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Eur J Radiol. 2019 Jun;115:16-21. doi: 10.1016/j.ejrad.2019.03.010. Epub 2019 Mar 15.

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method.

Author information

1
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China.
2
Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning Province, PR China.
3
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Sciences, Beijing, PR China.
4
Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning Province, PR China. Electronic address: mahe@bmie.neu.edu.cn.
5
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Sciences, Beijing, PR China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, PR China. Electronic address: jie.tian@ia.ac.cn.
6
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China. Electronic address: wang6@tjh.tjmu.edu.cn.

Abstract

PURPOSE:

To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa).

MATERIALS AND METHODS:

Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient. The minority group in the training cohort was balanced via the synthetic minority over-sampling technique (SMOTE) method. We used minimum-redundancy maximum-relevance (mRMR) selection and the LASSO algorithm for feature selection and radiomics signature building. The classification performance of the radiomics signature for csPCa and ciPCa was evaluated by receiver operating characteristic curve analysis in the training and test cohorts.

RESULTS:

Nine features were selected for the radiomics signature building. Significant differences in the radiomics signature existed between the csPCa and ciPCa groups in both the training and test cohorts (p < 0.01 for both). The AUC, sensitivity and specificity of the radiomics signature were 0.872 (95% CI: 0.823-0.921), 0.883, and 0.753, respectively, in the training cohort, and 0.823 (95% CI: 0.669-0.976), 0.841, and 0.727, respectively, in the test cohort.

CONCLUSION:

Mp-MRI-based radiomics signature have the potential to noninvasively discriminate between csPCa and ciPCa.

KEYWORDS:

Magnetic resonance imaging; Neoplasm grading; Prostatic neoplasms; Radiomics

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