目的:本研究旨在提高使用gadobutrot的对比增强乳腺磁共振成像(MRI)对乳腺良性病变和恶性病变的鉴别诊断准确性。此外,本研究旨在解决当前基于乳腺影像报告和数据系统(BI-RADS)的成像技术和标准的局限性.
方法:在日本进行的一项多中心回顾性研究中,包括200名妇女,包括100个良性病变和100个恶性病变,全部归类为BI-RADS类别3和4。MRI协议包括具有脂肪抑制的3D快速梯度回波T1加权图像,用Gadobutrol作为造影剂。分析包括评估患者和病变特征,包括年龄,尺寸,location,纤维腺体组织,背景实质增强(BPE),信号强度,以及质量和非质量增强的发现。在这项研究中,进行了单变量和多变量逻辑回归分析,连同决策树分析,确定病变分类的重要预测因子。
结果:确定了病变特征的差异,这可能会影响恶性肿瘤的风险。多变量逻辑回归模型显示年龄,病变位置,形状,和信号强度是恶性肿瘤的重要预测因子。决策树分析确定了额外的诊断因素,包括病变边缘和BPE水平。决策树模型显示出很高的诊断准确性,逻辑回归模型显示质量曲线下面积为0.925,非质量增强曲线下面积为0.829。
结论:本研究强调了整合患者年龄的重要性,病变位置,并将BPE水平纳入BI-RADS标准,提高乳腺良恶性病变的鉴别。这种方法可以最大限度地减少不必要的活检,并增强乳腺癌诊断的临床决策。强调gadobutrol在乳腺MRI评估中的有效性。
OBJECTIVE: This study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS).
METHODS: In a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions.
RESULTS: Differences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements.
CONCLUSIONS: This study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations.