Modeling the diffusion-weighted imaging signal for breast lesions in the b=200 to 3000 s/mm2 range: quality of fit and classification accuracy for different representations
Modeling the diffusion-weighted imaging signal for breast lesions in the b=200 to 3000 s/mm2 range: quality of fit and classification accuracy for different representations
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DOI：
10.1002/mrm.28161
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发表时间：
2020-01-23
影响因子：
3.3
通讯作者：
Goa, Pal Erik
中科院分区：
文献类型：
--
作者：
Vidic, Igor;Egnell, Liv;Goa, Pal Erik
目的评价乳腺良恶性病变中b值在200~3000 S/mm(2)范围内扩散加权成像信号的不同非高斯表示方法。方法对43例乳腺良、恶性肿瘤患者进行弥散加权成像(b值分别为200、600、1200、1800、2400和3000 S/mm(2))。在不同的b值范围内，来自感兴趣区域(ROI)的平均信号适用于六种不同的表示。采用修正后的Akaike信息标准(AICC)评定匹配质量，采用Friedman检验评定代表性等级。用受试者工作特征曲线的曲线下面积(AUC)评价各参数对良恶性病变的鉴别能力。病变ROI被分为中心和外围部分，以评估异质性的潜在影响。对噪声底限修正的敏感性也进行了评估。结果根据AICC，Pade指数是最好的，而峰度、分数和双指数3种模型的AUC最高，分别为0.99。在b(Max)=600 S/mm(2)处的单指数模型已经提供了AUC=0.96.采集时间相当短且分析更简单。恶性病变的中心区和周边区差异有统计学意义。单指数和双指数模型对不同程度的噪声底限校正最稳定。结论乳腺病变的DWI曲线在高b值时需要非高斯表示。然而，高b值数据对鉴别良恶性病变的临床价值尚不清楚。
Purpose To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm(2) in benign and malignant breast lesions. Methods Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm(2)). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges. Quality of fit was assessed by the corrected Akaike information criterion (AICc), and the Friedman test was used for assessing representation ranks. The area under the curve (AUC) of receiver operating characteristic curves were used to evaluate the power of derived parameters to differentiate between malignant and benign lesions. The lesion ROI was divided in central and peripheral parts to assess potential effect of heterogeneity. Sensitivity to noise-floor correction was also evaluated. Results The Pade exponent was ranked as the best based on AICc, whereas 3 models (kurtosis, fractional, and biexponential) achieved the highest AUC = 0.99 for lesion differentiation. The monoexponential model at b(max) = 600 s/mm(2) already provides AUC = 0.96, with considerably shorter acquisition time and simpler analysis. Significant differences between central and peripheral parts of lesions were found in malignant lesions. The mono- and biexponential models were most stable against varying degrees of noise-floor correction. Conclusion Non-Gaussian representations are required for fitting of the DWI curve at high b-values in breast lesions. However, the added clinical value from the high b-value data for differentiation of benign and malignant lesions is not clear.