IMRT QA using machine learning: A multi‐institutional validation

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.

Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning

“…Several publications have demonstrated promising results in correlating IMRT QA passing rates with beam complexity scores . Machine learning and deep learning have recently been used to predict IMRT QA passing rates . Among these reports Valdes et al .…”

A deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance

“…9, 10 Valdes et al discussed machine learning-based prediction models, in which gamma evaluation was performed using criteria of 3% (local)/3 mm. 11,12 However, recent publications have suggested that criteria stricter than 3%(global)/3 mm be applied for IMRT plan gamma evaluation for error detection. 13,14 Nelms et.al.…”

Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning

“…However, this measurement‐based QA still requires either setup or beam delivery times and cannot predict unacceptable‐quality plans . Recently, prediction of dosimetric accuracy has been developed as a more efficient patient‐specific QA method than measurement‐based QA . In fact, prediction does not require setup or beam delivery times, leading to increased adoption of IMRT and VMAT in clinical facilities.…”

“…Sum et al reported the prediction of beam output and derived MUs using machine learning . In recent years, there is a growing interest in applying machine learning for predicting the gamma passing rate for IMRT . For instance, Valdes et al predicted the gamma passing rate of 498 IMRT plans using 78 parameters, including MU, linac type, and position of multi‐leaf collimator .…”