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Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area

Authors
 Nalee Kim  ;  Jaehee Chun  ;  Jee Suk Chang  ;  Chang Geol Lee  ;  Ki Chang Keum  ;  Jin Sung Kim 
Citation
 CANCERS, Vol.13(4) : 702, 2021-02 
Journal Title
 CANCERS 
Issue Date
2021-02
Abstract
This study investigated the feasibility of deep learning-based segmentation (DLS) and continual training for adaptive radiotherapy (RT) of head and neck (H&N) cancer. One-hundred patients treated with definitive RT were included. Based on 23 organs-at-risk (OARs) manually segmented in initial planning computed tomography (CT), modified FC-DenseNet was trained for DLS: (i) using data obtained from 60 patients, with 20 matched patients in the test set (DLSm); (ii) using data obtained from 60 identical patients with 20 unmatched patients in the test set (DLSu). Manually contoured OARs in adaptive planning CT for independent 20 patients were provided as test sets. Deformable image registration (DIR) was also performed. All 23 OARs were compared using quantitative measurements, and nine OARs were also evaluated via subjective assessment from 26 observers using the Turing test. DLSm achieved better performance than both DLSu and DIR (mean Dice similarity coefficient; 0.83 vs. 0.80 vs. 0.70), mainly for glandular structures, whose volume significantly reduced during RT. Based on subjective measurements, DLS is often perceived as a human (49.2%). Furthermore, DLSm is preferred over DLSu (67.2%) and DIR (96.7%), with a similar rate of required revision to that of manual segmentation (28.0% vs. 29.7%). In conclusion, DLS was effective and preferred over DIR. Additionally, continual DLS training is required for an effective optimization and robustness in personalized adaptive RT.
Files in This Item:
T202100789.pdf Download
DOI
10.3390/cancers13040702
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers
Yonsei Authors
Keum, Ki Chang(금기창) ORCID logo https://orcid.org/0000-0003-4123-7998
Kim, Nalee(김나리) ORCID logo https://orcid.org/0000-0003-4742-2772
Kim, Jinsung(김진성) ORCID logo https://orcid.org/0000-0003-1415-6471
Lee, Chang Geol(이창걸) ORCID logo https://orcid.org/0000-0002-8702-881X
Chang, Jee Suk(장지석) ORCID logo https://orcid.org/0000-0001-7685-3382
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/182243
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