Multi-institutional Validation of a Knowledge-based Planning Model for Patients Enrolled on RTOG 0617: Implications for Plan Quality Controls in Cooperative Group Trials

• Type of Material: Article
• Authors: James A. Kavanaugh, Washington University in St. Louis; Sarah Holler, Bucknell University; Todd A. DeWees, Washington University in St. Louis; Clifford G. Robinson, Washington University in St. Louis; Jeffey D. Bradley, Washington University in St. Louis; Puneeth Iyengar, University of Texas Southwestern; Kristin Higgins, Emory University; Sasa Mutic, Washington University in St. Louis; Lindsey A. Olsen, Washington University in St. Louis
• Language: English
• Date: 2019-03-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2019 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.prro.2018.11.007
• Title of Journal or Parent Work: Practical Radiation Oncology
• Volume: 9
• Issue: 2
• Start Page: e218
• End Page: e227
• Grant/Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
• Keywords: efficacy; single-institution knowledge based planning; dosimetric plan quality control
• Research Categories: Physics, Radiation; Health Sciences, Oncology

Abstract

Purpose To evaluate the feasibility of using a single-institution knowledge based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectively evaluated using a subset of plans submitted to RTOG 0617. Methods and Materials A single KBP model was created utilizing a commercially available software (RapidPlan™, Varian Medical Systems, Palo Alto, CA) and data from 106 patients with non-small cell lung cancer (NSCLC) treated at a single institution. All plans had prescriptions ranging from 60Gy/30fx to 74Gy/37fx and followed planning guidelines from RTOG 0617. Two sets of optimization objectives were created to produce different trade-offs using the single KBP model predictions: one prioritizing target coverage (PC) and a second prioritizing lung sparing (LS) while allowing acceptable variation in target coverage. Three institutions that submitted a high volume of clinical plans to RTOG 0617 provided 25 patients which were replanned using both sets of optimization objectives. Model-generated dose volume histogram predictions were used to identify patients that exceeded Lungs-CTV V20Gy > 37% and would benefit from the LS objectives. Overall plan quality differences between KBP-generated plans and clinical plans were evaluated at RTOG 0617 defined dosimetric end-points. Results Target coverage and OAR sparing was significantly improved for most KBP generated plans compared to clinical trial data. The KBP model using PC objectives reduced heart Dmean and V40Gy by 2.1Gy and 5.2%, respectively. Similarly, utilizing LS objectives reduced the Lungs-CTV Dmean and V20Gy by 2.0Gy and 2.9% respectively. The KBP predictions correctly identified all patients with Lungs-CTV V20Gy>37% (5/25) and significantly reduced dose to Lungs-CTV by applying LS optimization objectives. Conclusions A single institution KBP model can be applied as a QC tool for multi-institutional clinical trials to improve overall plan quality and provide decision-support to determine the need for anatomy-based dosimetric trade-offs.