Explaining the dosimetric impact of contouring errors in head and neck radiotherapy.

. Auto-contouring of organs at risk (OAR) is becoming more common in radiotherapy. An important issue in clinical decision making is judging the quality of the auto-contours. While recent studies considered contour quality by looking at geometric errors only, this does not capture the dosimetric impact of the errors. In this work, we studied the relationship between geometrical errors, the local dose and the dosimetric impact of the geometrical errors.. For 94 head and neck patients, unmodified atlas-based auto-contours and clinically used delineations of the parotid glands and brainstem were retrieved. VMAT plans were automatically optimized on the auto-contours and evaluated on both contours. We defined the dosimetric impact on evaluation (DIE) as the difference in the dosimetric parameter of interest between the two contours. We developed three linear regression models to predict the DIE using: (1) global geometric metrics, (2) global dosimetric metrics, (3) combined local geometric and dosimetric metrics. For model (3), we next determined the minimal amount of editing information required to produce a reliable prediction. Performance was assessed by the root mean squared error (RMSE) of the predicted DIE using 5-fold cross-validation.. In model (3), the median RMSE of the left parotid was 0.4 Gy using 5% of the largest editing vectors. For the right parotid and brainstem the results were 0.5 Gy using 10% and 0.4 Gy using 1% respectively. The median RMS of the DIE was 0.6 Gy, 0.7 Gy and 0.9 Gy for the left parotid, the right parotid and the brainstem, respectively. Model (3), combining local dosimetric and geometric quantities, outperformed the models that used only geometric or dosimetric information.. We showed that the largest local errors plus the local dose suffice to accurately predict the dosimetric impact, opening the door to automated dosimetric QA of auto-contours.