Lo J Y, Baker J A, Kornguth P J, Iglehart J D, Floyd C E
Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
Radiology. 1997 Apr;203(1):159-63. doi: 10.1148/radiology.203.1.9122385.
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).
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.
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%).
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.
评估人工神经网络（ANN）能否基于易于获得的医学检查结果（即根据美国放射学会乳腺影像报告和数据系统分类的乳房X线摄影检查结果以及患者年龄）预测乳腺癌的浸润情况。
在254例成年患者中，随机选取266个经活检取样的病变进行研究。其中有96个恶性病变和170个良性病变。基于9项乳房X线摄影检查结果和患者年龄，构建了一个三层反向传播网络，以预测恶性病变是原位癌还是浸润癌。
人工神经网络预测恶性病变浸润情况的受试者操作特征曲线下面积（Az）为0.91±0.03。它正确识别出了所有28例原位癌（特异性为100%）以及68例浸润癌中的48例（敏感性为71%）。
人工神经网络利用乳房X线摄影特征和患者年龄对乳腺癌的浸润情况进行了准确分类，而这些信息此前只能通过活检获取。这一知识可能有助于手术规划，并可能有助于降低不必要活检的成本和发病率。
