- •XGBoost was trained to predict the gamma passing rate for patient-specific QA.
- •The machine learning model was evaluated in a multicentric scenario.
- •The model provided accurate and conservative estimations of the gamma passing rate.
- •Machine learning can streamline the radiotherapy workflow.
- •Machine learning models should be verified within centers before clinical use.
Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans.
Materials and methods
5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model’s performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model’s sensitivity and specificity, were computed.
The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model’s predictions were, on average, close to the real values and provided a conservative estimation of the GPR.
ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.
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Published online: April 25, 2023
Accepted: April 14, 2023
Received in revised form: March 2, 2023
Received: November 24, 2022
© 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.