Machine learning methods for accurately predicting survival and guiding treatment in stage I and II hepatocellular carcinoma

Accurately predicting survival in patients with early hepatocellular carcinoma (HCC) is essential for making informed decisions about treatment and prognosis. Herein, we have developed a machine learning (ML) model that can predict patient survival and guide treatment decisions. We obtained patient demographic information, tumor characteristics, and treatment details from the SEER database. To analyze the data, we employed a Cox proportional hazards (CoxPH) model as well as 3 ML algorithms: neural network multitask logistic regression (N-MLTR), DeepSurv, and random survival forest (RSF). Our evaluation relied on the concordance index (C-index) and Integrated Brier Score (IBS). Additionally, we provided personalized treatment recommendations regarding surgery and chemotherapy choices and validated models’ efficacy. A total of 1136 patients with early-stage (I, II) hepatocellular carcinoma (HCC) who underwent liver resection or transplantation were randomly divided into training and validation cohorts at a ratio of 3:7. Feature selection was conducted using Cox regression analyses. The ML models (NMLTR: C-index = 0.6793; DeepSurv: C-index = 0.7028; RSF: C-index = 0.6890) showed better discrimination in predicting survival than the standard CoxPH model (C-index = 0.6696). Patients who received recommended treatments had higher survival rates than those who received unrecommended treatments. ML-based surgery treatment recommendations yielded higher hazard ratios (HRs): NMTLR HR = 0.36 (95% CI: 0.25–0.51, P < .001), DeepSurv HR = 0.34 (95% CI: 0.24–0.49, P < .001), and RSF HR = 0.37 (95% CI: 0.26–0.52, P = <.001). Chemotherapy treatment recommendations were associated with significantly improved survival for DeepSurv (HR: 0.57; 95% CI: 0.4–0.82, P = .002) and RSF (HR: 0.66; 95% CI: 0.46–0.94, P = .020). The ML survival model has the potential to benefit prognostic evaluation and treatment of HCC. This novel analytical approach could provide reliable information on individual survival and treatment recommendations.