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Article

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

Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul 03722, Korea
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Author to whom correspondence should be addressed.
Academic Editor: Johannes H. A. M. Kaanders
Cancers 2021, 13(4), 702; https://doi.org/10.3390/cancers13040702
Received: 19 December 2020 / Revised: 2 February 2021 / Accepted: 6 February 2021 / Published: 9 February 2021
(This article belongs to the Special Issue Adaptive Radiotherapy for Head and Neck Cancer)
We analyzed the contouring data of 23 organs-at-risk from 100 patients with head and neck cancer who underwent definitive radiation therapy (RT). Deep learning-based segmentation (DLS) with continual training was compared to DLS with conventional training and deformable image registration (DIR) in both quantitative and qualitative (Turing’s test) methods. Results indicate the effectiveness of DLS over DIR and that of DLS with continual training over DLS with conventional training in contouring for head and neck region, especially for glandular structures. DLS with continual training might be beneficial for optimizing personalized adaptive RT in head and neck region.
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. View Full-Text
Keywords: head and neck cancer; deep learning; auto segmentation; artificial intelligence; adaptive radiation therapy head and neck cancer; deep learning; auto segmentation; artificial intelligence; adaptive radiation therapy
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MDPI and ACS Style

Kim, N.; Chun, J.; Chang, J.S.; Lee, C.G.; Keum, K.C.; Kim, J.S. Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area. Cancers 2021, 13, 702. https://doi.org/10.3390/cancers13040702

AMA Style

Kim N, Chun J, Chang JS, Lee CG, Keum KC, Kim JS. Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area. Cancers. 2021; 13(4):702. https://doi.org/10.3390/cancers13040702

Chicago/Turabian Style

Kim, Nalee, Jaehee Chun, Jee Suk Chang, Chang Geol Lee, Ki Chang Keum, and Jin Sung Kim. 2021. "Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area" Cancers 13, no. 4: 702. https://doi.org/10.3390/cancers13040702

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