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轴向剪切应变弹性成像在乳腺病变分类中的应用:来自体内数据的进一步结果。

Axial-shear strain elastography for breast lesion classification: further results from in vivo data.

机构信息

Department of Diagnostic and Interventional Imaging, The University of Texas Medical School, Houston, TX 77030, USA.

出版信息

Ultrasound Med Biol. 2011 Feb;37(2):189-97. doi: 10.1016/j.ultrasmedbio.2010.11.001. Epub 2011 Jan 5.

Abstract

The purpose of this work was to investigate the potential of the normalized axial-shear strain area (NASSA) feature, derived from axial-shear strain elastograms (ASSE), for breast lesion classification of fibroadenoma and cancer. This study consisted of previously acquired in vivo digital radiofrequency data of breast lesions. A total of 33 biopsy-proven malignant tumors and 30 fibroadenoma cases were included in the study, which involved three observers blinded to the original BIRADS-ultrasound scores. The observers outlined the lesions on the sonograms. The ASSEs were segmented and color-overlaid on the sonograms, and the NASSA feature from the ASSE was computed semi-automatically. Receiver operating characteristic (ROC) curves were then generated and the area under the curve (AUC) was calculated for each observer performance. A logistic regression classifier was built to compare the improvement in the AUC when using BIRADS scores plus NASSA values as opposed to BIRADS scores alone. BIRADS score ROC had an AUC of 0.89 (95% CI = 0.81 to 0.97). In comparison, the average of the AUC for all the three observers using ASSE feature alone was 0.84. However, the AUC increased to 0.94 (average of 3 observers) when BIRADS score and ASSE feature were combined. The results demonstrate that the NASSA feature derived from ASSE has the potential to improve BIRADS breast lesion classification of fibroadenoma and malignant tumors.

摘要

本研究旨在探讨轴向剪切应变面积(NASSA)特征在乳腺病变纤维腺瘤和癌症分类中的应用价值。该研究纳入了先前获得的乳腺病变的体内数字射频数据。共有 33 例经活检证实的恶性肿瘤和 30 例纤维腺瘤病例纳入研究,3 名观察者对原始 BI-RADS 超声评分不知情。观察者在超声图像上描绘病变。对 ASSE 进行分割并在超声图像上叠加颜色,然后半自动计算 ASSE 中的 NASSA 特征。然后生成受试者工作特征(ROC)曲线,并计算每个观察者的曲线下面积(AUC)。构建逻辑回归分类器,比较使用 BI-RADS 评分加 NASSA 值与仅使用 BI-RADS 评分时 AUC 的改善情况。BI-RADS 评分的 ROC 曲线 AUC 为 0.89(95%CI=0.81~0.97)。相比之下,仅使用 ASSE 特征时,3 名观察者的 AUC 平均值为 0.84。然而,当 BI-RADS 评分和 ASSE 特征结合使用时,AUC 增加至 0.94(3 名观察者的平均值)。结果表明,ASSE 衍生的 NASSA 特征有可能改善 BI-RADS 乳腺病变纤维腺瘤和恶性肿瘤的分类。

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