Background: Given its heterogeneity and diverse clinical outcomes, precise subclassification of BCLC-C hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment.
Methods: We recruited 2,626 patients with BCLC-C stage HCC from multiple centers, comprising training/test (n=1,693) and validation cohorts (n=933). The XGBoost was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-/intermediate-/high-/very high-risk subgroups which were based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C).
Results: The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-/12-/24-month survival of patients with BCLC-C were 0.800/0.831/0.715, respectively-significantly higher than those of the conventional models, which was consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKIs) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab-bevacizumab as the best therapy particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high-to-very high-risk subgroup.
Conclusions: ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.