多模态超声成像在乳腺影像报告和数据系统 4 类乳腺病变中的应用：恶性肿瘤预测模型。
Multimodal Ultrasound Imaging in Breast Imaging-Reporting and Data System 4 Breast Lesions: A Prediction Model for Malignancy.
机构信息
Department of Medical Ultrasound, Shanghai 10th People's Hospital, Ultrasound Research and Education Institute, Tongji University Cancer Center, Tongji University School of Medicine, Shanghai, China; Thyroid Institute, Tongji University School of Medicine, Shanghai, China; Shanghai Center for Thyroid Diseases, Shanghai, China.
Department of Medical Ultrasound, Shanghai 10th People's Hospital, Ultrasound Research and Education Institute, Tongji University Cancer Center, Tongji University School of Medicine, Shanghai, China; Thyroid Institute, Tongji University School of Medicine, Shanghai, China; Shanghai Center for Thyroid Diseases, Shanghai, China.
出版信息
Ultrasound Med Biol. 2020 Dec;46(12):3188-3199. doi: 10.1016/j.ultrasmedbio.2020.08.003. Epub 2020 Sep 4.
The purpose of this study was to develop, validate and test a prediction model for discriminating malignant from benign breast lesions using conventional ultrasound (US), US elastography of strain elastography and contrast-enhanced ultrasound (CEUS). The study included 454 patients with breast imaging-reporting and data system (BI-RADS) category 4 breast lesions identified on histologic examinations. Firstly, 228 breast lesions (cohort 1) were analyzed by logistic regression analysis to identify the risk factors, and a breast malignancy prediction model was created. Secondly, the prediction model was validated in cohort 2 (84 patients) and tested in cohort 3 (142 patients) by using analysis of the area under the receiver operating characteristic curve (AUC). Univariate regression indicated that age ≥40 y, taller than wide shape on US, early hyperenhancement on CEUS and enlargement of enhancement area on CEUS were independent risk factors for breast malignancy (all p < 0.05). The logistic regression equation was established as follows: p = 1/1+Exp∑[-5.066 + 3.125 x (if age ≥40 y) + 1.943 x (if taller than wide shape) + 1.479 x (if early hyperenhancement) + 4.167 x (if enlargement of enhancement area). The prediction model showed good discrimination performance with an AUC of 0.967 in cohort 1, 0.948 in cohort 2 and 0.920 in cohort 3. By using the prediction model to selectively downgrade category 4a lesions, the re-rated BI-RADS yield an AUC of 0.880 (95% confidence interval [CI], 0.794-0.965) in cohort 2 and 0.870 (95% CI, 0.801-0.939) in cohort 3. The specificity increased from 0.0% (0/35) to 80.0% (28/35) without loss of sensitivity (from 100.0% to 95.9%, p = 0.153) in cohort 2. Similarly, the specificity increased from 0.0% (0/58) to 77.6% (45/58) without loss of sensitivity (from 100.0% to 96.4%, p = 0.081) in cohort 3. Multimodal US showed good diagnostic performance in predicting breast malignancy of BI-RADS category 4 lesions. Although the loss of sensitivity was existing, the addition of multimodal US to US BI-RADS could improve the specificity in BI-RADS category 4 lesions, which reduced unnecessary biopsies.
本研究旨在开发、验证和测试一种使用常规超声（US）、应变弹性成像和对比增强超声（CEUS）鉴别乳腺良恶性病变的预测模型。该研究纳入了 454 例经组织学检查证实为乳腺影像报告和数据系统（BI-RADS）4 类乳腺病变的患者。首先，对 228 例乳腺病变（队列 1）进行逻辑回归分析，以确定风险因素，并建立乳腺恶性肿瘤预测模型。其次，在队列 2（84 例）和队列 3（142 例）中使用受试者工作特征曲线（ROC）下面积（AUC）分析验证预测模型。单因素回归分析表明，年龄≥40 岁、US 上呈 taller than wide 形状、CEUS 早期强化和 CEUS 强化面积增大是乳腺恶性肿瘤的独立危险因素（均 P<0.05）。逻辑回归方程如下：p=1/1+Exp∑[-5.066+3.125x（年龄≥40 岁）+1.943x（taller than wide 形状）+1.479x（早期强化）+4.167x（强化面积增大）]。该预测模型在队列 1 中的 AUC 为 0.967，在队列 2 中的 AUC 为 0.948，在队列 3 中的 AUC 为 0.920，具有良好的判别性能。使用预测模型选择性降低 4a 类病变的 BI-RADS 分级，重新评级的 BI-RADS 在队列 2 中的 AUC 为 0.880（95%置信区间[CI]：0.794-0.965），在队列 3 中的 AUC 为 0.870（95%CI：0.801-0.939）。在队列 2 中，特异性从 0.0%（0/35）增加到 80.0%（28/35），而敏感性没有降低（从 100.0%降至 95.9%，P=0.153）；在队列 3 中，特异性从 0.0%（0/58）增加到 77.6%（45/58），而敏感性没有降低（从 100.0%降至 96.4%，P=0.081）。多模态超声在预测 BI-RADS 4 类病变的乳腺恶性肿瘤方面具有良好的诊断性能。虽然敏感性有所降低，但在 US BI-RADS 中增加多模态 US 可以提高 BI-RADS 4 类病变的特异性，从而减少不必要的活检。
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