AI-Augmented Clinical Decision Support in a Patient-Centric Precision Oncology Registry

Purpose: xDECIDE is a clinical decision support system, accessed through a web portal and powered by a “Human-AI Team,” that offers oncology health care providers with treatment options personalized for their cancer patients and outcomes tracking through an observational research protocol. This article describes the xDECIDE process and the artificial intelligence (AI)-assisted technologies that ingest electronic medical records, generate a structured personal health record, standardize clinico-genomic features, and produce ranked treatment options based on clinical evidence, expert insights, and real-world outcomes generated by the system itself. Methods: Patients enroll directly into the IRB-approved pan-cancer XCELSIOR registry (NCT03793088). Patient consent permits data aggregation, continuous learning from clinical outcomes, and sharing of limited datasets for research. xDECIDE aggregates and processes medical records with natural language processing and machine learning to generate a structured care summary with a standardized list of patient features. These features are utilized by an ensemble of AI-based models called xCORE (xCures Option Ranking Engine) to create a ranked list of treatment options. The output of xCORE is reviewed by molecular pharmacologists and oncologists in a virtual tumor board (VTB) setting to create a report of treatment options and supporting rationales, individualized to the patient. Treating physicians use an interactive portal to view these data and reports and to continuously monitor their patients' information. Results: At the time of writing, over 7000 patients have enrolled in XCELSIOR, including over 1000 with central nervous system cancers, over 800 with pancreatic cancer, and over 300 patients each with lung, breast, and colorectal cancers. Over 350 VTBs have been performed for patients across indications including glioma, pancreatic cancer, and fibrolamellar carcinoma, with >450 therapeutic options discussed and over 2000 consensus rationales delivered. Over 500 treatment rationale statements (“rules”) have been encoded to reference discrete patient features to improve algorithm decision-making. Conclusion: xDECIDE clinical decision support can aid oncologists in their practice of medicine. The system identifies potentially effective treatment options individualized for each patient from the integration of real-world evidence, human expert knowledge and opinion, and scientific and clinical publications and databases, and offers a platform to learn from the experience of every patient.