Intratumoral Fibrosis and Underlying Angiogenesis in Hepatocellular Carcinoma

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Abstract

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