Yibing Wang1, Ben J M Heijmen2, Steven F Petit2. 1. Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands. Electronic address: y.wang.1@erasmusmc.nl. 2. Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Abstract
PURPOSE: To prospectively investigate the use of an independent DVH prediction tool to detect outliers in the quality of fully automatically generated treatment plans for prostate cancer patients. MATERIALS/ METHODS: A plan QA tool was developed to predict rectum, anus and bladder DVHs, based on overlap volume histograms and principal component analysis (PCA). The tool was trained with 22 automatically generated, clinical plans, and independently validated with 21 plans. Its use was prospectively investigated for 50 new plans by replanning in case of detected outliers. RESULTS: For rectum Dmean, V65Gy, V75Gy, anus Dmean, and bladder Dmean, the difference between predicted and achieved was within 0.4 Gy or 0.3% (SD within 1.8 Gy or 1.3%). Thirteen detected outliers were re-planned, leading to moderate but statistically significant improvements (mean, max): rectum Dmean (1.3 Gy, 3.4 Gy), V65Gy (2.7%, 4.2%), anus Dmean (1.6 Gy, 6.9 Gy), and bladder Dmean (1.5 Gy, 5.1 Gy). The rectum V75Gy of the new plans slightly increased (0.2%, p = 0.087). CONCLUSION: A high accuracy DVH prediction tool was developed and used for independent QA of automatically generated plans. In 28% of plans, minor dosimetric deviations were observed that could be improved by plan adjustments. Larger gains are expected for manually generated plans.
PURPOSE: To prospectively investigate the use of an independent DVH prediction tool to detect outliers in the quality of fully automatically generated treatment plans for prostate cancerpatients. MATERIALS/ METHODS: A plan QA tool was developed to predict rectum, anus and bladder DVHs, based on overlap volume histograms and principal component analysis (PCA). The tool was trained with 22 automatically generated, clinical plans, and independently validated with 21 plans. Its use was prospectively investigated for 50 new plans by replanning in case of detected outliers. RESULTS: For rectum Dmean, V65Gy, V75Gy, anus Dmean, and bladder Dmean, the difference between predicted and achieved was within 0.4 Gy or 0.3% (SD within 1.8 Gy or 1.3%). Thirteen detected outliers were re-planned, leading to moderate but statistically significant improvements (mean, max): rectum Dmean (1.3 Gy, 3.4 Gy), V65Gy (2.7%, 4.2%), anus Dmean (1.6 Gy, 6.9 Gy), and bladder Dmean (1.5 Gy, 5.1 Gy). The rectum V75Gy of the new plans slightly increased (0.2%, p = 0.087). CONCLUSION: A high accuracy DVH prediction tool was developed and used for independent QA of automatically generated plans. In 28% of plans, minor dosimetric deviations were observed that could be improved by plan adjustments. Larger gains are expected for manually generated plans.
Authors: Dale Roach; Geert Wortel; Cesar Ochoa; Henrik R Jensen; Eugene Damen; Philip Vial; Tomas Janssen; Christian Rønn Hansen Journal: Phys Imaging Radiat Oncol Date: 2019-04-24
Authors: Kelly Kisling; Carlos Cardenas; Brian M Anderson; Lifei Zhang; Anuja Jhingran; Hannah Simonds; Peter Balter; Rebecca M Howell; Kathleen Schmeler; Beth M Beadle; Laurence Court Journal: Pract Radiat Oncol Date: 2020-05-23