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Liao W, Luo X, He Y, Dong Y, Li C, Li K, Zhang S, Zhang S, Wang G, Xiao J. Comprehensive Evaluation of a Deep Learning Model for Automatic Organs-at-Risk Segmentation on Heterogeneous Computed Tomography Images for Abdominal Radiation Therapy. Int J Radiat Oncol Biol Phys 2023; 117:994-1006. [PMID: 37244625 DOI: 10.1016/j.ijrobp.2023.05.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/13/2023] [Accepted: 05/18/2023] [Indexed: 05/29/2023]
Abstract
PURPOSE Our purpose was to develop a deep learning model (AbsegNet) that produces accurate contours of 16 organs at risk (OARs) for abdominal malignancies as an essential part of fully automated radiation treatment planning. METHODS AND MATERIALS Three data sets with 544 computed tomography scans were retrospectively collected. Data set 1 was split into 300 training cases and 128 test cases (cohort 1) for AbsegNet. Data set 2, including cohort 2 (n = 24) and cohort 3 (n = 20), were used to validate AbsegNet externally. Data set 3, including cohort 4 (n = 40) and cohort 5 (n = 32), were used to clinically assess the accuracy of AbsegNet-generated contours. Each cohort was from a different center. The Dice similarity coefficient and 95th-percentile Hausdorff distance were calculated to evaluate the delineation quality for each OAR. Clinical accuracy evaluation was classified into 4 levels: no revision, minor revisions (0% < volumetric revision degrees [VRD] ≤ 10%), moderate revisions (10% ≤ VRD < 20%), and major revisions (VRD ≥20%). RESULTS For all OARs, AbsegNet achieved a mean Dice similarity coefficient of 86.73%, 85.65%, and 88.04% in cohorts 1, 2, and 3, respectively, and a mean 95th-percentile Hausdorff distance of 8.92, 10.18, and 12.40 mm, respectively. The performance of AbsegNet outperformed SwinUNETR, DeepLabV3+, Attention-UNet, UNet, and 3D-UNet. When experts evaluated contours from cohorts 4 and 5, 4 OARs (liver, kidney_L, kidney_R, and spleen) of all patients were scored as having no revision, and over 87.5% of patients with contours of the stomach, esophagus, adrenals, or rectum were considered as having no or minor revisions. Only 15.0% of patients with colon and small bowel contours required major revisions. CONCLUSIONS We propose a novel deep-learning model to delineate OARs on diverse data sets. Most contours produced by AbsegNet are accurate and robust and are, therefore, clinically applicable and helpful to facilitate radiation therapy workflow.
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Affiliation(s)
- Wenjun Liao
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Xiangde Luo
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai AI Laboratory, Shanghai, China
| | - Yuan He
- Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Ye Dong
- Department of NanFang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Churong Li
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Kang Li
- West China Biomedical Big Data Center
| | - Shichuan Zhang
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Shaoting Zhang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai AI Laboratory, Shanghai, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai AI Laboratory, Shanghai, China
| | - Jianghong Xiao
- Radiotherapy Physics & Technology Center, Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
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Kim WC, Won YK, Lee SM, Heo NH, Yeo SG, Chang AR, Bae SH, Kim JS, Yoo ID, Hong SP, Min CK, Jo IY, Kim ES. Evaluating the Necessity of Adaptive RT and the Role of Deformable Image Registration in Lung Cancer with Different Pathologic Classifications. Diagnostics (Basel) 2023; 13:2956. [PMID: 37761323 PMCID: PMC10527903 DOI: 10.3390/diagnostics13182956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/07/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND This study aimed to analyze differential radiotherapy (RT) responses according to the pathological type of lung cancer to see the possibility of applying adaptive radiotherapy (ART). METHODS ART planning with resampled-computed tomography was conducted for a total of 30 patients (20 non-small-cell lung cancer patients and 10 small-cell lung cancer patients) using a deformable image registration technique to reveal gross tumor volume (GTV) changes according to the duration of RT. RESULTS The small-cell lung cancer group demonstrated an average GTV reduction of 20.95% after the first week of initial treatment (p = 0.001), whereas the adenocarcinoma and squamous cell carcinoma groups showed an average volume reduction of 20.47% (p = 0.015) and 12.68% in the second week. The application of ART according to the timing of GTV reduction has been shown to affect changes in radiation dose irradiated to normal tissues. This suggests that ART applications may have to be different depending on pathological differences in lung cancer. CONCLUSION Through these results, the present study proposes the possibility of personalized treatment options for individual patients by individualizing ART based on specific radiation responses by pathologic types of lung cancer.
