Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks
Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks
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DOI：
10.1158/0008-5472.can-18-0494
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发表时间：
2018-09-01
期刊：
影响因子：
11.2
通讯作者：
Yang, Shujian
中科院分区：
文献类型：
--
作者：
Lu, Yun;Yu, Qiyue;Yang, Shujian
MRI是确定盆腔淋巴结转移诊断的金标准。传统上，医学放射科医生分析区域淋巴结的MRI图像特征，根据他们的主观经验做出诊断决策，这种诊断缺乏客观性和准确性。该研究使用28，080张淋巴结转移的MRI图像训练了一个更快的基于区域的卷积神经网络（Faster RCNN），使Faster R-CNN能够读取这些图像并进行诊断。为了进行临床验证，收集了各个医疗中心的414例直肠癌病例，并将基于Faster R-CNN的诊断与使用受试者工作特征曲线（ROC）的放射科医生诊断进行了比较。Faster R-CNN ROC下的面积为0.912，表明诊断更有效和客观。Faster R-CNN的诊断时间为20 s/例，远短于放射科医生的平均诊断时间（600 s/例）。意义：Faster R-CNN能够准确高效地诊断淋巴结转移。(C)2018年AACR。
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.