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Updated: Jul 5, 2025

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
Published on: October 6, 2023
Towards an Explainable AI Platform to Study Interruptions in Cancer Radiation Therapy
Arash Shaban-Nejad1, Nariman Ammar2, Fekede Kumsa1
1University of Tennessee Health Science Center - Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, Tennessee, USA.
Abstract:
Radiation therapy interruptions drive cancer treatment failures; they represent an untapped opportunity for improving outcomes and narrowing treatment disparities. This research reports on the early development of the X-CART platform, which uses explainable AI to model cancer treatment outcome metrics based on high-dimensional associations with our local social determinants of health dataset to identify and explain causal pathways linking social disadvantage with increased radiation therapy interruptions.

