Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM).
Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM).
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DOI：
10.3390/diagnostics13061129
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发表时间：
2023-03-16
期刊：
影响因子：
3.6
通讯作者：
Rohde, Gustavo K.
中科院分区：
文献类型：
--
作者：
Miller, Matthew M.;Rubaiyat, Abu Hasnat Mohammad;Rohde, Gustavo K.
我们试图开发新的定量方法来表征可疑对比增强乳腺X线摄影（CEM）结果的乳腺X线摄影密度和对比度增强的空间分布，以改善乳腺病变的恶性与良性分类。我们回顾性分析了2014-2020年在IRB批准的研究中在我们机构接受CEM成像和组织采样的所有乳腺病变。基于乳腺X线密度和对比度增强的径向分布的平均直方图，使用惩罚线性判别分析对病变进行分类。t检验被用来比较密度、对比度以及连接的密度和对比度直方图的分类准确性。使用逻辑回归和AUC-ROC分析来评估添加人口统计学和临床数据是否提高了模型的准确性。共对159项可疑发现进行了评价。密度直方图在将病变分类为恶性或良性方面比随机分类器更准确（62.37% vs. 48%; p < 0.001），但连接的密度和对比度直方图显示出比单独的密度直方图更高的准确性（71.25%; p < 0.001）。在我们的模型中包括人口统计学和临床数据导致比级联密度和对比图像更高的AUC-ROC（0.81 vs. 0.70; p < 0.001）。在侵袭性与非侵袭性恶性肿瘤的分类中，与单独的密度直方图相比，串联密度和对比度直方图在准确性方面没有显著改善（77.63% vs. 78.59%; p = 0.504）。我们的研究结果表明，乳腺X线摄影密度的径向分布的定量差异可用于区分恶性和良性乳腺病变;然而，分类准确性显着提高了对比增强成像数据从CEM。添加患者人口统计学和临床信息进一步提高了分类准确性。
We sought to develop new quantitative approaches to characterize the spatial distribution of mammographic density and contrast enhancement of suspicious contrast-enhanced mammography (CEM) findings to improve malignant vs. benign classifications of breast lesions. We retrospectively analyzed all breast lesions that underwent CEM imaging and tissue sampling at our institution from 2014–2020 in this IRB-approved study. A penalized linear discriminant analysis was used to classify lesions based on the averaged histograms of radial distributions of mammographic density and contrast enhancement. T-tests were used to compare the classification accuracies of density, contrast, and concatenated density and contrast histograms. Logistic regression and AUC-ROC analyses were used to assess if adding demographic and clinical data improved the model accuracy. A total of 159 suspicious findings were evaluated. Density histograms were more accurate in classifying lesions as malignant or benign than a random classifier (62.37% vs. 48%; p < 0.001), but the concatenated density and contrast histograms demonstrated a higher accuracy (71.25%; p < 0.001) than the density histograms alone. Including the demographic and clinical data in our models led to a higher AUC-ROC than concatenated density and contrast images (0.81 vs. 0.70; p < 0.001). In the classification of invasive vs. non-invasive malignancy, the concatenated density and contrast histograms demonstrated no significant improvement in accuracy over the density histograms alone (77.63% vs. 78.59%; p = 0.504). Our findings suggest that quantitative differences in the radial distribution of mammographic density could be used to discriminate malignant from benign breast findings; however, classification accuracy was significantly improved with the addition of contrast-enhanced imaging data from CEM. Adding patient demographic and clinical information further improved the classification accuracy.
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影响因子：
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