BREAST-CANCER - PREDICTION WITH ARTIFICIAL NEURAL-NETWORK-BASED ON BI-RADS STANDARDIZED LEXICON
BREAST-CANCER - PREDICTION WITH ARTIFICIAL NEURAL-NETWORK-BASED ON BI-RADS STANDARDIZED LEXICON
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DOI：
10.1148/radiology.196.3.7644649
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发表时间：
1995-09-01
期刊：
影响因子：
19.7
通讯作者：
FLOYD, CE
中科院分区：
文献类型：
--
作者：
BAKER, JA;KORNGUTH, PJ;FLOYD, CE
目的：确定用于乳腺良恶性病变分类的人工神经网络(ANN)是否可以标准化，以供所有放射科医生使用。材料和方法：根据美国放射学会乳腺影像记录和数据系统(BI-RADS)的标准化词典构建人工神经网络。该网络的18个输入值包括10个BI-RADS病变描述符和8个来自患者病史的输入值。对206例患者(良性133例，恶性73例)进行了训练和测试。结果：在指定的输出阈值下，人工神经网络可以将活检的阳性预测值(PPV)从35%提高到61%，相对敏感度为100%。当灵敏度固定为95%时，ANN的特异度(62%)显著高于放射科医师的特异度(30%)(P<；.01)。结论：BI-RADS词典提供了一种标准化的语言，可提高乳房活检的PPV。
PURPOSE: To determine if an artificial neural network (ANN) to categorize benign and malignant breast lesions can be standardized for use by all radiologists.MATERIALS AND METHODS: An ANN was constructed based on the standardized lexicon of the Breast Imaging Recording and Data System (BI-RADS) of the American College of Radiology. Eighteen inputs to the network included 10 BI-RADS lesion descriptors and eight input values from the patient's medical history. The network was trained and tested on 206 cases (133 benign, 73 malignant cases). Receiver operating characteristic curves for the network and radiologists were compared.RESULTS: At a specified output threshold, the ANN would have improved the positive predictive value (PPV) of biopsy from 35% to 61% with a relative sensitivity of 100%. At a fixed sensitivity of 95%, the specificity of the ANN (62%) was significantly greater than the specificity of radiologists (30%) (P < .01).CONCLUSION: The BI-RADS lexicon provides a standardized language between mammographers and an ANN that can improve the PPV of breast biopsy.