Geometric and dosimetric analysis of CT- and MR-based automatic contouring for the EPTN contouring atlas in neuro-oncology

作者：Femke Vaassen, Catharina M.L. Zegers, David Hofstede, Mart Wubbels, Hilde Beurskens, Lindsey Verheesen, Richard Canters, Pádraig Looney, M. Battye, Mark J. Gooding, Inge Compter, Daniëlle B. P. Eekers, Wouter van Elmpt · 发表于：Physica Medica · 年份：2023 · DOI：10.1016/j.ejmp.2023.103156 · 被引用次数：7 · 研究领域：Advanced Radiotherapy Techniques、Radiation Therapy and Dosimetry、Glioma Diagnosis and Treatment

Purpose Atlas-based and deep-learning contouring (DLC) are methods for automatic segmentation of organs-at-risk (OARs). The European Particle Therapy Network (EPTN) published a consensus-based atlas for delineation of OARs in neuro-oncology. In this study, geometric and dosimetric evaluation of automatically-segmented neuro-oncological OARs was performed using CT- and MR-models following the EPTN-contouring atlas. Methods Image and contouring data from 76 neuro-oncological patients were included. Two atlas-based models (CT-atlas and MR-atlas) and one DLC-model (MR-DLC) were created. Manual contours on registered CT-MR-images were used as ground-truth. Results were analyzed in terms of geometrical (volumetric Dice similarity coefficient (vDSC), surface DSC (sDSC), added path length (APL), and mean slice-wise Hausdorff distance (MSHD)) and dosimetrical accuracy. Distance-to-tumor analysis was performed to analyze to which extent the location of the OAR relative to planning target volume (PTV) has dosimetric impact, using Wilcoxon rank-sum tests. Results CT-atlas outperformed MR-atlas for 22/26 OARs. MR-DLC outperformed MR-atlas for all OARs. Highest median (95 %CI) vDSC and sDSC were found for the brainstem in MR-DLC: 0.92 (0.88–0.95) and 0.84 (0.77–0.89) respectively, as well as lowest MSHD: 0.27 (0.22–0.39)cm. Median dose differences (ΔD) were within ± 1 Gy for 24/26(92 %) OARs for all three models. Distance-to-tumor showed a significant correlation for ΔD max,0.03cc -paramet...