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

Journal: European radiology

Published Date: Apr 3, 2017

Abstract

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

Authors

Jing Wang

Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
Chen-Jiang Wu

Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Mei-Ling Bao

Department of Pathology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Jing Zhang

MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
Xiao-Ning Wang

Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Yu-Dong Zhang

University of Leicester, Leicester, United Kingdom.

Keywords

Aged Aged, 80 and over Humans Machine Learning Magnetic Resonance Imaging Male Middle Aged Predictive Value of Tests Prostatic Neoplasms Radiology Information Systems ROC Curve Sensitivity and Specificity Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (28374077)

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

Abstract

Authors

Keywords

External Resources

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