Hybrid high-definition microvessel imaging/shear wave elastography improves breast lesion characterization.
Hybrid high-definition microvessel imaging/shear wave elastography improves breast lesion characterization.
复制标题
混合高清晰度微血管成像/剪切波弹性成像改善了乳腺病变特征。
DOI：
10.1186/s13058-022-01511-5
复制
发表时间：
2022-03-05
期刊：
影响因子：
--
通讯作者：
Alizad A
中科院分区：
文献类型：
--
作者：
Gu J;Ternifi R;Larson NB;Carter JM;Boughey JC;Stan DL;Fazzio RT;Fatemi M;Alizad A
目前乳腺成像方式的低特异性导致不必要的随访和活检增加。本研究的目的是评价高清晰度微血管成像（HDMI）和二维横波弹性成像（SWE）定量参数与临床因素（病变深度和年龄）相结合对改善乳腺病变鉴别的疗效。在这项前瞻性研究中，从2016年6月至2021年4月，招募了诊断超声发现乳腺病变并建议进行核心针活检的患者。活检前进行HDMI和SWE检查。引入两个新的HDMI参数Murray 's deviation和分岔角，以及一个新的SWE参数mass characteristic frequency进行定量分析。随机选取70%（360/514）的数据，通过弹性网络logistic回归训练单纯基于HDMI、单纯基于SWE、HDMI与SWE联合、以及HDMI、SWE与临床因素联合的病变恶性预测模型，并用剩余30%（154/514）的数据进行验证。基于优化的阈值选择，比较不同模型在ROC曲线下的面积以及灵敏度和特异性方面的预测性能。共纳入508例患者（平均年龄54岁±15岁），其中女性507例，男性1例，可疑乳腺病变514例（范围4 ~ 72 mm，中位尺寸13 mm）。其中204例为恶性病变。合并SWE和HDMI定量参数的SWE-HDMI预测模型的AUC为0.973 (95% CI 0.95-0.99)，显著高于单独使用SWE模型或HDMI模型的预测结果。恶性概率的最佳截断值为0.25，敏感性和特异性分别为95.5%和89.7%。随着临床因素的加入，特异性进一步提高。相应的模型定义为SWE-HDMI-C预测模型，AUC为0.981 （95% CI 0.96-1.00）。SWE-HDMI- c检测模型结合了SWE估计、HDMI定量生物标志物和临床因素，大大提高了乳腺病变表征的准确性。在线版本包含补充材料，可在10.1186/s13058-022-01511-5获得。
Low specificity in current breast imaging modalities leads to increased unnecessary follow-ups and biopsies. The purpose of this study is to evaluate the efficacy of combining the quantitative parameters of high-definition microvasculature imaging (HDMI) and 2D shear wave elastography (SWE) with clinical factors (lesion depth and age) for improving breast lesion differentiation. In this prospective study, from June 2016 through April 2021, patients with breast lesions identified on diagnostic ultrasound and recommended for core needle biopsy were recruited. HDMI and SWE were conducted prior to biopsies. Two new HDMI parameters, Murray’s deviation and bifurcation angle, and a new SWE parameter, mass characteristic frequency, were included for quantitative analysis. Lesion malignancy prediction models based on HDMI only, SWE only, the combination of HDMI and SWE, and the combination of HDMI, SWE and clinical factors were trained via elastic net logistic regression with 70% (360/514) randomly selected data and validated with the remaining 30% (154/514) data. Prediction performances in the validation test set were compared across models with respect to area under the ROC curve as well as sensitivity and specificity based on optimized threshold selection. A total of 508 participants (mean age, 54 years ± 15), including 507 female participants and 1 male participant, with 514 suspicious breast lesions (range, 4–72 mm, median size, 13 mm) were included. Of the lesions, 204 were malignant. The SWE-HDMI prediction model, combining quantitative parameters from SWE and HDMI, with AUC of 0.973 (95% CI 0.95–0.99), was significantly higher than the result predicted with the SWE model or HDMI model alone. With an optimal cutoff of 0.25 for the malignancy probability, the sensitivity and specificity were 95.5% and 89.7%, respectively. The specificity was further improved with the addition of clinical factors. The corresponding model defined as the SWE-HDMI-C prediction model had an AUC of 0.981 (95% CI 0.96–1.00). The SWE-HDMI-C detection model, a combination of SWE estimates, HDMI quantitative biomarkers and clinical factors, greatly improved the accuracy in breast lesion characterization. The online version contains supplementary material available at 10.1186/s13058-022-01511-5.
登录
查看更多内容
DOI：
10.1109/tuffc.2015.007043
发表时间：
2015-09
期刊：
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子：
--
作者：
Denis M;Bayat M;Mehrmohammadi M;Gregory A;Song P;Whaley DH;Pruthi S;Chen S;Fatemi M;Alizad A
通讯作者：
Alizad A
影响因子：
10.6
作者：
Demene, Charlie;Deffieux, Thomas;Tanter, Mickael
通讯作者：
Tanter, Mickael
影响因子：
2.9
作者：
Christensen-Jeffries K;Couture O;Dayton PA;Eldar YC;Hynynen K;Kiessling F;O'Reilly M;Pinton GF;Schmitz G;Tang MX;Tanter M;van Sloun RJG
通讯作者：
van Sloun RJG
影响因子：
2.3
作者：
Du, Jing;Li, Feng-Hua;Zhu, Cai-Xia
通讯作者：
Zhu, Cai-Xia
影响因子：
2.9
作者：
Li, Lujing;Zhou, Xinchuan;Zni, Hui
通讯作者：
Zni, Hui