Improving Quality and Consistency in NRG Oncology Radiation Therapy Oncology Group 0631 for Spine Radiosurgery via Knowledge-Based Planning

Purpose: To use knowledge-based planning (KBP) as a method of producing highquality, consistent, protocol-compliant treatment plans in a complex setting of spine stereotactic body radiation therapy on NRG Oncology Radiation Therapy Oncology Group (RTOG) 0631. Methods and Materials: An internally developed KBP model was applied to an external validation cohort of 22 anonymized cases submitted under NRG Oncology RTOG 0631. The original and KBP plans were compared via their protocol compliance, target conformity and gradient index, dose to critical structures, and dose to surrounding normal tissues. Results: The KBP model generated plans meeting all protocol objectives in a single optimization when tested on both internal and protocol-submitted NRG Oncology RTOG 0631 cases. Two submitted plans that were considered to have a protocolunacceptable deviation were made protocol compliant through the use of the model. There were no statistically significant differences in protocol spinal cord metrics (D10% and D0.03cc) between the manually optimized plans and the KBP plans. The volume of planning target volume receiving prescription dose increased from 93.3% +/- 3.2% to 98.3% +/- 1.4% (P Z.01) when using KBP. High-dose spillage to surrounding normal tissues (V105%) showed no significant differences (2.1 +/- 7.3 cm(3) for manual plans to 1.8 +/- 0.6 cm(3) with KBP), and dosimetric outliers with large amounts of spillage were eliminated through the use of KBP. Knowledgebased planning plans were also found to be significantly more consistent in several metrics, including target coverage and high dose outside of the target. Conclusion: Incorporation of KBP models into the clinical trial setting may have a profound impact on the quality of trial results, owing to the increase in consistency and standardization of planning, especially for treatment sites or techniques that are nonstandard. (C) 2017 Elsevier Inc. All rights reserved.