The value of texture analysis based on T 2WI and apparent diffusion coefficient map in discriminating low grade from high grade prostate cancer
10.3760/cma.j.cn112149-20191210-00974
- VernacularTitle:基于T 2WI、表观扩散系数图的纹理分析鉴别低、高级别前列腺癌的价值
- Author:
Jinke XIE
1
;
Xiangde MIN
;
Basen LI
;
Zhaoyan FENG
;
Peipei ZHANG
;
Wei CAI
;
Huijuan YOU
;
Chanyuan FAN
;
Liang WANG
Author Information
1. 华中科技大学同济医学院附属同济医院放射科，武汉 430030
- From:
Chinese Journal of Radiology
2020;54(12):1191-1196
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of texture analysis based on T 2WI and apparent diffusion coefficient (ADC) maps in discriminating low grade from high grade prostate cancer (PCa). Methods:Retrospective analysis was performed on patients who were confirmed to be PCa by pathology after surgery and underwent MRI examination in the department of radiology,Tongji Hospital,Tongji Medical College, Huazhong University of Science and Technology before radical surgery, including routine T 1WI, T 2WI and diffusion weighted imaging (DWI) sequences. 3D data analysis module of the MaZda software was used to manually draw region of interest (ROIs) slice by slice on T 2WI and ADC images, and generate volume of interest (VOI) of the entire tumor. MaZda software was also used to extract texture features. The independent sample t test or Mann-Whitney U test were used to identify the texture features with statistically significant differences between low and high grade PCa groups. Lasso regression model was used to select the best combination of texture features for identifying low and high grade PCa, and then the model was built. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model in both training cohort and test cohort. Results:The best texture feature combination selected by Lasso regression model were the S (1, 0, 0) correlation of T 2WI and the S (1, 0, 0) correlation, S (1, -1, 0) sum entropy and vertical-run length nonuniformity of ADC maps. The area under the ROC curve (AUC) of the model in training cohort was 0.823, and the sensitivity and specificity were 70.4% and 80.8%, respectively, which were better than the single texture feature. The AUC of the model in test cohort was 0.714, which was worse than training cohort. Conclusion:The texture analysis of T 2WI and ADC maps is valuable for the identification of low and high grade PCa.
