基于乳腺 MRI 的纹理分析在鉴别绝经前妇女良性和恶性非肿块样强化中的附加价值。
Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women.
机构信息
Department of Radiology, Guangdong Women and Children Hospital, No.521, Xingnan Road, Panyu District, Guangzhou, 511400, China.
Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
出版信息
BMC Med Imaging. 2021 Mar 12;21(1):48. doi: 10.1186/s12880-021-00571-x.
BACKGROUND
Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women.
METHODS
Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson's chi-square test and Mann-Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis.
RESULTS
The combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising.
CONCLUSIONS
With the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.
背景
非肿块样强化（NME）是一种诊断难题，高度依赖于放射科医生的经验。纹理分析（TA）可以作为量化病变特征的客观方法。然而，基于常规 MRI 特征的预测模型中 TA 扮演什么角色仍不清楚。本研究旨在探讨 TA 在鉴别绝经前妇女良恶性 NME 中的作用。
方法
回顾性纳入经组织学证实的 NME 患者（良性：58 例；恶性：89 例）。然后，将 102 例和 45 例患者分别分为训练组和验证组。扫描序列包括脂肪抑制 T2 加权和脂肪抑制对比增强 T1 加权，均在 1.5T MRI 系统上采集。两名放射科医生根据乳腺影像报告和数据系统（2013 年）评估临床和常规磁共振特征（CRMC）。在训练组中，从所有增强后序列中提取纹理特征。建立组合模型，然后在验证组中进行评估。采用 Pearson χ2 检验和 Mann-Whitney U 检验分别比较分类变量和连续变量。采用逻辑回归分析和受试者工作特征曲线评估 CRMC、TA 及其组合模型在 NME 诊断中的诊断性能。
结果
与 CRMC 或 TA 单独相比，组合模型在鉴别良恶性 NME 方面表现出更好的诊断性能（AUC：0.887 比 0.832 比 0.74）。此外，与 CRMC 相比，该模型具有较高的特异性（72.5%比 80%）。验证组的结果证实了该模型的前景。
结论
TA 和 CRMC 的联合使用可以提高鉴别良恶性 NME 的诊断性能。
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