Treatment plan complexity quantification for predicting gamma passing rates in patient‐specific quality assurance for stereotactic volumetric modulated arc therapy

Author:

Xue Xudong1,Luan Shunyao12,Ding Yi1,Li Xiangbin1,Li Dan1,Wang Jingya1,Ma Chi3,Jiang Man4,Wei Wei1,Wang Xiao3ORCID

Affiliation:

1. Department of Radiation Oncology Hubei Cancer Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

2. Department of Optoelectronic Engineering Huazhong University of Science and Technology Wuhan China

3. Department of Radiation Oncology Rutgers‐Cancer Institute of New Jersey Rutgers‐Robert Wood Johnson Medical School New Brunswick New Jersey USA

4. Department of Nuclear Engineering and Technology School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan China

Abstract

AbstractPurposeTo investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning.MethodsThe entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2 mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis.ResultsThe three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and −0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five‐fold cross‐validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365.ConclusionsThe calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Publisher

Wiley

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