Discriminating low grade from high grade prostate cancer based on MR apparent diffusion coefficient map texture analysis
10.3760/cma.j.issn.1005-1201.2019.10.013
- VernacularTitle: 基于ADC图的纹理分析在低、高级别前列腺癌诊断中的价值
- Author:
Chanyuan FAN
1
;
Xiangde MIN
1
;
Qiubai LI
2
;
Junhua FANG
3
;
Zhihua FANG
4
;
Peipei ZHANG
1
;
Chaoyan FENG
1
;
Huijuan YOU
1
;
Liang WANG
1
Author Information
1. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
2. Department of Radiology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA
3. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (Now Works in Department of Radiology, Traditional Chinese Medicine Hospital of Zhijiang City, Hubei Province, Zhijiang 443200, China)
4. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (Now Works in Department of Radiology, the People′s Hospital (Traditional Chinese Medicine) of Fuliang County, Jingdezhen City, Jiangxi Provicne, Jingdezhen 333000, China)
- Publication Type:Journal Article
- Keywords:
Prostatic neoplasms;
Texture analysis;
Magnetic resonance imaging
- From:
Chinese Journal of Radiology
2019;53(10):859-863
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of texture analysis based on MR ADC map of prostate in differentiating between low-grade and high-grade prostate cancer (PCa).
Methods:PCa confirmed by pathology after radical prostatectomy were analyzed retrospectively, all patients underwent multiparametric MRI before radical prostatectomy, including T1WI,T2WI and DWI. On the ADC map, ROI was drawn manually to encompass the whole tumor by ITK-SNAP software. The python-based pyradiomics package was used to extract 105 texture features. The intraclass correlation coefficient was used to evaluate the repeatability of the texture features. The independent sample t test or Mann-Whitney U test was used to exclude features that had no significant difference between low grade and high grade PCa. Lasso regression model and 5 fold cross validation method were used to obtain texture feature combination of the highest performance and develop a classification modelfor discriminating low from high grade PCa. ROC curve was used to evaluate the diagnostic efficiency of the model.
Result:Ninety patients with PCa confirmed by pathology after radical prostatectomywere analyzed retrospectively,including 36 patients with low-level PCa (GS≤3+4) and 54 patients with high-level PCa (GS≥4+3). The area under curve of the model was 0.841, with sensitivity 69.6% and specificity 91.2%, which was significantly higher than single texture feature or traditional mean ADC value.
Conclusion:Texture analysis based on MRI-ADC map of prostate could be used to discriminate low grade PCa from high grade PCa.
