Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer



To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa).


This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis.


For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923–0.976]) than PI-RADS (Az: 0.878 [0.834–0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945–0.988] vs. 0.940 [0.905–0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960–0.995]) and PCa versus TZ (Az: 0.968 [0.940–0.985]).


Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa.

Key Points

Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS.

Adding MR radiomics significantly improved the performance of PI-RADS.

DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.

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Area under the ROC curve


Dynamic contrast-enhanced


Diffusional kurtosis imaging


Diffusion-weighted imaging


Multi-parametric magnetic resonance imaging


Prostate cancer


Prostate Imaging Reporting and Data System


Peripheral zone


Readout segmentation of long variable echo-trains


Receiver operating characteristic curve


Support vector machine


Transitional zone


Volume of interest


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Author information

Correspondence to Yu-Dong Zhang.

Ethics declarations


The scientific guarantor of this publication is Y. Zhang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.


This research was supported by A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD; JX10231801; Z.Y.D.) and China Postdoctoral Fund (2015 M580453 to Y. Zhang).

Statistics and biometry

One of the authors (Y. Zhang) has significant statistical expertise.

No complex statistical methods were necessary for this paper.

Ethical approval

Institutional Review Board approval was obtained.

Informed consent

Written informed consent was waived by the Institutional Review Board.


• retrospective

• diagnostic or prognostic study

• performed at one institution

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Wang, J., Wu, C., Bao, M. et al. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol 27, 4082–4090 (2017) doi:10.1007/s00330-017-4800-5

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  • Prostate cancer
  • Prostate Imaging Reporting and Data System v2
  • Machine learning
  • Support vector machine
  • Multi-parametric MRI