Diagnostic value of radiomics based on biparametric prostate MRI imaging in Gleason classification of prostate cancer
10.3760/cma.j.issn.1005-1201.2019.10.011
- VernacularTitle: 双参数MRI影像组学对前列腺癌Gleason分级的诊断价值
- Author:
Hongtao ZHANG
1
;
Zeyu HU
2
;
Haiyi WANG
3
;
Bo WANG
4
;
Xu BAI
3
;
Huiyi YE
3
Author Information
1. Department of Radiology, the First Medical Center of PLA General Hospital, Beijing 100853, China (Now Works in the Department of Radiology, South Area of the Fifth Medical Center of PLA General Hospital, Beijing 100071, China)
2. College of Microelectronics, Xidian University, Xi′an 710071, China
3. Department of Radiology, the First Medical Center of PLA General Hospital, Beijing 100853, China
4. Tsinghua University, Beijing 100084, China
- Publication Type:Journal Article
- Keywords:
Prostatic neoplasms;
Magnetic resonance imaging;
Radiomics
- From:
Chinese Journal of Radiology
2019;53(10):849-852
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of radiomics in stratifying the Gleason score (GS) of prostate cancer based on vast image features from biparametric MRI.
Methods:Three hundred and sixteen patients were enrolled in this study from October, 2015 to December, 2018 and their results of surgical pathology were obtained. The lesions were manually depicted by 3D-Slicer. Then, 106-dimensional features extracted by radiomics were used to conduct Spearman non-parametric correlation test with the high and low risk stratification of GS. The constructed Neural Network was trained with the features after dimension reduction by principal component analysis as the input. Then, the testing set was fed in to get the predictive capability of the model. In the end, 10-fold cross-validation and shuffle of 100 times were used to test the accuracy of the prediction and the generalization ability of the model.
Results:Seventy seven-dimensional features with significant correlation were found at the level of P valued=0.05 (two-tailed). After dimensional features were reduced, 21 dimensional new feature spaces with 99% original feature information were obtained. The results on the testing data after the 10-fold validation and shuffle were AUC=0.712 with T2WI, AUC=0.689 with DWI (b=1 000 s/mm2), AUC=0.689 with DWI (b=2 000 s/mm2) and AUC=0.691 with DWI (b=3 000 s/mm2).
Conclusion:The neural network after extracting features from biparametric MRI images can accurately and automatically distinguish the high risk and low risk groups of Gleason grade of prostatic cancer.
