Automated Closed- and Open-Loop Validation of Knowledge-Based Planning Routines Across Multiple Disease Sites

Abstract

Purpose: Knowledge-based planning (KBP) clinical implementation necessitates significant upfront effort, even within a single disease site. The purpose of this study was to demonstrate an efficient method for clinicians to assess the noninferiority of KBP across multiple disease sites and estimate any systematic dosimetric differences after implementation. We sought to establish these endpoints in a plurality of previously treated patients (validation set) with both closed-loop (training set overlapping validation set) and open-loop (independent training set) KBP routines. Methods and Materials: We identified 53 prostate, 24 prostatic fossa, 54 hypofractionated lung, and 52 head and neck patients treated with volumetric modulated arc therapy in the year directly preceding our clinic's broad adoption of RapidPlan (Varian Medical Systems, Palo Alto, CA). Using the Varian Eclipse Scripting API, our program takes as input a list of patients, then performs semiautomated structure matching, fully automated RapidPlan-driven optimization, and plan comparison. All plans were normalized to the planning target volume (PTV) D95% = 100%. Dose metric differences (ΔDx = Dx,clinical − Dx,KBP) were computed for standard PTV and organ-at-risk (OAR) dose-volume histogram parameters across disease sites. A 2-tailed paired t test quantified statistical significance (P