Deep learning-based two-step organs at risk auto-segmentation model for brachytherapy planning in parotid gland carcinoma.

Abstract

Purpose: Delineation of organs at risk (OARs) represents a crucial step for both tailored delivery of radiation doses and prevention of radiation-induced toxicity in brachytherapy. Due to lack of studies on auto-segmentation methods in head and neck cancers, our study proposed a deep learning-based two-step approach for auto-segmentation of organs at risk in parotid carcinoma brachytherapy.

Material and methods: Computed tomography images of 200 patients with parotid gland carcinoma were used to train and evaluate our in-house developed two-step 3D nnU-Net-based model for OARs auto-segmentation. OARs during brachytherapy were defined as the auricula, condyle process, skin, mastoid process, external auditory canal, and mandibular ramus. Auto-segmentation results were compared to those of manual segmentation by expert oncologists. Accuracy was quantitatively evaluated in terms of dice similarity coefficient (DSC), Jaccard index, 95^th-percentile Hausdorff distance (95HD), and precision and recall. Qualitative evaluation of auto-segmentation results was also performed.

Results: The mean DSC values of each OAR were 0.88, 0.91, 0.75, 0.89, 0.74, and 0.93, respectively, indicating close resemblance of auto-segmentation results to those of manual contouring. In addition, auto-segmentation could be completed within a minute, as compared with manual segmentation, which required over 20 minutes. All generated results were deemed clinically acceptable.

Conclusions: Our proposed deep learning-based two-step OARs auto-segmentation model demonstrated high efficiency and good agreement with gold standard manual contours. Thereby, this novel approach carries the potential in expediting the treatment planning process of brachytherapy for parotid gland cancers, while allowing for more accurate radiation delivery to minimize toxicity.