定量集成磁共振成像鉴别乳腺良恶性病变

目的 探讨定量集成磁共振成像（SyMRI）在乳腺良恶性病变鉴别诊断中的应用价值。方法 回顾性分析经病理证实的95例乳腺病变患者的磁共振成像（MRI）资料。患者均于术前行乳腺常规MRI和SyMRI扫描，病灶动态增强磁共振成像（DCEMRI）均显示乳腺肿块样病变。根据病理结果将病例分为良性组和恶性组，分析病变的MRI征象，同时测量病变增强前后的SyMRI定量参数，分别记录为T1p、T2p、PDp和T1e、T2e、PDe，并计算各定量参数的相对变化率。基于全子集回归和Logistic回归构建不同的诊断模型，通过受试者工作特征曲线（ROC）评价不同诊断模型鉴别乳腺良恶性病变的效能。结果 乳腺良性和恶性病变的MRI征象（形态、边界、内部强化方式、时间-信号强度曲线和T2WI信号特征）差异具有统计学意义（P<0.05）。除PDp和T2e外，其他定量参数在两组间的差异均有统计学意义（P<0.05）。“DCE-MRI+SyMRI (DCE-MRI+T2p+T1e)”的诊断模型是最佳的预测模型，其AUC为0.995（95% CI：0.983~1.000）。结论 定量SyMRI的衍生定量参数可以间接反映乳腺病变组织的微结构特征，有助于乳腺良恶性病变的鉴别诊断。

Differential diagnosis of benign and malignant breast lesions using quantitative synthetic magnetic resonance imaging

Objective To investigate the value of quantitative synthetic magnetic resonance imaging (SyMRI) in distinguishing between benign and malignant breast lesions. Methods We retrospectively collected data of preoperative conventional MRI and multi-dynamic multi-echo sequences from 95 patients with breast lesions showing mass-type enhancement on DCE-MRI, including 27 patients with benign lesions and 68 with malignant lesions. The MRI features of the lesions (shape, margin, internal enhancement pattern, time-signal intensity curve, and T2WI signal) were analyzed, and for each lesion, SyMRI-generated quantitative parameters including T1 and T2 relaxation time and proton density (PD) were measured before and after enhancement and recorded as T1p, T2p, PDp and T1e, T2e, and PDe, respectively. The relative change rate of each parameter was calculated. Logistic regression and all-subset regression analyses were performed for variable selection to construct diagnostic models of the breast lesions, and receiver-operating characteristic (ROC) analysis was used to assess the performance of each model for differentiation of benign and malignant lesions. Results There were significant differences in the MRI features between benign and malignant lesions (P<0.05). All the SyMRI-generated quantitative parameters, with the exception of T2e and Pdp, showed significant differences between benign and malignant lesions (P<0.05). Among the constructed diagnostic models, the model based on all the DCE-MRI features combined with SyMRI parameters T2p and T1e (DCE-MRI+T2p+T1e) showed the best performance in the differential diagnosis malignant breast masses with an AUC of 0.995 (95% CI: 0.983-1.000). Conclusion Quantitative SyMRI can be used for differential diagnosis of benign and malignant breast lesions.