Deep Learning-Based Multileaf Collimator Error Classification and Quantification in Patient- Specific Intensity Modulated Radiation Therapy Quality Assurance

Purpose This study presents a deep learning–based patient-specific quality assurance (PSQA) framework for rectal cancer intensity-modulated radiation therapy (IMRT) designed to classify and quantify multileaf collimator (MLC) position errors. Materials and Methods Thirty rectal IMRT treatment plans were analyzed, and both systematic and random MLC errors were deliberately introduced by modifying the digital imaging and communications in medicine - radiation therapy plan files. The framework utilizes convolutional neural networks (CNNs) trained on subtraction images generated from electronic portal imaging device–acquired and portal dose image prediction–predicted dose distributions. One CNN was developed to categorize plans based on the associated errors into three groups: error-free, systematic errors, and random errors. In parallel, regression-based CNN models were created to estimate the magnitude of the detected errors. Results The classification network achieved an overall accuracy of 96.67%, with excellent sensitivity and specificity across all categories. For systematic error estimation, the regression model produced a mean absolute error of 1.082 and a strong R-squared of 0.804, indicating precise quantification capability. In contrast, the random error model reached an accuracy of 89.00% but had a lower R-squared of 0.294, highlighting an area for future improvement. Conclusion These findings suggest that deep learning models can offer more detailed and quantitative insights into treatment errors compared to traditional gamma analysis, ultimately enhancing PSQA processes and contributing to improved treatment verification and patient safety.