X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology

Multimodal explainable artificial intelligence (XAI) is transforming oncology, yet its clinical adoption is constrained by limited interpretability and fragmented use of real-world data (RWD), including electronic medical records, geospatial data, and social determinants of health. We present the XAI CAncer Radiation Therapy (X-CART) platform, a multimodal RWD pipeline designed to enable explainable, clinically actionable precision oncology.

X-CART integrates heterogeneous RWD into a unified, AI-ready architecture for explainable radiotherapy interruption (RTI) risk prediction and patient navigation. A retrospective proof-of-implementation analysis demonstrated the pipeline's predictive modeling and explainability. This framework supports targeted, patient-level intervention strategies.

We developed a scalable, interoperable pipeline that synthesizes clinical and community-level data into a secure multimodal framework for RTI risk prediction and patient navigation. The system employs iterative machine learning with feedback-driven retraining and delivers real-time, interpretable outputs through an electronic medical record-embedded dashboard. The architecture is registry-extensible and aligned with interoperability standards to support multi-institutional deployment. As proof of implementation of the Data Science module, a retrospective cohort analysis including 2525 patients identified 622 RTI events (24.6%) and demonstrated modest discrimination with eXtreme Gradient Boosting on an independent hold-out test set (area under the receiver operating characteristic curve = 0.689) with cohort-level SHapley Additive exPlanations interpretability.

The framework addresses critical RWD challenges, including data quality, completeness, and traceability. X-CART establishes a generalizable, privacy-preserving foundation for XAI in oncology and provides a practical blueprint for enriching cancer registries with multimodal RWD to support scalable, real-world evidence generation.