LEAD-ONC: A Platform for Reconstructing Survival Data From Trial Reports for Targeted Evidence Synthesis and Clinical...

Clinical trial design in oncology relies heavily on evidence synthesized from prior studies. However, relevant information, including Kaplan-Meier curves, baseline characteristics, and eligibility criteria, is typically embedded in figures, tables, and free text, making systematic extraction and quantitative synthesis challenging. We developed LEAD-ONC (Literature to Evidence for Analytics and Design in Oncology), a platform designed to transform published clinical trial reports into structured, analyzable data to support evidence-informed trial design.

LEAD-ONC integrates large language models and computer vision techniques to extract multimodal information from published oncology trials, including survival curves, risk tables, and baseline characteristics. Individual patient data are reconstructed from digitized Kaplan-Meier curves, whereas baseline covariates are extracted and harmonized at the trial level across studies. The platform incorporates cross-trial similarity assessment to identify clinically comparable studies and applies Bayesian hierarchical survival modeling to generate predictive survival distributions for future trials.

We demonstrate the utility of LEAD-ONC using a case study in metastatic non-small cell lung cancer. The platform successfully extracted and harmonized data from multiple phase III trials and generated predictive survival distributions for a target population defined by mixed histology. Model-based projections of median overall survival and treatment effect illustrate how the platform can support quantitative assumptions for trial design.

LEAD-ONC provides a scalable framework for systematic evidence synthesis from published literature. By enabling target population-specific survival projections, the platform supports more transparent and data-driven clinical trial design.

LEAD-ONC is available at: https://lead-onc.org/step1.