Highly efficient training, refinement, and validation of a knowledge-based plan quality control system for radiotherapy clinical trials
Abstract
Purpose To demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiotherapy clinical trial plan quality control (QC) system. Methods We analyzed 86 stage IB-IVA cervical cancer patients treated with intensity modulated radiotherapy (IMRT) at two institutions according to XXXXX multi-institutional protocol standards. The protocol utilized a planning target volume (PTV) and two primary organs-at-risk (OARs): pelvic bone marrow (PBM) and bowel. Secondary OARs were rectum and bladder. Initial UNFILTERED dose-volume histogram (DVH) estimation models were trained using all 86 plans. REFINED training sets and DVH-estimation models were constructed by identifying 30/86 plans emphasizing PBM-sparing (comparing protocol-specified V10 and V20 with UNFILTERED predictions), and another 30/86 plans emphasizing bowel sparing (comparing V40 and V45, 9 in common with PBM set). To obtain deliverable KBP plans, REFINED models must inform patient-specific optimization objectives/priorities (an auto-planning “routine”). Four candidate routines emphasizing different tradeoffs were composed and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine best meeting protocol objectives in the 51-patient training sample (KBPFINAL), protocol-specific DVH metrics and normal tissue complication probability (NTCP) were compared for original vs. KBPFINAL plans across the 35-patient validation…
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