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Generalizable Magnetic Resonance Imaging- based Nasopharyngeal Carcinoma Delineation Bridging Gaps Across Multiple Centers and Raters With Active Learning

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机构: [1]Univ Elect Sci & Technol China, Dept Radiat Oncol,Affiliated Canc Hosp, Radiat Oncol Key Lab Sichuan Prov,Sichuan Canc Ctr, Sichuan Clin Res Ctr Canc,Sichuan Canc Hosp & Inst, Chengdu, Peoples R China [2]Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China [3]Hong Kong Univ Sci & Technol, Dept Syst Hub, Guangzhou, Peoples R China [4]Southern Med Univ, Nanfang Hosp, Dept Radiat Oncol, Guangzhou, Peoples R China [5]Univ Sci & Technol China, Affiliated Hosp USTC 1, Dept Radiat Oncol, Div Life Sci & Med, Hefei, Anhui, Peoples R China [6]Univ Elect Sci & Technol China, Sichuan Prov Peoples Hosp, Canc Ctr, Chengdu, Peoples R China [7]Sichuan Univ, West China Hosp, Radiotherapy Phys & Technol Ctr, Canc Ctr,Dept Radiat Oncol, Chengdu, Peoples R China [8]SenseTime Res, Shanghai, Peoples R China [9]Shanghai AI Lab, Shanghai, Peoples R China
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关键词: Nasopharyngeal carcinoma Deep learning Segmentation magnetic resonance

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Purpose: To develop a deep learning method exploiting active learning and source-free domain adaptation for gross tumor volume delineation in nasopharyngeal carcinoma (NPC), addressing the variability and inaccuracy when deploying segmentation models in multicenter and multirater settings. Methods and Materials: One thousand fifty-seven magnetic resonance imaging scans of patients with NPC from 5 hospitals were retrospectively collected and annotated by experts from the same medical group with consensus for multicenter adaptation evaluation. One data set was used for model development (source domain), with the remaining 4 for adaptation testing (target domains). Meanwhile, another set of 170 patients with NPC, with annotations delineated by 4 independent experts, was created for multirater adaptation evaluation. We evaluated the pretrained model's migration ability to the 4 multicenter and 4 multirater target domains. Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and other metrics were used for quantitative evaluations. Results: In the adaptation of dataset5 to other data sets, our source-free active learning adaptation method only requires limited labeled target samples (only 20%) to achieve a median DSC ranging from 0.70 to 0.86 and a median HD95 ranging from 3.16 to 7.21 mm for 4 target centers, and 0.78 to 0.85 and 3.64 to 6.00 mm for 4 multirater data sets. For DSC, our results for 3 of 4 multicenter data sets and all multirater data sets showed no statistical difference compared to the fully supervised U-Net model (P values > 0.05) and significantly surpassed comparison models for 3 multicenter data sets and all multirater data sets (P values < 0.05). Clinical assessment showed that our method-generated delineations can be used both in multicenter and multirater scenarios after minor refinement (revision ratio <10% and median time <2 minutes). Conclusions: The proposed method effectively minimizes domain gaps and delivers encouraging performance compared with fully supervised learning models with limited labeled training samples, offering a promising and practical solution for accurate and generalizable gross tumor volume segmentation in NPC. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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基金编号: 82203197 62271115 2023NSFSC1852 2023NSFSC0720

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大类 | 1 区 医学
小类 | 2 区 肿瘤学 2 区 核医学
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大类 | 1 区 医学
小类 | 2 区 肿瘤学 2 区 核医学
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Q1 ONCOLOGY Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

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第一作者机构: [1]Univ Elect Sci & Technol China, Dept Radiat Oncol,Affiliated Canc Hosp, Radiat Oncol Key Lab Sichuan Prov,Sichuan Canc Ctr, Sichuan Clin Res Ctr Canc,Sichuan Canc Hosp & Inst, Chengdu, Peoples R China [2]Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China
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