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Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks

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单位: [1]Affiliated Hospital of Qingdao University, Qingdao, China. [2]Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China. [3]Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, & National Clinical Research Center for Digestive Diseases, Beijing, China. [4]Beijing Hospital & National Center of Gerontology, Beijing. China. [5]Fourth Hospital of Hebei Medical University, Hebei, China. [6]First Affiliated Hospital of Zhengzhou University, Zhenzhou, China. [7]Qingdao Municipal Hospital, Qingdao, China. [8]The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
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MRI is the gold standard for confirming a pelvic lymph node metastasis diagnosis. Traditionally, medical radiologists have analyzed MRI image features of regional lymph nodes to make diagnostic decisions based on their subjective experience; this diagnosis lacks objectivity and accuracy. This study trained a faster region-based convolutional neural network (Faster RCNN) with 28,080 MRI images of lymph node metastasis, allowing the Faster R-CNN to read those images and to make diagnoses. For clinical verification, 414 cases of rectal cancer at various medical centers were collected, and Faster R-CNN-based diagnoses were compared with radiologist diagnoses using receiver operating characteristic curves (ROC). The area under the Faster R-CNN ROC was 0.912, indicating a more effective and objective diagnosis. The Faster R-CNN diagnosis time was 20 s/case, which was much shorter than the average time (600 s/case) of the radiologist diagnoses. Significance: Faster R-CNN enables accurate and efficient diagnosis of lymph node metastases. (C) 2018 AACR.

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出版当年[2017]版:
大类 | 1 区 医学
小类 | 1 区 肿瘤学
最新[2025]版:
大类 | 1 区 医学
小类 | 1 区 肿瘤学
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出版当年[2016]版:
Q1 ONCOLOGY
最新[2024]版:
Q1 ONCOLOGY

影响因子: 最新[2024版] 最新五年平均[2021-2025] 出版当年[2016版] 出版当年五年平均[2012-2016] 出版前一年[2015版] 出版后一年[2017版]

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第一作者单位: [1]Affiliated Hospital of Qingdao University, Qingdao, China. [2]Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China. [*1]Affiliated Hospital of Qingdao University, Shinan Jiangsu-road No. 16, Qingdao, Shandong 266071, China.
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通讯机构: [1]Affiliated Hospital of Qingdao University, Qingdao, China. [2]Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, China. [*1]Affiliated Hospital of Qingdao University, Shinan Jiangsu-road No. 16, Qingdao, Shandong 266071, China.
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