Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
GE Healthcare, Wuhan, China.
J Magn Reson Imaging. 2023 Sep;58(3):963-974. doi: 10.1002/jmri.28600. Epub 2023 Feb 3.
Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable.
To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images.
Retrospective.
A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results.
FIELD STRENGTH/SEQUENCE: A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence.
In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort.
Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant.
For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635.
Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions.
Stage 2.
非肿块样强化（NME）病变被认为是导致不必要活检的主要原因。弥散加权成像（DWI）或动态对比增强（DCE）序列通常用于区分良性和恶性 NME。了解哪种方法更有效和可靠非常重要。
基于对比增强（CE）MRI 图像的形态学特征评估，比较 DCE 曲线和 DWI 在鉴别良性和恶性 NME 病变方面的诊断性能。
回顾性。
在训练队列中，共纳入 180 例患者的 184 个病变和 75 例患者的 77 个病变，这些病变均有病理结果。
磁场强度/序列：3.0T/multi-b 值 DWI（b 值=0、50、1000 和 2000 sec/mm）和时间分辨血管造影与随机轨迹和容积内插屏气检查（TWIST-VIBE）序列。
在训练队列中，首先构建了一种基于分布和内部增强特征的形态学诊断模型。然后，使用二元逻辑回归，以病理结果为参考标准，建立了表观扩散系数（ADC）模型（ADC+morphology）和时间-强度曲线（TIC）模型（TIC+morphology）。在训练和验证队列中比较了两种模型的敏感性、特异性和曲线下面积（AUC）。
进行了受试者工作特征（ROC）曲线分析和两样本 t 检验/Mann-Whitney U 检验/卡方检验。P<0.05 被认为具有统计学意义。
在训练队列中，TIC/ADC 模型的敏感性分别为 0.924/0.814，特异性分别为 0.615/0.615，AUC 分别为 0.811（95%，0.727，0.894）/0.769（95%，0.681，0.856）。TIC-ADC 联合模型的 AUC 明显高于 ADC 模型，与 TIC 模型相当（P=0.494）。在验证队列中，TIC/ADC 模型的 AUC 分别为 0.799/0.635。
基于形态学分析，TIC 模型在鉴别良性和恶性 NME 病变方面的性能优于 ADC 模型。
4 级。
2 级。
