Lo J Y, Baker J A, Kornguth P J, Floyd C E
Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
Acad Radiol. 1999 Jan;6(1):10-5. doi: 10.1016/s1076-6332(99)80056-7.
The authors evaluated the contribution of medical history data to the prediction of breast cancer with artificial neural network (ANN) models based on mammographic findings.
Three ANNs were developed: The first used 10 Breast Imaging Reporting and Data System (BI-RADS) variables; the second, the BI-RADS variables plus patient age; the third, the BI-RADS variables, patient age, and seven other history variables, for a total of 18 inputs. Performance of the ANNs and the original radiologist's impression were evaluated with five metrics: receiver operating characteristic area index (Az); specificity at given sensitivities of 100%, 98%, and 95%; and positive predictive value.
All three ANNs consistently outperformed the radiologist's impression over all five performance metrics. The patient-age variable was particularly valuable. Adding the age variable to the basic ANN model, which used only the BI-RADS findings, significantly improved Az (P = .028). In fact, replacing all history data with just the age variable resulted in virtually no changes for Az or specificity at 98% sensitivity (P = .324 and P = .410, respectively).
Patient age was an important variable for the prediction of breast cancer from mammographic findings with the ANNs. For this data set, all history data could be replaced with age alone.
作者基于乳腺钼靶检查结果，评估了病史数据对人工神经网络（ANN）模型预测乳腺癌的贡献。
开发了三个人工神经网络：第一个使用10个乳腺影像报告和数据系统（BI-RADS）变量；第二个使用BI-RADS变量加患者年龄；第三个使用BI-RADS变量、患者年龄和其他七个病史变量，总共18个输入变量。使用五个指标评估人工神经网络和放射科医生的原始诊断结果的性能：受试者操作特征曲线下面积指数（Az）；在给定敏感性为100%、98%和95%时的特异性；以及阳性预测值。
在所有五个性能指标上，所有三个人工神经网络的表现均始终优于放射科医生的诊断结果。患者年龄变量特别有价值。将年龄变量添加到仅使用BI-RADS检查结果的基本人工神经网络模型中，显著提高了Az（P = 0.028）。事实上，仅用年龄变量取代所有病史数据，在敏感性为98%时，Az或特异性几乎没有变化（分别为P = 0.324和P = 0.410）。
患者年龄是通过人工神经网络从乳腺钼靶检查结果预测乳腺癌的一个重要变量。对于该数据集，所有病史数据都可以仅用年龄来取代。
