Establishment of machine learning models for diagnosis of clinically significant prostate cancer based on multi-parameter MRI and radiomics
10.13929/j.1003-3289.201902129
- Author:
Tao PENG
1
Author Information
1. Department of Radiology, Affiliated Hospital of Chengdu University
- Publication Type:Journal Article
- Keywords:
Machine learning;
Magnetic resonance imaging;
Prostatic neoplasms;
Raiomics;
Texture analysis
- From:
Chinese Journal of Medical Imaging Technology
2019;35(10):1526-1530
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To establish machine learning models based on multi-parameter MRI (mpMRI) and radiomics features, and to investigate their value for diagnosis of clinically significant prostate cancer (CSPC). Methods: Logistic regression (LR), stepwise regression (SR), classical decision tree (cDT), conditional inference tree (CIT), random forest (RF) and support vector machine (SVM) models were established with combining of texture analysis, dynamic contrast enhanced MRI (DCE-MRI), prostate imaging reporting and data system (PI-RADS) score and part of clinical data. ROC curve and decision curve analysis (DCA) were used to evaluate the models and the importance of variables. Results: AUC of RF model for diagnosing CSPC in verification group was larger than that of SVM, cDT and SR model (all P<0.05). There was no statistically significant difference for diagnosing CSPC in AUC of RF model and LR, CIT model (P>0.05), nor of AUC for diagnosing CSPC among the other models in validation group (all P>0.05). When the probability threshold was 16%-91%, the net benefit of RF model was the largest, better than other models. When the probability threshold was 23%-91%, the net benefit of SVM model was second only to the RF model, but better than other models. Prostate specific antigen density (PSAD) and some texture analysis parameters were of high importance. Conclusion: RF model is superior to other models in diagnosis of CSPC, SVM model comes second. PSAD and some texture analysis parameters are more important than PI-RADS score and DCE-MRI quantitative parameters for diagnosis of CSPC.
