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

Dakai Jin

Abstract:

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 ofOARs, 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.