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      IMRT QA using machine learning: A multi‐institutional validation

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          Abstract

          Purpose

          To validate a machine learning approach to Virtual intensity‐modulated radiation therapy ( IMRT) quality assurance ( QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.

          Methods

          A Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode‐array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units ( CU) or dose were included in the comparison. 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

          The methodology predicted passing rates within 3% accuracy for all composite plans measured using diode‐array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per‐beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under‐response in low‐dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline ( CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.

          Conclusions

          We have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

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          Most cited references22

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          The report of Task Group 100 of the AAPM: Application of risk analysis methods to radiation therapy quality management.

          The increasing complexity of modern radiation therapy planning and delivery challenges traditional prescriptive quality management (QM) methods, such as many of those included in guidelines published by organizations such as the AAPM, ASTRO, ACR, ESTRO, and IAEA. These prescriptive guidelines have traditionally focused on monitoring all aspects of the functional performance of radiotherapy (RT) equipment by comparing parameters against tolerances set at strict but achievable values. Many errors that occur in radiation oncology are not due to failures in devices and software; rather they are failures in workflow and process. A systematic understanding of the likelihood and clinical impact of possible failures throughout a course of radiotherapy is needed to direct limit QM resources efficiently to produce maximum safety and quality of patient care. Task Group 100 of the AAPM has taken a broad view of these issues and has developed a framework for designing QM activities, based on estimates of the probability of identified failures and their clinical outcome through the RT planning and delivery process. The Task Group has chosen a specific radiotherapy process required for "intensity modulated radiation therapy (IMRT)" as a case study. The goal of this work is to apply modern risk-based analysis techniques to this complex RT process in order to demonstrate to the RT community that such techniques may help identify more effective and efficient ways to enhance the safety and quality of our treatment processes. The task group generated by consensus an example quality management program strategy for the IMRT process performed at the institution of one of the authors. This report describes the methodology and nomenclature developed, presents the process maps, FMEAs, fault trees, and QM programs developed, and makes suggestions on how this information could be used in the clinic. The development and implementation of risk-assessment techniques will make radiation therapy safer and more efficient.
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            Evaluating IMRT and VMAT dose accuracy: practical examples of failure to detect systematic errors when applying a commonly used metric and action levels.

            This study (1) examines a variety of real-world cases where systematic errors were not detected by widely accepted methods for IMRT/VMAT dosimetric accuracy evaluation, and (2) drills-down to identify failure modes and their corresponding means for detection, diagnosis, and mitigation. The primary goal of detailing these case studies is to explore different, more sensitive methods and metrics that could be used more effectively for evaluating accuracy of dose algorithms, delivery systems, and QA devices.
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              Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective.

              Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models. These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, "spam" filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the "barrier to entry" for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods--logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)--and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation.
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                Author and article information

                Contributors
                gilmer.valdes@ucsf.edu
                Journal
                J Appl Clin Med Phys
                J Appl Clin Med Phys
                10.1002/(ISSN)1526-9914
                ACM2
                Journal of Applied Clinical Medical Physics
                John Wiley and Sons Inc. (Hoboken )
                1526-9914
                17 August 2017
                September 2017
                : 18
                : 5 ( doiID: 10.1002/acm2.2017.18.issue-5 )
                : 279-284
                Affiliations
                [ 1 ] Department of Radiation Oncology University of California San Francisco Medical Center San Francisco CA USA
                [ 2 ] Department of Medical Physics Memorial Sloan Kettering Cancer Center New York NY USA
                [ 3 ] Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA
                Author notes
                [*] [* ] Author to whom correspondence should be addressed. Gilmer Valdes

                E‐mail: gilmer.valdes@ 123456ucsf.edu ; Telephone: (415) 910‐6105

                [†]

                First authors contribution

                Article
                ACM212161
                10.1002/acm2.12161
                5874948
                28815994
                18aede16-a82e-4e02-998a-cbb17960e971
                © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 May 2017
                : 30 June 2017
                : 10 July 2017
                Page count
                Figures: 4, Tables: 1, Pages: 6, Words: 4386
                Funding
                Funded by: NIH/NCI Cancer Center
                Award ID: P30 CA008748
                Categories
                87.55.Qr
                Radiation Oncology Physics
                Radiation Oncology Physics
                Custom metadata
                2.0
                acm212161
                September 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.3.4 mode:remove_FC converted:29.03.2018

                imrt qa,machine learning,poisson regression,radiotherapy

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