Dataset related to article "Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process"

<p>&nbsp;This record contains raw data related to article &ldquo;Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process&quot;</p> <p><em>Purpose:</em> 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.</p> <p><em>Materials and Methods:</em> 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&rsquo;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&rsquo;s sensitivity and specificity, were computed.</p> <p><em>Results:</em> 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&rsquo;s predictions were, on average, close to the real values and provided a conservative estimation of the GPR.</p> <p><em>Conclusions</em>: 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.</p> <p>&nbsp;</p>