Multimodal multiphasic preoperative image-based deep-learning predicts HCC outcomes after curative surgery.
Authors
Affiliations (12)
Affiliations (12)
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong.
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong.
- Department of Medicine, Division of Gastroenterology and Hepatology, Taipei Veterans General Hospital, Taipei, Taiwan.
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong.
- State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong.
- Department of Surgery, School of Clinical Medicine, The University of Hong Kong, Hong Kong.
- Department of Surgery, Queen Elizabeth Hospital, Hong Kong.
- Department of Surgery, Tuen Mun Hospital, Hong Kong.
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong.
- Healthcare and Services Center, Taipei Veterans General Hospital, Taipei, Taiwan.
- Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
Abstract
HCC recurrence frequently occurs after curative surgery. Histological microvascular invasion (MVI) predicts recurrence but cannot provide preoperative prognostication, whereas clinical prediction scores have variable performances. Recurr-NET, a multimodal multiphasic residual-network random survival forest deep-learning model incorporating preoperative CT and clinical parameters, was developed to predict HCC recurrence. Preoperative triphasic CT scans were retrieved from patients with resected histology-confirmed HCC from 4 centers in Hong Kong (internal cohort). The internal cohort was randomly divided in an 8:2 ratio into training and internal validation. External testing was performed in an independent cohort from Taiwan.Among 1231 patients (age 62.4y, 83.1% male, 86.8% viral hepatitis, and median follow-up 65.1mo), cumulative HCC recurrence rates at years 2 and 5 were 41.8% and 56.4%, respectively. Recurr-NET achieved excellent accuracy in predicting recurrence from years 1 to 5 (internal cohort AUROC 0.770-0.857; external AUROC 0.758-0.798), significantly outperforming MVI (internal AUROC 0.518-0.590; external AUROC 0.557-0.615) and multiple clinical risk scores (ERASL-PRE, ERASL-POST, DFT, and Shim scores) (internal AUROC 0.523-0.587, external AUROC: 0.524-0.620), respectively (all p < 0.001). Recurr-NET was superior to MVI in stratifying recurrence risks at year 2 (internal: 72.5% vs. 50.0% in MVI; external: 65.3% vs. 46.6% in MVI) and year 5 (internal: 86.4% vs. 62.5% in MVI; external: 81.4% vs. 63.8% in MVI) (all p < 0.001). Recurr-NET was also superior to MVI in stratifying liver-related and all-cause mortality (all p < 0.001). The performance of Recurr-NET remained robust in subgroup analyses. Recurr-NET accurately predicted HCC recurrence, outperforming MVI and clinical prediction scores, highlighting its potential in preoperative prognostication.