Purpose: To develop and validate a protocol-agnostic machine learning platform ("Predictive Planning") for knowledge-based planning (KBP) in external-beam radiotherapy. Materials and Methods: 5334 retrospective photon-beam treatment plans (1145 Head and Neck (H PP), 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 PTVs. Results: In the plan comparison study, there were no OAR dose metrics for which PP were statistically significantly greater than CP. For H&N, PP mean dose (Gy) was significantly lower for Brain (5. 8 vs. 7. 3, p=0. 0003), Brainstem (9. 6 vs. 13. 8, p<0. 0001), Glottis (26. 9 vs. 31. 9, p=0. 0017), OpticChiasm (7. 3 vs. 12. 2, p<0. 0001), ParotidL/R (19. 0/20. 8 vs. 22. 3/25. 7, p<0. 0001/<0. 0001) and SpinalCord (10. 1 vs. 15. 7, p<0. 0001). For Abdominal, PP mean dose was significantly lower for Heart (2. 2 vs. 3. 4, p=0. 0039), KidneyR (6. 2 vs. 8. 6, p=0. 002), SpinalCanal (4. 8 vs. 6. 7, p=0. 0004), and Stomach (9. 1 vs. 10. 9, p=0. 002). For Pelvis, PP mean dose was significantly lower for Bladder (30. 7 vs. 33. 7, p<0. 0001), FemurHeadL/R (13. 8/13. 9 vs. 17. 0/16. 5, p=0. 0026/0. 0046), PenileBulb (15. 8 vs. 21. 9, p=0. 0039), and Rectum (28. 1 vs. 34. 2, p=0. 0002). Mean PTV coverage was significantly higher for H&N and Thoracic, and equivalent for Abdominal and Pelvis. Conclusions: Predictive Planning represents a shift in KBP from protocol-specific models trained with small, uniform datasets to protocol-agnostic, disease-site-specific models trained with large, heterogenous datasets. Plans generated using optimization objectives predicted by the models had equivalent or superior dosimetric outcomes.
Shade et al. (Wed,) studied this question.