Classification of signal-time curves from dynamic MR mammography by neural networks
Classification of signal-time curves from dynamic MR mammography by neural networks
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DOI：
10.1016/s0730-725x(01)00222-3
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发表时间：
2001-01-01
影响因子：
2.5
通讯作者：
Brix, G
中科院分区：
文献类型：
--
作者：
Lucht, REA;Knopp, MV;Brix, G
本研究的目的是测试人工神经网络对动态MRI乳房肿块信号时间曲线的分类性能。我们检查了105个实质组织、162个恶性组织和102个良性组织的信号时程。后两组经组织病理学证实。对信号时间曲线不同时间分辨率下的四种神经网络进行了测试。分辨率范围从28次测量（时间间隔为23秒）到仅3次测量（在造影剂施用后1.8分钟、3分钟和10分钟）。使用28个测量点（灵敏度：84%，特异性：81%）对良恶性病变的鉴别效果最好。使用三个测量点的结果是78%的灵敏度和76%的特异性。这些结果与人类专家在不考虑附加形态学信息的情况下通过视觉评估信号时间曲线获得的值相对应。ALL检查网络对良性病变为纤维腺瘤和良性增生性改变的亚分类结果不佳。即使只进行少量的对比后测量，神经网络也能在计算上快速区分恶性和良性病变。良性病变类型的更精确的说明将需要结合额外的形态学或药代动力学信息。(C) 2001爱思唯尔科学公司版权所有。
The aim of this study was to test the performance of artificial neural networks for the classification of signal-time curves obtained from breast masses by dynamic MRI. Signal-time courses from 105 parenchyma, 162 malignant, and 102 benign tissue regions were examined. The latter two groups were histopathologically verified. Four neural networks corresponding to different temporal resolutions of the signal-time curves were tested. The resolution ranges from 28 measurements with a temporal spacing of 23s to just 3 measurements taken 1.8, 3, and 10 minutes after contrast medium administration. Discrimination between malignant and benign lesions is best if 28 measurement points are used (sensitivity: 84%, specificity: 81%). The use of three measurement points results in 78% sensitivity and 76% specificity. These results correspond to values obtained by human experts who visually evaluated signal-time curves without considering additional morphologic information. ALL examined networks yielded poor results for the subclassification of the benign lesions into fibroadenomas and benign proliferative changes. Neural networks can computationally fast distinguish between malignant and benign lesions even when only a few post-contrast measurements are made. More precise specification of the type of the benign lesion will require incorporation of additional morphological or pharmacokinetic information. (C) 2001 Elsevier Science Inc. All rights reserved.