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The role of histogram analysis in diffusion-weighted imaging in the differential diagnosis of benign and malignant breast lesions

机译:直方图分析在良性和恶性乳房病变差异诊断中扩散加权成像的作用

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The present study aims to investigate the role of histogram analysis of intravoxel incoherent motion (IVIM) in the differential diagnosis of benign and malignant breast lesions. The magnetic resonance imaging and clinical data of 55 patients (63 lesions) were retrospectively analyzed. The multi-b-valued diffusion-weighted imaging image was processed using the MADC software to obtain the gray-scaled maps of apparent diffusion coefficient (ADC)-slow, ADC-fast and f. The MaZda software was used to extract the histogram metrics of these maps. Combined with the conventional sequence images, the region of interest (ROI) was manually drawn along the edge of the lesion at the maximum level of the gray-scale image, and the difference of the data was analyzed between the benign and malignant breast lesions. There were 29 patients with 37 benign lesions, which included 23 fibroadenomas, 6 adenosis, 1 breast cysts, 4 intraductal papillomas, and 3 inflammations of breast. Furthermore, 26 malignant lesions in 26 patients, which included 20 non-specific invasive ductal carcinomas, 5 intraductal carcinomas and 1 patient with squamous cell carcinoma. The ADC-slow (mean and the 50th percentile) and f (minimum, mean, kurtosis, the 10th percentile and 50th percentile) of these malignant breast lesions were significantly lower than those of benign lesions (P??0.05), while ADC-fast (kurtosis) and f (variance, skewness) of these malignant breast lesions were significantly higher than those of benign lesions (P??0.05). The histogram analysis of ADC-slow (mean and the 50th percentile), ADC-fast (kurtosis) and f (minimum, mean, kurtosis, the 10th percentile and 50th percentile. Variance, skewness) can provide a more objective and accurate basis for the differential diagnosis of benign and malignant breast lesions.
机译:本研究旨在探讨透明氧化术(IVIM)在良性和恶性乳腺病变的鉴别诊断中的血管结言运动(IVIM)直方图分析的作用。回顾性分析了55例患者(63个病变)的磁共振成像和临床资料。使用MADC软件处理多B值的扩散加权成像图像,以获得表观扩散系数(ADC)的灰度缩放图--SLOW,ADC-FAST和F. MAZDA软件用于提取这些地图的直方图度量。结合常规序列图像,在灰度图像的最大水平的最大水平处手动地绘制感兴趣区域(ROI),并且在良性和恶性乳房病变之间分析了数据的差异。有29例患有37例良性病变,其中包括23名纤维腺瘤,6个腺度,1个乳腺囊肿,4个内部乳头瘤和3个乳腺癌。此外,26名患者中的26例恶性病变,包括20例非特异性侵入性导管癌,5例内含癌和1例患有鳞状细胞癌。这些恶性乳腺病变的adc-slow(平均值和50百分位数)和f(最小,平均值,峰,第10位和第50位)显着低于良性病变(p≤0.05)这些恶性乳腺病变的ADC-FAST(kurtosis)和F(方差,偏差)显着高于良性病变(p≤≤0.05)。 ADC-SLOS的直方图分析(平均值和第50百分位数),ADC-FAST(Kurtosis)和F(最小,平均,峰,第10位和第50百分位数。方差,歪斜)可以为其提供更准确的基础良性和恶性乳腺病变的鉴别诊断。

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