Deep-learning (DL) radiopathomics models can help predict the presence of vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) — a tumor form linked to poor patient outcomes, according to a study published March 14 in Radiology: Imaging Cancer.
The models can also assess the risk for early recurrence and progression-free survival, noted a team led by Yixing Yu, MD, of the First Affiliated Hospital of Soochow University in Suzhou, China.
“VETC is a novel vascular pattern of HCC associated with poor prognosis and benefit of sorafenib treatment,” the group wrote. “In this study, we developed and validated DL radiomics and pathomics models for predicting [it].”
HCC is the third leading cause of cancer death around the world, the investigators wrote, and despite advances in diagnosis and…