Abstract 4221: CAPTYN, a six-variable machine-learning model predicting clinical benefit of atezolizumab-bevacizumab in hepatocellular carcinoma: Development and external validation in IMbrave150

Abstract Background: Current prognostic models for hepatocellular carcinoma (HCC) treated with atezolizumab plus bevacizumab (AB) rely on limited variables and lack prospective validation. We aimed to develop and externally validate a machine-learning model integrating multiple clinical variables to predict clinical benefit to first-line AB in advanced HCC. Methods and Results: This multicenter study included 637 patients with unresectable HCC from three hospitals and one phase III prospective trial (IMbrave150), grouped into four AB cohorts. The training set comprised patients from CHA Bundang Medical Center (Korea, n=301) and the Medical University of Vienna (Austria, n=53), while external validation used IMbrave150 (n=99) and Severance Hospital (Korea, n=184) cohort. Clinical benefit (CB) was defined as CR, PR, or SD with PFS ≥6 months by RECIST v1.1; all other cases were classified as non-clinical benefit (NCB). Among 14 candidate variables, nine were identified by univariable Cox regression for OS and PFS. A recursive elimination procedure maximizing five-fold cross-validated AUC for NCB classification identified six optimal predictors—CRP, AFP, platelet, total bilirubin, lymphocyte, and neutrophil—which were used to train an XGBoost classifier, termed CAPTYN. In the training set, CAPTYN achieved an AUC of 0.93. SHAP-based interpretation showed that elevated CRP, AFP, and total bilirubin and reduced lymphocyte counts contributed to NCB, whereas platelet and neutrophil counts exhibited U-shaped associations. In external validation, CAPTYN achieved AUCs of 0.70 (95% CI, 0.59-0.81) in IMbrave150 cohort and 0.67 (0.59-0.75) in Severance cohort, outperforming CRAFITY, ALBI, and CRAPT-M (DeLong's test p0.05). Calibration was acceptable (Brier score=0.22 and 0.24, respectively), and CAPTYN significantly stratified OS and PFS (IMbrave150 cohort: both p0.001; Severance cohort: p=0.012 for OS, p=0.009 for PFS), whereas comparator models failed to discriminate PFS. Subgroup analyses across demographics and disease features in IMbrave150 consistently showed higher hazard ratios (1.5) for OS and PFS in CAPTYN-predicted NCB patients. Conclusion: CAPTYN, a six-variable machine-learning model predicting CB to AB, was externally validated using a prospective trial and a real-world cohort, providing calibrated, interpretable probabilities that may inform individualized treatment decisions. Citation Format: Gae Hoon Jo, Sohyun Hwang, Bernhard Scheiner, Won Suk Lee, Beodeul Kang, Jung Sun Kim, Ho Yeong Lim, Chansik An, Dong Yun Kim, Inyoung Kim, Dong-hyuk Heo, Matthias Pinter, Beom Kyung Kim, Chan Kim, Hong Jae Chon. CAPTYN, a six-variable machine-learning model predicting clinical benefit of atezolizumab-bevacizumab in hepatocellular carcinoma: Development and external validation in IMbrave150 abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4221.