IMRT QA using machine learning: A multi-institutional validation.

Gilmer Valdes,Maria F Chan,Joseph O Deasy,Ryan Scheuermann,Seng Boh Lim,Timothy D Solberg

doi:10.1002/acm2.12161

Abstract

PurposeTo 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.MethodsA 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.ResultsThe 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.ConclusionsWe 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.

Highlights

Over 50% of cancer patients receive radiotherapy as partial or full cancer treatment, and radiotherapy is an increasingly complex process
Equation 9 was used to construct histograms of measured versus predicted passing rates using a 3%/3 mm local gamma threshold (Figs. 2 and 3). This magnitude commonly called residual is the standard metric to evaluate the performance of regression algorithm in a similar way that Area Under the Curve is used to evaluate performance in classification algorithms.[22]
All composite plans measured using diode-array detectors were predicted within 3% accuracy[2], while passing rates for portal dosimetry on per-beam basis were predicted within

Summary

Introduction

Over 50% of cancer patients receive radiotherapy as partial or full cancer treatment, and radiotherapy is an increasingly complex process. It is common to perform patient-specific pretreatment verification prior to intensity-modulated radiation therapy (IMRT) delivery. This process is time consuming and not altogether instructive due to the myriad of sources that affect a passing result. A machine learning algorithm, Virtual IMRT QA, was developed that can predict IMRT QA passing rates and identify underlying sources of errors not otherwise apparent.[20] The algorithm identified the correlation between the IMRT plan complexity metrics and gamma passing rates and was validated on a single planning/delivery platform. The objective of this study is to further validate the approach using a large, heterogeneous dataset using different QA measurement devices (diode-array detectors and portal dosimetry) on different models of treatment machines and at different institutions

Objectives

Methods

Results

Discussion

Conclusion