Literature DB >> 30912722

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

Li Lin1, Qi Dou1, Yue-Ming Jin1, Guan-Qun Zhou1, Yi-Qiang Tang1, Wei-Lin Chen1, Bao-An Su1, Feng Liu1, Chang-Juan Tao1, Ning Jiang1, Jun-Yun Li1, Ling-Long Tang1, Chuan-Miao Xie1, Shao-Min Huang1, Jun Ma1, Pheng-Ann Heng1, Joseph T S Wee1, Melvin L K Chua1, Hao Chen1, Ying Sun1.   

Abstract

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; P < .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 Online supplemental material is available for this article. See also the editorial by Chang in this issue.

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Year:  2019        PMID: 30912722     DOI: 10.1148/radiol.2019182012

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  52 in total

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2.  Future of Radiotherapy in Nasopharyngeal Carcinoma.

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6.  A convolutional neural network combined with positional and textural attention for the fully automatic delineation of primary nasopharyngeal carcinoma on non-contrast-enhanced MRI.

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Review 7.  Nasopharyngeal carcinoma: an evolving paradigm.

Authors:  Kenneth C W Wong; Edwin P Hui; Kwok-Wai Lo; Wai Kei Jacky Lam; David Johnson; Lili Li; Qian Tao; Kwan Chee Allen Chan; Ka-Fai To; Ann D King; Brigette B Y Ma; Anthony T C Chan
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8.  Inter-observer agreement of computed tomography and magnetic resonance imaging on gross tumor volume delineation of intrahepatic cholangiocarcinoma: an initial study.

Authors:  Nan Zhou; Anning Hu; Zhihao Shi; Xiaolu Wang; Qiongjie Zhu; Qun Zhou; Jun Ma; Feng Zhao; Weiwei Kong; Jian He
Journal:  Quant Imaging Med Surg       Date:  2021-02

9.  Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients.

Authors:  Yngve Mardal Moe; Aurora Rosvoll Groendahl; Oliver Tomic; Einar Dale; Eirik Malinen; Cecilia Marie Futsaether
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-02-09       Impact factor: 9.236

10.  Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks.

Authors:  Zhikai Liu; Fangjie Liu; Wanqi Chen; Xia Liu; Xiaorong Hou; Jing Shen; Hui Guan; Hongnan Zhen; Shaobin Wang; Qi Chen; Yu Chen; Fuquan Zhang
Journal:  Front Oncol       Date:  2021-02-16       Impact factor: 6.244

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