Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer.
Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer.
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DOI：
10.3390/diagnostics10090631
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发表时间：
2020-08-25
期刊：
影响因子：
--
通讯作者：
Sehgal CM
中科院分区：
文献类型：
--
作者：
Moustafa AF;Cary TW;Sultan LR;Schultz SM;Conant EF;Venkatesh SS;Sehgal CM
彩色多普勒在临床上用于超声对乳腺肿块血管的直观评估，以帮助确定恶性的可能性。在这项研究中，通过算法从乳腺超声图像中提取定量彩色多普勒放射组学特征用于机器学习，生成了一个比基于BI-RADSUS(BI-RADSUS)的灰度和临床类别的模型更高性能的乳腺癌诊断模型。对159例实性肿块的超声图像进行了分析。算法提取了9个灰度特征和2个彩色多普勒特征。这些特征，连同患者年龄和BI-RADSUS类别，被用来训练AdaBoost集成分类器。虽然计算机提取的灰度特征和彩色多普勒特征的训练都显著提高了根据临床特征训练的模型的性能，但根据受试者工作特征曲线下的面积来衡量，彩色多普勒和灰度特征训练进一步增加了受试者的受试者工作特征曲线下的面积，从0.925±0.022增加到0.958±0.013。在没有彩色多普勒的情况下，修剪20%的低置信度病例将这一指标提高到0.986±0.007，敏感性为100%，而必须修剪%的病例才能达到这一性能。通过对彩色多普勒特征的机器学习，乳腺癌超声诊断模型获得了更少的边界诊断和更高的ROC性能。
Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.
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影响因子：
19.7
作者：
Kolb, TM;Lichy, J;Newhouse, JH
通讯作者：
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2.3
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254.7
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