Knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer.

ABSTRACT

PURPOSE: To implement a knowledge-based (KB) optimization strategy to our adaptive (ART) early-regression guided boosting technique in neo-adjuvant radio-chemotherapy for rectal cancer. MATERIAL AND METHODS: The protocol consists of a first phase delivering 27.6 Gy to tumor/lymph-nodes (2.3 Gy/fr-PTV1), followed by the ART phase concomitantly delivering 18.6 Gy (3.1 Gy/fr) and 13.8 Gy (2.3 Gy/fr) to the residual tumor (PTVART) and to PTV1 respectively. PTVART is obtained by expanding the residual GTV, as visible on MRI at fraction 9. Forty plans were used to generate a KB-model for the first phase using the RapidPlan tool. Instead of building a new model, a robust strategy scaling the KB-model to the ART phase was applied. Both internal and external validation were performed for both phases all automatic plans (RP) were compared in terms of OARs/PTVs parameters against the original plans (RA). RESULTS: The resulting automatic plans were generally better than or equivalent to clinical plans. Of note, V30Gy and V40Gy were significantly improved in RP plans for bladder and bowel; gEUD analysis showed improvement for KB-modality for all OARs, up to 3 Gy for the bowel. CONCLUSIONS: The KB-model generated for the first phase was robust and it was also efficiently adapted to the ART phase. The performance of automatically generated plans were slightly better than the corresponding manual plans for both phases.