Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study

Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications. Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N). However, prohibitive labor costs cause most institutions to delineate a substantially smaller subset of OARs, neglecting the dose distributions of other OARs. Here, we present an automated and highly effective stratified OAR segmentation (SOARS) system using deep learning that precisely delineates a comprehensive set of 42 H&N OARs. We train SOARS using 176 patients from an internal institution and independently evaluate it on 1327 external patients across six different institutions. It consistently outperforms other state-of-the-art methods by at least 3-5% in Dice score for each institutional evaluation (up to 36% relative distance error reduction). Crucially, multi-user studies demonstrate that 98% of SOARS predictions need only minor or no revisions to achieve clinical acceptance (reducing workloads by 90%). Moreover, segmentation and dosimetric accuracy are within or smaller than the inter-user variation.

Automated summary

In this study, we propose an automated and highly effective stratified OAR segmentation system using deep learning (SOARS) that precisely delineates a comprehensive set of 42 H&N OARs. The results show that SOARS consistently outperforms other state-of-the-art methods in evaluations across different institutions, reducing workload significantly. Moreover, the segmentation and dosimetric accuracy are within or smaller than the inter-user variation.