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Title: MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation

Abstract

Purpose: To validate a machine learning approach to Virtual IMRT QA for accurately predicting gamma passing rates using different QA devices at different institutions. Methods: A Virtual IMRT QA was constructed using a machine learning algorithm based on 416 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3mm with 10% threshold. An independent set of 139 IMRT measurements from a different institution, with QA data based on portal dosimetry using the same gamma index and 10% threshold, was used to further test the algorithm. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input. Results: In addition to predicting passing rates with 3% accuracy for all composite plans using diode-array detectors, passing rates for portal dosimetry on per-beam basis were predicted with an error <3.5% for 120 IMRT measurements. The remaining measurements (19) had large areas of low CU, where portal dosimetry has larger disagreement with the calculated dose and, as such, large errors were expected. These beams need to be further modeled to correct the under-response in low dose regions. Important features selected by Lasso to predictmore » gamma passing rates were: complete irradiated area outline (CIAO) area, jaw position, fraction of MLC leafs with gaps smaller than 20 mm or 5mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted Average Irregularity Factor, duty cycle among others. Conclusion: We have demonstrated that the Virtual IMRT QA can predict passing rates using different QA devices and across multiple institutions. Prediction of QA passing rates could have profound implications on the current IMRT process.« less

Authors:
; ;  [1]; ;  [2]
  1. University of Pennsylvania, Philadelphia, PA (United States)
  2. Memorial Sloan-Kettering Cancer Center, New York, NY (United States)
Publication Date:
OSTI Identifier:
22653880
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; DOSIMETRY; FORECASTING; LEARNING; RADIATION DOSES; RADIOTHERAPY; RATS; VALIDATION

Citation Formats

Valdes, G, Scheuermann, R, Solberg, T, Chan, M, and Deasy, J. MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation. United States: N. p., 2016. Web. doi:10.1118/1.4957313.
Valdes, G, Scheuermann, R, Solberg, T, Chan, M, & Deasy, J. MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation. United States. doi:10.1118/1.4957313.
Valdes, G, Scheuermann, R, Solberg, T, Chan, M, and Deasy, J. Wed . "MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation". United States. doi:10.1118/1.4957313.
@article{osti_22653880,
title = {MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation},
author = {Valdes, G and Scheuermann, R and Solberg, T and Chan, M and Deasy, J},
abstractNote = {Purpose: To validate a machine learning approach to Virtual IMRT QA for accurately predicting gamma passing rates using different QA devices at different institutions. Methods: A Virtual IMRT QA was constructed using a machine learning algorithm based on 416 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3mm with 10% threshold. An independent set of 139 IMRT measurements from a different institution, with QA data based on portal dosimetry using the same gamma index and 10% threshold, was used to further test the algorithm. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input. Results: In addition to predicting passing rates with 3% accuracy for all composite plans using diode-array detectors, passing rates for portal dosimetry on per-beam basis were predicted with an error <3.5% for 120 IMRT measurements. The remaining measurements (19) had large areas of low CU, where portal dosimetry has larger disagreement with the calculated dose and, as such, large errors were expected. These beams need to be further modeled to correct the under-response in low dose regions. Important features selected by Lasso to predict gamma passing rates were: complete irradiated area outline (CIAO) area, jaw position, fraction of MLC leafs with gaps smaller than 20 mm or 5mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted Average Irregularity Factor, duty cycle among others. Conclusion: We have demonstrated that the Virtual IMRT QA can predict passing rates using different QA devices and across multiple institutions. Prediction of QA passing rates could have profound implications on the current IMRT process.},
doi = {10.1118/1.4957313},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}