Development and Clinical Validation of a Protocol-Agnostic Machine Learning Platform for Automated Treatment Planning...

To develop and validate a protocol-agnostic machine learning platform ("Predictive Planning") for knowledge-based planning (KBP) in external beam radiation therapy.

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.

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, P = .0003), Brainstem (9.6 vs 13.8, P < .0001), Glottis (26.9 vs 31.9, P = .0017), OpticChiasm (7.3 vs 12.2, P < .0001), Parotid_L/R (19.0/20.8 vs 22.3/25.7, P < .0001/<.0001) and SpinalCord (10.1 vs 15.7, P < .0001). For Abdominal, PP mean dose was significantly lower for Heart (2.2 vs 3.4, P = .0039), Kidney_R (6.2 vs 8.6, P = .002), SpinalCanal (4.8 vs 6.7, P = .0004), and Stomach (9.1 vs 10.9, P = .002). For Pelvis, PP mean dose was significantly lower for Bladder (30.7 vs 33.7, P < .0001), Femur_Head_L/R (13.8/13.9 vs 17.0/16.5, P = .0026/.0046), PenileBulb (15.8 vs 21.9, P = .0039), and Rectum (28.1 vs 34.2, P = .0002). Mean planning target volume coverage was significantly higher for H&N and Thoracic, and equivalent for Abdominal and Pelvis.

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.