title: A mathematical framework for virtual IMRT QA using machine learning.
abstract: OBJECTIVE
  It is common practice to perform patient-specific pretreatment verifications to the clinical delivery of IMRT. This process can be time-consuming and not altogether instructive due to the myriad sources that may produce a failing result. The purpose of this study was to develop an algorithm capable of predicting IMRT QA passing rates a priori.
  
  
  METHODS
  From all treatment, 498 IMRT plans sites were planned in eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. 3%/3 mm local dose/distance-to-agreement (DTA) was recorded using a commercial 2D diode array. Each plan was characterized by 78 metrics that describe different aspects of their complexity that could lead to disagreements between the calculated and measured dose. A Poisson regression with Lasso regularization was trained to learn the relation between the plan characteristics and each passing rate.
  
  
  RESULTS
  Passing rates 3%/3 mm local dose/DTA can be predicted with an error smaller than 3% for all plans analyzed. The most important metrics to describe the passing rates were determined to be the MU factor (MU per Gy), small aperture score, irregularity factor, and fraction of the plan delivered at the corners of a 40 × 40 cm field. The higher the value of these metrics, the worse the passing rates.
  
  
  CONCLUSIONS
  The Virtual QA process predicts IMRT passing rates with a high likelihood, allows the detection of failures due to setup errors, and it is sensitive enough to detect small differences between matched Linacs.
authors.affiliation: Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
authors.forename: G
authors.initials: G
authors.lastname: Valdes
authors.affiliation: Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
authors.forename: R
authors.initials: R
authors.lastname: Scheuermann
authors.affiliation: Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
authors.forename: C Y
authors.initials: CY
authors.lastname: Hung
authors.affiliation: Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
authors.forename: A
authors.initials: A
authors.lastname: Olszanski
authors.affiliation: Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
authors.forename: M
authors.initials: M
authors.lastname: Bellerive
authors.affiliation: Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
authors.forename: T D
authors.initials: TD
authors.lastname: Solberg
mesh_terms: D006801:Humans; D000069550:Machine Learning; D009369:Neoplasms / Q000532:radiotherapy; D011785:Quality Assurance, Health Care / Q000379:methods*; D011874:Radiometry; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000295:instrumentation / Q000379:methods*; D012044:Regression Analysis; D017211:Treatment Failure