Detecting MLC modeling errors using radiomics‐based machine learning in patient‐specific QA with an EPID for intensity‐modulated radiation therapy

Abstract

Compared to the conventional gamma analysis, the radiomics-based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics-based IMRT QA was shown to be a promising approach for detecting the MLC modeling error.