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首页> 外文会议>International Workshop on Breast Imaging >Three compartment breast machine learning model for improving computer-aided detection
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Three compartment breast machine learning model for improving computer-aided detection

机译：三室式乳房机器学习模型，用于改善计算机辅助检测

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Our purpose was to determine if the lipid, water, and protein lesion composition (3CB), combined with computer-aided detection (CAD) had higher biopsy malignancy specificity than CAD alone. High and low-kVp full-field digital 3CB mammograms were acquired on women with suspicious mammographic lesions (BIRADS 4) and that were to undergo biopsy. Radiologists delineated 673 lesions (98 invasive ductal cancers (IDC), 60 ductal carcinomas in situ (DCIS), 103 fibroadenomata (FA), and 412 benign (BN)) on the diagnostic mammograms using the pathology report to confirm location. The diagnostic mammograms were processed by iCAD SecondLook software using its most sensitive setting to create to further delineations and probabilities of malignancy. The iCAD delineated a total of 375 annotation agreeing regions that were classified as either masses or calcification cluster. The 3CB algorithm produced lipid, water, and protein thickness maps for all ROIs and peripheral rings from which 84 compositional input features were derived. A neural network (3CBNN) was trained with cross-validation on 80% of the data to predict the lesion type. Biopsy pathology served as the gold standard outcome. IDC and DCIS predicted probabilities were summed together to obtain a probability of malignancy which was evaluated against the iCAD probabilities using the area under the ROC curves. On a holdout test set, 20% of the data, the iCAD's output alone had an AUC of 0.61 while the 3CBNN's AUC was 0.73. We conclude that compositional information provided by the 3CB algorithm contains important diagnostic information that can increase specificity of CAD software.
机译：我们的目的是确定脂质，水和蛋白质病变组合物（3CB）与计算机辅助检测（CAD）组合是否比单独的CAD具有更高的活检恶性特异性。高和低kVp全场数字3CB乳房X线照片是在有可疑乳房X线摄影病灶（BIRADS 4）并且要进行活检的妇女身上获得的。放射科医生使用病理报告确定了位置，在诊断的X线照片上划定了673个病变（98个浸润性导管癌（IDC），60个原位导管癌（DCIS），103个纤维腺瘤（FA）和412良性（BN））。诊断性乳房X线照片由iCAD SecondLook软件使用其最敏感的设置进行处理，以进一步描述恶性肿瘤的可能性和可能性。 iCAD描绘了总共375个注释一致区域，这些区域被分类为肿块或钙化簇。 3CB算法生成了所有ROI和外围环的脂质，水和蛋白质厚度图，并从中推导出了84个成分输入特征。通过对80％的数据进行交叉验证来训练神经网络（3CBNN），以预测病变类型。活检病理学是金标准的结果。将IDC和DCIS的预测概率相加，得出恶性概率，并使用ROC曲线下的面积针对iCAD概率进行评估。在保留测试集（数据的20％）上，仅iCAD的输出的AUC为0.61，而3CBNN的AUC为0.73。我们得出结论，由3CB算法提供的成分信息包含重要的诊断信息，可以增加CAD软件的特异性。

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