Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)

Korean Society of Radiological Science (대한방사선과학회)
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Prediction of Delivery Quality Assurance Via Machine Learning in Helical Tomotherapy

방사선치료 시 다양한 기계학습을 이용한 선량품질관리 결과의 예측

  • 장경환 (극동대학교 방사선학과)
  • Received : 2024.06.18
  • Accepted : 2024.07.08
  • Published : 2024.08.31
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Abstract

The objective of this study was to evaluate the accuracy and impact of leaf open time (LOT) and pitch using various machine learning models on EBT film-based delivery quality assurance (DQA) performed on 211 patients of helical tomotherapy (HT). We randomly selected passed (n=191) and failed (n=20) DQA measurements to evaluate the accuracy of the k-nearest neighbor (KNN), support vector machine (SVM), naive Bayes (NB) and logistic regression (LR) models using scale-dependent metrics such as the coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). We evaluated the performance of the four prediction models in terms of the accuracy, precision, sensitivity, and F1-score using a confusion matrix, finding the NB and LR models to achieve optimal results. The results of this study are expected to reduce the workload of medical physicists and dosimetrists by predicting DQA results according to LOT and pitch in advance.

Keywords

Acknowledgement

This work was supported by the 2023 Far East University Research Grant (FEU2023R29)

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