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首页> 外文期刊>Journal of Digital Imaging >A Comprehensive Methodology for Determining the Most Informative Mammographic Features
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A Comprehensive Methodology for Determining the Most Informative Mammographic Features

机译：确定最翔实的乳腺X线摄影特征的综合方法

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This study aims to determine the most informative mammographic features for breast cancer diagnosis using mutual information (MI) analysis. Our Health Insurance Portability and Accountability Act-approved database consists of 44,397 consecutive structured mammography reports for 20,375 patients collected from 2005 to 2008. The reports include demographic risk factors (age, family and personal history of breast cancer, and use of hormone therapy) and mammographic features from the Breast Imaging Reporting and Data System lexicon. We calculated MI using Shannon’s entropy measure for each feature with respect to the outcome (benign/malignant using a cancer registry match as reference standard). In order to evaluate the validity of the MI rankings of features, we trained and tested naïve Bayes classifiers on the feature with tenfold cross-validation, and measured the predictive ability using area under the ROC curve (AUC). We used a bootstrapping approach to assess the distributional properties of our estimates, and the DeLong method to compare AUC. Based on MI, we found that mass margins and mass shape were the most informative features for breast cancer diagnosis. Calcification morphology, mass density, and calcification distribution provided predictive information for distinguishing benign and malignant breast findings. Breast composition, associated findings, and special cases provided little information in this task. We also found that the rankings of mammographic features with MI and AUC were generally consistent. MI analysis provides a framework to determine the value of different mammographic features in the pursuit of optimal (i.e., accurate and efficient) breast cancer diagnosis.
机译：这项研究旨在确定使用相互信息（MI）分析进行乳腺癌诊断的信息最丰富的X线摄影特征。我们的健康保险可移植性和责任法案批准的数据库包含从2005年至2008年收集的20,375例患者的4,34397份连续的结构化乳腺X线照片报告。这些报告包括人口统计学风险因素（年龄，乳腺癌的家庭和个人病史以及激素治疗的使用）和乳房成像报告和数据系统词典提供的乳房X光检查功能。我们使用Shannon熵测度针对每个特征相对于结局（以癌症登记匹配为参考的良性/恶性）计算MI。为了评估特征MI等级的有效性，我们使用十倍交叉验证对特征上的朴素贝叶斯分类器进行了训练和测试，并使用ROC曲线下的面积（AUC）测量了预测能力。我们使用自举方法评估我们估计的分布特性，并使用DeLong方法比较AUC。基于MI，我们发现肿块边缘和肿块形状是诊断乳腺癌最有用的特征。钙化形态，质量密度和钙化分布为区分良性和恶性乳腺发现提供了预测信息。乳房组成，相关发现和特殊病例在这项任务中提供的信息很少。我们还发现，MI和AUC的乳腺特征的排名通常是一致的。 MI分析提供了一个框架，可确定追求最佳（即准确和高效）乳腺癌诊断的不同乳腺钼靶特征的价值。

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