AI-Guided First-Line Immunotherapy Selection in Unresectable Hepatocellular Carcinoma

This pragmatic, multicenter, cluster-randomized trial will evaluate whether a locked artificial intelligence (AI) clinical decision-support system can improve outcomes by helping multidisciplinary teams select first-line immune checkpoint inhibitor (ICI)-based systemic treatment for adults with unresectable hepatocellular carcinoma (HCC). Twenty-six hospitals or independent HCC multidisciplinary teams will be randomly assigned in a 1:1 ratio to AI-assisted treatment selection or usual-care treatment selection. Approximately 1,800 participants will be enrolled. Eligible participants must already be considered suitable for first-line ICI-based systemic therapy; the study does not compare immunotherapy with no immunotherapy. At AI-assisted sites, the system will use prespecified pretreatment information to estimate and compare expected outcomes across clinically appropriate, locally available, guideline-concordant ICI-based regimens. The AI output is advisory. Treating clinicians and patients retain responsibility for the final treatment decision, and reasons for not following an AI recommendation will be recorded. At usual-care sites, treatment will be selected through the standard multidisciplinary decision-making process without access to the AI output. Both groups will receive approved standard-of-care treatments. The AI model, input definitions, preprocessing pipeline, decision rules, thresholds, and software version will be locked before enrollment of the first participant and will not be retrained or modified using trial outcome data. Both groups will use the same eligibility criteria, patient-registration time point, imaging schedule, follow-up schedule, and outcome definitions. The primary outcome is progression-free survival assessed by blinded independent central imaging review. Overall survival is a key secondary outcome. Additional outcomes include tumor response, duration of response, safety, quality of life, treatment delivery, and implementation mea