Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.
Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.
复制标题
根据乳房X线照相特征用人工神经网络预测乳腺癌侵袭。
DOI：
10.1148/radiology.203.1.9122385
复制
发表时间：
1997
期刊：
影响因子：
19.7
通讯作者：
C. E. Floyd
中科院分区：
文献类型：
--
作者：
J. Y. Lo;J. A. Baker;P. Kornguth;J D Iglehart;C. E. Floyd
目的
评估人工神经网络（ANN）是否可以根据现有的医学发现（即根据美国放射学会乳腺成像报告和数据系统和患者年龄分类的乳腺X线检查结果）预测乳腺癌侵袭。
材料和方法
在254名成人患者中，随机选择了266个活检时取样的病变进行研究。恶性病变96例，良性病变170例。根据9例乳腺X线检查结果和患者年龄，开发了一个三层反向传播网络来预测恶性病变是原位的还是浸润性的。
结果
人工神经网络预测恶性病变的浸润，受试者工作特征曲线（Az）下的面积为0.91 +/-0.03。它正确识别了所有28种原位癌（特异性，100%）和68种浸润性癌中的48种（敏感性，71%）。
结论
人工神经网络使用乳房X线特征和患者年龄来准确地对乳腺癌的浸润进行分类，这些信息以前只能通过活检获得。这方面的知识可能有助于手术计划，并可能有助于减少不必要的活检的成本和发病率。
PURPOSE
To evaluate whether an artificial neural network (ANN) can predict breast cancer invasion on the basis of readily available medical findings (ie, mammographic findings classified according to the American College of Radiology Breast Imaging Reporting and Data System and patient age).
MATERIALS AND METHODS
In 254 adult patients, 266 lesions that had been sampled at biopsy were randomly selected for the study. There were 96 malignant and 170 benign lesions. On the basis of nine mammographic findings and patient age, a three-layer backpropagation network was developed to predict whether the malignant lesions were in situ or invasive.
RESULTS
The ANN predicted invasion among malignant lesions with an area under the receiver operating characteristic curve (Az) of .91 +/- .03. It correctly identified all 28 in situ cancers (specificity, 100%) and 48 of 68 invasive cancers (sensitivity, 71%).
CONCLUSION
The ANN used mammographic features and patient age to accurately classify invasion among breast cancers, information that was previously available only by means of biopsy. This knowledge may assist in surgical planning and may help reduce the cost and morbidity of unnecessary biopsy.