Sun Shi Yun, Ding Yingying, Li Zhuolin, Nie Lisha, Liao Chengde, Liu Yifan, Zhang Jia, Zhang Dongxue
Department of Radiology, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China.
Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing, China.
Front Oncol. 2021 Oct 15;11:699127. doi: 10.3389/fonc.2021.699127. eCollection 2021.
To evaluate the value of synthetic magnetic resonance imaging (syMRI), diffusion-weighted imaging (DWI), DCE-MRI, and clinical features in breast imaging-reporting and data system (BI-RADS) 4 lesions, and develop an efficient method to help patients avoid unnecessary biopsy.
A total of 75 patients with breast diseases classified as BI-RADS 4 (45 with malignant lesions and 30 with benign lesions) were prospectively enrolled in this study. T1-weighted imaging (T1WI), T2WI, DWI, and syMRI were performed at 3.0 T. Relaxation time (T1 and T2), apparent diffusion coefficient (ADC), conventional MRI features, and clinical features were assessed. "T" represents the relaxation time value of the region of interest pre-contrast scanning, and "T+" represents the value post-contrast scanning. The rate of change in the T value between pre- and post-contrast scanning was represented by ΔT%.
ΔT1%, T2, ADC, age, body mass index (BMI), menopause, irregular margins, and heterogeneous internal enhancement pattern were significantly associated with a breast cancer diagnosis in the multivariable logistic regression analysis. Based on the above parameters, four models were established: model 1 (BI-RADS model, including all conventional MRI features recommended by BI-RADS lexicon), model 2 (relaxation time model, including ΔT1% and T2), model 3 [multi-parameter (mp)MRI model, including ΔT1%, T2, ADC, margin, and internal enhancement pattern], and model 4 (combined image and clinical model, including ΔT1%, T2, ADC, margin, internal enhancement pattern, age, BMI, and menopausal state). Among these, model 4 has the best diagnostic performance, followed by models 3, 2, and 1.
The mpMRI model with DCE-MRI, DWI, and syMRI is a robust tool for evaluating the malignancies in BI-RADS 4 lesions. The clinical features could further improve the diagnostic performance of the model.
评估合成磁共振成像(syMRI)、扩散加权成像(DWI)、动态对比增强磁共振成像(DCE-MRI)以及临床特征在乳腺影像报告和数据系统(BI-RADS)4类病变中的价值,并开发一种有效的方法帮助患者避免不必要的活检。
本研究前瞻性纳入了75例被分类为BI-RADS 4类的乳腺疾病患者(45例为恶性病变,30例为良性病变)。在3.0 T条件下进行T1加权成像(T1WI)、T2加权成像(T2WI)、DWI和syMRI检查。评估弛豫时间(T1和T2)、表观扩散系数(ADC)、传统MRI特征以及临床特征。“T”代表对比剂前扫描感兴趣区域的弛豫时间值,“T+”代表对比剂后扫描的值。对比剂前、后扫描T值的变化率用ΔT%表示。
在多变量逻辑回归分析中,ΔT1%、T2、ADC、年龄、体重指数(BMI)、绝经状态、边缘不规则以及内部强化不均匀模式与乳腺癌诊断显著相关。基于上述参数,建立了四个模型:模型1(BI-RADS模型,包括BI-RADS词典推荐的所有传统MRI特征)、模型2(弛豫时间模型,包括ΔT1%和T2)、模型3[多参数(mp)MRI模型,包括ΔT1%、T2、ADC、边缘和内部强化模式]以及模型4(影像与临床联合模型,包括ΔT1%、T2、ADC、边缘、内部强化模式、年龄、BMI和绝经状态)。其中,模型4具有最佳的诊断性能,其次是模型3、模型2和模型1。
结合DCE-MRI、DWI和syMRI的mpMRI模型是评估BI-RADS 4类病变恶性程度的有力工具。临床特征可进一步提高该模型的诊断性能。