Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning

Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time-consuming and expensive high-throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significant challenges in biology and clinical settings. Our aimed to develop a self-supervised attention-based multiple instance learning (SSL-ABMIL) model to predict TMB and VHL mutation status from hematoxylin and eosin-stained histopathological images.