ArticleAdvances in radiation oncology2026
Development and Clinical Validation of a Protocol-Agnostic Machine Learning Platform for Automated Treatment Planning in External Beam Radiation Therapy.
Article in Advances in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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15 authors.
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Abstract
Purpose: To develop and validate a protocol-agnostic machine learning platform ("Predictive Planning") for knowledge-based planning (KBP) in external beam radiation therapy. Methods and Materials: Five thousand three hundred thirty-four retrospective photon beam treatment plans (1145 Head and Neck [H&N], 1623 Thoracic, 781 Abdominal, 1785 Pelvis) from 2 institutions were used to develop and internally test general-purpose models for organ at risk (OAR) dose-volume histogram prediction. Models were deployed in a commercial system (Plan AI, Sun Nuclear Corporation), and 72 retrospective clinical plans (18 H&N, 20 Thoracic, 17 Abdominal, 17 Pelvis) were replanned using treatment planning system optimization objectives predicted by the models ("Predictive Plans"; PPs), without objective value changes. Dose metrics were compared for 25, 11, 8, and 7 OARs for H&N, Thoracic, Abdominal, and Pelvis plans, respectively, and for planning target volumes. Results: In the plan comparison study, there were no OAR dose metrics for which PPs were statistically significantly greater than clinical plans. For H&N, PP mean dose (Gy) was significantly lower for Brain (5.8 vs 7.3, Conclusions: Predictive planning represents a shift in KBP from protocol-specific models trained with small, uniform data sets to protocol-agnostic, disease-site-specific models trained with large, heterogeneous data sets. Plans generated using optimization objectives predicted by the models had equivalent or superior dosimetric outcomes.
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