The use of advanced machine learning to predict outcomes after atezolizumab plus bevacizumab for advanced hepatocellular carcinoma: a retrospective cohort study

BACKGROUND: Combination immune checkpoint inhibitors are recommended as first-line therapy for advanced hepatocellular carcinoma. However, only a third of patients respond to treatment, and improved approaches to predict response are required. Using baseline clinical data, we aimed to use advanced machine learning models to predict overall survival and progression-free survival in patients with advanced hepatocellular carcinoma receiving atezolizumab plus bevacizumab. METHODS: This retrospective cohort study was conducted at 24 centres across eight countries. Patients aged 18 years and older with a histological or radiological diagnosis of advanced hepatocellular carcinoma were included; those who had received previous systemic therapy for hepatocellular carcinoma were excluded. All patients received intravenous atezolizumab 1200 mg plus bevacizumab 15 mg/kg once every 3 weeks until disease progression. Seven supervised machine learning models, in combination with 13 feature selection techniques, were trained on 44 baseline clinical variables for the prediction of overall survival and progression-free survival. The three best-performing models, combined with their optimum feature selection techniques, were used to develop ensemble machine learning models for the prediction of overall survival and progression-free survival. The primary outcomes of the study were the predictions of overall survival, progression-free survival, and immunotherapy response using advanced machine learning. k-means clustering was used to stratify patients into two groups: those at low risk and those at high risk of either death (in the overall survival model) or disease progression (in the progression-free survival model). FINDINGS: 934 patients who received immunotherapy from May 1, 2018 and were followed up until Oct 1, 2023 were screened, of whom 160 were excluded and 774 were included in the final study. Patients were divided into training (n=339), internal validation (n=146) and external validation (n=289) cohorts. Support vector machine, neural network, and naive Bayes algorithms had the best performance in the prediction of overall survival; for progression-free survival, the highest-performing algorithms were ridge regression, naive Bayes, and logistic regression. In the external validation cohort, the ensemble model for the prediction of overall survival (area under the receiver operating characteristic curve 0·75 [95% CI 0·69-0·81]) significantly outperformed all eight of the tested clinical benchmark variables: Barcelona Clinic Liver Cancer (BCLC) stage (0·54 [0·48-0·61]; p