Vision transformer-based model can optimize curative-intent treatment for patients with recurrent hepatocellular carcinoma.

Abstract

The treatment selection for recurrent hepatocellular carcinoma (rHCC) within Milan criteria after hepatectomy remains challenging. Here, we present HEROVision, a Vision Transformer-based model designed for personalized prognosis prediction and treatment optimization between thermal ablation (TA) and surgical resection (SR). HEROVision is trained on initial HCC cohorts (8492 images; 772 patients) and independently tested on rHCC cohorts (9163 images; 833 patients) from five centers. Propensity score matching (PSM) forms two groups of rHCC patients underwent TA and SR to fairly evaluate whether optimized treatment selection by HEROVision have clinical benefits. HEROVision significantly outperforms all six guideline staging systems in the external testing cohort, both in time-dependent concordance index and area under the curve (all P < 0.002). After PSM, 35.9% (23/64) and 6.6% (6/91) high-risk rHCC patients are identified, who could achieve improved prognosis by changing their treatments. HEROVision shows promise in optimizing individualized treatment between TA and SR for early-stage rHCC, complementing current clinical guidelines.