Liu Hong-Li, Zong Min, Wei Han, Lou Jian-Juan, Wang Si-Qi, Zou Qi-Gui, Shi Hai-Bin, Jiang Yan-Ni
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Br J Radiol. 2017 Nov;90(1079):20170394. doi: 10.1259/bjr.20170394. Epub 2017 Sep 6.
This study aims to find out the benefits of adding histogram analysis of apparent diffusion coefficient (ADC) maps onto dynamic contrast-enhanced MRI (DCE-MRI) in predicting breast malignancy.
This study included 95 patients who were found with breast mass-like lesions from January 2014 to March 2016 (47 benign and 48 malignant). These patients were estimated by both DCE-MRI and diffusion-weighted imaging (DWI) and classified into two groups, namely, the benign and the malignant. Between these groups, the DCE-MRI parameters, including morphology, enhancement homogeneity, maximum slope of increase (MSI) and time-signal intensity curve (TIC) type, as well as histogram parameters generated from ADC maps were compared. Then, univariate and multivariate logistic regression analyses were conducted to determine the most valuable variables in predicting malignancy. Receiver operating characteristic curve analyses were taken to assess their clinical values.
The lesion morphology, MSI and TIC Type (p < 0.05) were significantly different between the two groups. Multivariate logistic regression analyses revealed that irregular morphology, TIC Type II/III and ADC were important predictors for breast malignancy. Increased area under curve (AUC) and specificity can be achieved with Model 2 (irregular morphology + TIC Type II/III + ADC < 1.047 ×10 mm s) as the criterion than Model 1 (irregular morphology + TIC Type II/III) only (Model 2 vs Model 1; AUC, 0.822 vs 0.705; sensitivity, 68.8 vs 75.0%; specificity, 95.7 vs 66.0%).
Irregular morphology, TIC Type II/III and ADC are indicators for predicting breast malignancy. Histogram analysis of ADC maps can provide additional value in predicting breast malignancy. Advances in knowledge: The morphology, MSI and TIC types in DCE-MRI examination have significant difference between the benign and malignant groups. A higher AUC can be achieved by using ADC as the diagnostic index than other ADC parameters, and the difference in AUC based on ADC and ADC was statistically significant. The irregular morphology, TIC Type II/III and ADC were significant predictors for malignant lesions.
本研究旨在探讨在动态对比增强磁共振成像（DCE-MRI）基础上增加表观扩散系数（ADC）图的直方图分析对预测乳腺恶性肿瘤的价值。
本研究纳入了2014年1月至2016年3月间发现的95例乳腺类肿块病变患者（47例良性，48例恶性）。对这些患者进行了DCE-MRI和扩散加权成像（DWI）检查，并分为良性和恶性两组。比较两组之间的DCE-MRI参数，包括形态、强化均匀性、最大上升斜率（MSI）和时间-信号强度曲线（TIC）类型，以及从ADC图生成的直方图参数。然后进行单因素和多因素逻辑回归分析，以确定预测恶性肿瘤最有价值的变量。采用受试者操作特征曲线分析评估其临床价值。
两组之间的病变形态、MSI和TIC类型（p < 0.05）有显著差异。多因素逻辑回归分析显示，不规则形态、TIC II/III型和ADC是乳腺恶性肿瘤的重要预测指标。以模型2（不规则形态+TIC II/III型+ADC<1.047×10 mm²/s）为标准比仅以模型1（不规则形态+TIC II/III型）为标准能获得更高的曲线下面积（AUC）和特异性（模型2与模型1比较；AUC，0.822对0.705；敏感性，68.8%对75.0%；特异性，95.7%对66.0%）。
不规则形态、TIC II/III型和ADC是预测乳腺恶性肿瘤的指标。ADC图的直方图分析在预测乳腺恶性肿瘤方面可提供额外价值。知识进展：DCE-MRI检查中的形态、MSI和TIC类型在良性和恶性组之间有显著差异。以ADC作为诊断指标比其他ADC参数能获得更高的AUC，且基于ADC和ADC的AUC差异有统计学意义。不规则形态、TIC II/III型和ADC是恶性病变的重要预测指标。
