Texture analysis of diffusion-weighted magnetic resonance imaging to identify atypically enhanced small hepatocellular carcinoma and dysplastic nodules under the background of cirrhosis
10.3760/cma.j.issn.1007-3418.2020.01.010
- VernacularTitle: MR扩散加权成像纹理分析鉴别肝硬化背景下不典型强化的小肝癌和增生结节
- Author:
Xi ZHONG
1
;
Jiansheng LI
1
;
Zhijun CHEN
1
;
Jinxue YIN
1
;
Si GUI
1
;
Ziqing SUN
1
;
Hongsheng TANG
2
Author Information
1. Department of Radiology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, China
2. Department of Abdominal Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, China
- Publication Type:Journal Article
- Keywords:
Carcinoma, hepatocellular;
Liver cirrhosis;
Diagnosis, differential;
Diffusion-weighted MR imaging;
Texture analysis
- From:
Chinese Journal of Hepatology
2020;28(1):37-42
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of texture analysis based on diffusion-weighted magnetic resonance imaging (DWI) in the differential diagnosis of atypically enhanced small hepatocellular carcinoma (sHCC) and dysplastic nodules (DNs) in liver cirrhosis.
Methods:Data of 59 cases with atypical enhancement and solitary cirrhotic nodule (≤2 cm) confirmed by dynamic contrast enhanced MRI and surgical pathology specimen were analyzed retrospectively. Among them, 37 cases were of atypically enhanced sHCC and 22 cases of DNS. The DWI signal characteristics of the lesions were analyzed to measure the average apparent diffusion coefficient (ADC) value of the lesions, and the ADC ratio of the lesion to the liver parenchyma. MaZda software was used to manually draw the region of interest to extract the texture parameters of DWI lesions. The three sets (combination of Fisher coefficient, classification of error probability combined with average correlation coefficient and interactive information) were used to select the thirty optimal texture parameters. Raw data analysis (RDA), principal component analysis (PCA), linear discriminant analysis (LDA) and non-linear discriminant analysis (NDA) were performed for texture classification. The difference of ADC value and ADC ratio between sHCC and DNS group was compared by independent sample t-test, and χ2 test was used to compare the count data (or rate). ROC curve analysis was used to evaluate the diagnostic efficiency.
Results:The sensitivity, specificity and accuracy of DWI high-signal in the identification of atypically enhanced sHCC and DNs were 94.6% (35/37), 68.2% (15/22), and 84.7% (50/59), respectively. The ADC ratio of atypically enhanced sHCC was significantly lower than DNs, and the difference was statistically significant (t = 2.99, P = 0.002). The sensitivity, specificity, and accuracy for the diagnosis of atypically enhanced sHCC were 73.0% (27/37), 72.7% (16/22) and 72.9% (43/59), respectively. The sensitivity, specificity and accuracy of DWI texture analysis in diagnosing atypically enhanced sHCC were 94.6% (35/37), 95.5% (21/22) and 94.9% (56/59).The diagnostic efficiency of DWI texture analysis (AUC = 0.94) was significantly higher than DWI high-signal (AUC = 0.81) and ADC ratio (AUC = 0.72).
Conclusion:The texture analysis based on DWI can identify atypically enhanced sHCC and dysplastic nodules under the background of cirrhosis, and its efficacy is better than qualitative and quantitative DWI.
