Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images.

ABSTRACT: Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor recurrence at 5 years post-surgery. We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved concordance indices of 0.724 and 0.683 on the internal and external test cohorts, respectively, exceeding the performance of the standard Tumor-Node-Metastasis classification system.

SUBMITTER: Yamashita R 

PROVIDER: S-EPMC7820423 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature