Target Volume Delineation Training for Clinical Oncology Trainees: The Role of ARENA and COPP

Segmentation of 71 Anatomical Structures Necessary for the Evaluation of Guideline-Conforming Clinical Target Volumes in Head and Neck Cancers

The delineation of the clinical target volumes (CTVs) for radiation therapy is time-consuming, requires intensive training and shows high inter-observer variability. Supervised deep-learning methods depend heavily on consistent training data; thus, State-of-the-Art research focuses on making CTV labels more homogeneous and strictly bounding them to current standards. International consensus expert guidelines standardize CTV delineation by conditioning the extension of the clinical target volume on the surrounding anatomical structures. Training strategies that directly follow the construction rules given in the expert guidelines or the possibility of quantifying the conformance of manually drawn contours to the guidelines are still missing. Seventy-one anatomical structures that are relevant to CTV delineation in head- and neck-cancer patients, according to the expert guidelines, were segmented on 104 computed tomography scans, to assess the possibility of automating their segmentation by State-of-the-Art deep learning methods. All 71 anatomical structures were subdivided into three subsets of non-overlapping structures, and a 3D nnU-Net model with five-fold cross-validation was trained for each subset, to automatically segment the structures on planning computed tomography scans. We report the DICE, Hausdorff distance and surface DICE for 71 + 5 anatomical structures, for most of which no previous segmentation accuracies have been reported. For those structures for which prediction values have been reported, our segmentation accuracy matched or exceeded the reported values. The predictions from our models were always better than those predicted by the TotalSegmentator. The sDICE with 2 mm margin was larger than 80% for almost all the structures. Individual structures with decreased segmentation accuracy are analyzed and discussed with respect to their impact on the CTV delineation following the expert guidelines. No deviation is expected to affect the rule-based automation of the CTV delineation.

Evaluation of the impact of teaching on delineation variation during a virtual stereotactic ablative radiotherapy contouring workshop

Introduction:

Variation in delineation of target volumes/organs at risk (OARs) is well recognised in radiotherapy and may be reduced by several methods including teaching. We evaluated the impact of teaching on contouring variation for thoracic/pelvic stereotactic ablative radiotherapy (SABR) during a virtual contouring workshop.

Materials and methods:

Target volume/OAR contours produced by workshop participants for three cases were evaluated against reference contours using DICE similarity coefficient (DSC) and line domain error (LDE) metrics. Pre- and post-workshop DSC results were compared using Wilcoxon signed ranks test to determine the impact of teaching during the workshop.

Results:

Of 50 workshop participants, paired pre- and post-workshop contours were available for 21 (42%), 20 (40%) and 22 (44%) participants for primary lung cancer, pelvic bone metastasis and pelvic node metastasis cases, respectively. Statistically significant improvements post-workshop in median DSC and LDE results were observed for 6 (50%) and 7 (58%) of 12 structures, respectively, although the magnitude of DSC/LDE improvement was modest in most cases. An increase in median DSC post-workshop ≥0·05 was only observed for GTVbone, IGTVlung and SacralPlex, and reduction in median LDE > 1 mm was only observed for GTVbone, CTVbone and SacralPlex. Post-workshop, median DSC values were >0·7 for 75% of structures. For 92% of the structures, post-workshop contours were considered to be acceptable or within acceptable variation following review by the workshop faculty.

Conclusions:

This study has demonstrated that virtual SABR contouring training is feasible and was associated with some improvements in contouring variation for multiple target volumes/OARs.