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

Xianghua Ye, Dazhou Guo, Jia Ge, Senxiang Yan, Yi Xin, Yuchen Song, Yongheng Yan, Bing-shen Huang, Tsung-Min Hung, Zhuotun Zhu, Ling Peng, Yanping Ren, Rui Liu, Gong Zhang, Mengyuan Mao, Xiaohua Chen, Zhongjie Lu, Wenxiang Li, Yuzhen Chen, Lingyun Huang, Jing Xiao, Adam P. Harrison, Le Lu, Chien-Yu Lin (), Dakai Jin () and Tsung-Ying Ho ()
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Xianghua Ye: The First Affiliated Hospital, Zhejiang University
Dazhou Guo: Alibaba Group
Jia Ge: The First Affiliated Hospital, Zhejiang University
Senxiang Yan: The First Affiliated Hospital, Zhejiang University
Yi Xin: Ping An Technology
Yuchen Song: The First Affiliated Hospital, Zhejiang University
Yongheng Yan: The First Affiliated Hospital, Zhejiang University
Bing-shen Huang: Chang Gung Memorial Hospital
Tsung-Min Hung: Chang Gung Memorial Hospital
Zhuotun Zhu: Johns Hopkins University
Ling Peng: Zhejiang Provincial People’s Hospital, Hangzhou
Yanping Ren: Huadong Hospital Affiliated to Fudan University
Rui Liu: The First Affiliated Hospital, Xi’an Jiaotong University
Gong Zhang: People’s Hospital of Shanxi Province
Mengyuan Mao: Southern Medical University
Xiaohua Chen: The First Hospital of Lanzhou University
Zhongjie Lu: The First Affiliated Hospital, Zhejiang University
Wenxiang Li: The First Affiliated Hospital, Zhejiang University
Yuzhen Chen: Chang Gung Memorial Hospital
Lingyun Huang: Ping An Technology
Jing Xiao: Ping An Technology
Adam P. Harrison: Q Bio Inc
Le Lu: Alibaba Group
Chien-Yu Lin: Chang Gung Memorial Hospital
Dakai Jin: Alibaba Group
Tsung-Ying Ho: Chang Gung Memorial Hospital

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: 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 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.

Date: 2022
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DOI: 10.1038/s41467-022-33178-z

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