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Affiliation(s)
- Woo Chul Kim
- Department of Radiation Oncology, Division of Medical Physics, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea; (W.C.K.); (C.K.M.)
- Department of Radiation Oncology, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea;
| | - Yong Kyun Won
- Department of Radiation Oncology, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea;
| | - Sang Mi Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea; (S.M.L.); (I.D.Y.); (S.-p.H.)
| | - Nam Hun Heo
- Clinical Trial Center, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea;
| | - Seung-Gu Yeo
- Department of Radiation Oncology, Soonchunhyang University Bucheon Hospital, 170, Jomaru-ro, Bucheon 14584, Republic of Korea; (S.-G.Y.); (S.H.B.)
| | - Ah Ram Chang
- Department of Radiation Oncology, Soonchunhyang University Seoul Hospital, 59, Daesagwan-ro, Yongsan-gu, Seoul 04401, Republic of Korea; (A.R.C.); (J.S.K.)
| | - Sun Hyun Bae
- Department of Radiation Oncology, Soonchunhyang University Bucheon Hospital, 170, Jomaru-ro, Bucheon 14584, Republic of Korea; (S.-G.Y.); (S.H.B.)
| | - Jae Sik Kim
- Department of Radiation Oncology, Soonchunhyang University Seoul Hospital, 59, Daesagwan-ro, Yongsan-gu, Seoul 04401, Republic of Korea; (A.R.C.); (J.S.K.)
| | - Ik Dong Yoo
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea; (S.M.L.); (I.D.Y.); (S.-p.H.)
| | - Sun-pyo Hong
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea; (S.M.L.); (I.D.Y.); (S.-p.H.)
| | - Chul Kee Min
- Department of Radiation Oncology, Division of Medical Physics, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea; (W.C.K.); (C.K.M.)
- Department of Radiation Oncology, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea;
| | - In Young Jo
- Department of Radiation Oncology, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea;
| | - Eun Seog Kim
- Department of Radiation Oncology, Soonchunhyang University Cheonan Hospital, 31, Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea;
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Takahashi H, Kadoya N, Katsuta Y, Tanaka S, Arai K, Yamamoto T, Umezawa R, Jingu K. [Evaluation of Accuracy of Deformable Image Registration with Newly Developed Treatment Planning Support Software for Thoracic Images]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:1187-1193. [PMID: 36002256 DOI: 10.6009/jjrt.2022-1308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This study evaluated accuracy of deformable image registration (DIR) with twelve parameter settings for thoracic images. We used peak-inhale and peak-exhale images for ten patients provided by DIR-lab. We used a prototype version of iCView software (ITEM Corporation) with DIR to perform intensity, structure, and hybrid-based DIR with the twelve parameter settings. DIR accuracy was evaluated by a target registration error (TRE) using 300 bronchial bifurcations and the Dice similarity coefficient (DSC) of the lungs. For twelve parameter settings, TRE ranged from 2.83 mm to 5.27 mm, whereas DSC ranged from 0.96 to 0.98. These results demonstrated that DIR accuracy differed among parameter settings and show that appropriate parameter settings are required for clinical practice.
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Affiliation(s)
- Haruna Takahashi
- Department of Radiation Oncology, Tohoku University School of Medicine
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University School of Medicine
| | - Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University School of Medicine
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University School of Medicine
| | - Kazuhiro Arai
- Department of Radiation Oncology, Tohoku University School of Medicine
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University School of Medicine
| | - Rei Umezawa
- Department of Radiation Oncology, Tohoku University School of Medicine
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University School of Medicine
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