Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.

Radiology

From the Department of Radiation Oncology (L.L., G.Q.Z., J.Y.L., L.L.T., S.M.H., J.M., Y.S.) and Imaging Diagnosis and Interventional Center (C.M.X.), Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 651 Dongfeng Rd East, Guangzhou 510060, China; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR (Q.D., Y.M.J., P.A.H., H.C.); Imsight Medical Technology, Shenzhen, China (H.C.); Divisions of Radiation Oncology (J.T.S.W., M.L.K.C.) and Medical Sciences (M.L.K.C.), National Cancer Center Singapore, Singapore; Oncology Academic Programme, Duke-NUS Medical School, Singapore (M.L.K.C.); Department of Radiation Oncology, Jiangxi Cancer Hospital, Nanchang, China (Y.Q.T.); Department of Radiation Oncology, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, China (W.L.C.); Department of Radiation Oncology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China (B.A.S.); Department of Radiation Oncology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China (F.L.); Department of Radiation Oncology, Zhejiang Provincial Cancer Hospital, Key Laboratory of Radiation Oncology of Zhejiang Province, Hangzhou, China (C.J.T.); and Department of Radiation Oncology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, China (N.J.).

Published: June 2019

Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor delineation to avoid toxicities from radiation therapy; however, tumor target contouring for head and neck radiation therapy is labor intensive and highly variable among radiation oncologists. Purpose To construct and validate an artificial intelligence (AI) contouring tool to automate primary gross tumor volume (GTV) contouring in patients with NPC. Materials and Methods In this retrospective study, MRI data sets covering the nasopharynx from 1021 patients (median age, 47 years; 751 male, 270 female) with NPC between September 2016 and September 2017 were collected and divided into training, validation, and testing cohorts of 715, 103, and 203 patients, respectively. GTV contours were delineated for 1021 patients and were defined by consensus of two experts. A three-dimensional convolutional neural network was applied to 818 training and validation MRI data sets to construct the AI tool, which was tested in 203 independent MRI data sets. Next, the AI tool was compared against eight qualified radiation oncologists in a multicenter evaluation by using a random sample of 20 test MRI examinations. The Wilcoxon matched-pairs signed rank test was used to compare the difference of Dice similarity coefficient (DSC) of pre- versus post-AI assistance. Results The AI-generated contours demonstrated a high level of accuracy when compared with ground truth contours at testing in 203 patients (DSC, 0.79; 2.0-mm difference in average surface distance). In multicenter evaluation, AI assistance improved contouring accuracy (five of eight oncologists had a higher median DSC after AI assistance; average median DSC, 0.74 vs 0.78; < .001), reduced intra- and interobserver variation (by 36.4% and 54.5%, respectively), and reduced contouring time (by 39.4%). Conclusion The AI contouring tool improved primary gross tumor contouring accuracy of nasopharyngeal carcinoma, which could have a positive impact on tumor control and patient survival. © RSNA, 2019 See also the editorial by Chang in this issue.

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Source
http://dx.doi.org/10.1148/radiol.2019182012DOI Listing

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