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Multicentric Evaluation of a Machine Learning Model to Streamline the Radiotherapy Patient Specific Quality Assurance Process by Nicola Lambri; Victor Hernandez; Jordi Sáez; Marco Pelizzoli; Sara Parabicoli; Stefano Tomatis; Daniele Loiacono; Marta Scorsetti; Pietro Mancosu is a Medicine article available to read on EtoBox.

What is Multicentric Evaluation of a Machine Learning Model to Streamline the Radiotherapy Patient Specific Quality Assurance Process about?

Purpose 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. Results 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

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Author

Nicola Lambri; Victor Hernandez; Jordi Sáez; Marco Pelizzoli; Sara Parabicoli; Stefano Tomatis; Daniele Loiacono; Marta Scorsetti; Pietro Mancosu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Health Sciences)