A mathematical framework for virtual IMRT QA using machine learning

G. Valdes, R. Scheuermann, C. Y. Hung, A. Olszanski, M. Bellerive, T. D. Solberg

Research output: Contribution to journalArticle

42 Scopus citations

Abstract

Purpose: 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.

Original languageEnglish (US)
Pages (from-to)4323-4334
Number of pages12
JournalMedical Physics
Volume43
Issue number7
DOIs
Publication statusPublished - Jul 1 2016

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Keywords

  • local analysis
  • machine learning
  • Poisson regression
  • virtual IMRT QA

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

Valdes, G., Scheuermann, R., Hung, C. Y., Olszanski, A., Bellerive, M., & Solberg, T. D. (2016). A mathematical framework for virtual IMRT QA using machine learning. Medical Physics, 43(7), 4323-4334. https://doi.org/10.1118/1.4953835