Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set
Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set
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DOI：
10.1148/radiol.2018180608
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发表时间：
2019-03-01
期刊：
影响因子：
19.7
通讯作者：
Shepherd, John
中科院分区：
文献类型：
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作者：
Drukker, Karen;Giger, Maryellen L.;Shepherd, John
目的：探讨乳腺X线摄影放射组学和双能量乳腺X线摄影的定量三腔室乳腺（3CB）图像分析的结合，以限制不必要的良性乳腺活检。材料和方法：对于这项前瞻性研究，双能量头尾位和内外侧斜位乳腺X线摄影在109名妇女活检前立即获得（平均年龄，51岁;范围，31-85岁），2013年至2017年，乳腺成像报告和数据系统4或5类乳腺肿块（35例浸润性癌症，74例良性）。通过使用图像内体模，从高能量和低能量下的衰减计算每个像素处的水、脂质和蛋白质厚度的三个定量隔室。肿块被自动分割，并从低能量乳腺X线照片和定量分区图像中提取特征。使用具有预定义特征签名的线性判别分类器的十倍交叉验证有助于通过（a）单独的水-脂质-蛋白质组合物图像，（B）单独的乳房X线摄影放射组学，和（c）两者的组合图像分析来区分恶性和良性肿块。在最大灵敏度下进行活检的阳性预测值（PPV 3）是主要的性能指标，并将结果与常规诊断数字乳腺X线摄影的结果进行比较。结果：在我们的数据集中，常规诊断数字乳腺X线摄影的PPV 3为32.1%（109例中的35例; 95%置信区间[CI]：23.9%，41.3%），灵敏度为100%。相比之下，乳腺X线摄影放射组学联合定量3CB图像分析的PPV 3为49%。（34/70; 95% CI：36.5%，58.9%; P < .001），灵敏度为97%（34/35; 95%CI：90.3%，100%; P < .001）和35.8%（39/109）的总活检次数减少（P < .001）。结论：乳腺肿块的定量三室乳腺图像分析结合乳腺X线摄影放射组学具有减少不必要的乳腺活检的潜力。(C)RSNA，2018
Purpose: To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies.Materials and Methods: For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV3) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography.Results: The PPV3 for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV3 of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001).Conclusion: Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. (C) RSNA, 2018