Predicting the error magnitude in patient-specific QA during radiotherapy based on ResNet

Article type: Research Article

Authors: Huang, Ying^{a; b; c; 1} | Pi, Yifei^{d; 1} | Ma, Kui^e | Miao, Xiaojuan^f | Fu, Sichao^f | Feng, Aihui^{a; c} | Duan, Yanhua^{a; c} | Kong, Qing^a | Zhuo, Weihai^{b; *} | Xu, Zhiyong^{c; *}

Affiliations: [a] Institute of Modern Physics, Fudan University, Shanghai, China | [b] Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai, China | [c] Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China | [d] Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Henan, China | [e] Varian Medical Systems, Beijing, China | [f] The General Hospital of Western Theater Command PLA, Chengdu, China

Correspondence: [*] Corresponding author: Weihai Zhuo, Key Lab of Nucl. Phys. & Ion-Beam Appl. (MOE), Fudan University, 200433, Shanghai, China. E-mail: [email protected] and Zhiyong Xu, Shanghai Chest Hospital, Shanghai Jiao Tong University, 200030, Shanghai, China. E-mail: [email protected].

Note: [1] These authors have contributed equally to this work and share first authorship.

Abstract: BACKGROUND:The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude. OBJECTIVE:The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet. METHODS:A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude. RESULTS:In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD + EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45–1.54, 0.58–0.90, 0.32–0.36, and 0.15–0.24; the mean absolute error (MAE) was 1.06–1.18, 0.32–0.78, 0.25–0.27, and 0.11–0.18, respectively, for COLL, MU, MLCR and MLCS. CONCLUSIONS:In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD + EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA.

Keywords: Error magnitude prediction, ResNet, dose distribution, patient-specific QA

DOI: 10.3233/XST-230251

Journal: Journal of X-Ray Science and Technology, vol. 32, no. 3, pp. 797-807, 2024