Diagnostic Accuracy of Multi-Parametric Magnetic Resonance Imaging for Differentiation of Benign and Malignant Lesions of Prostate Using Radiomics Analysis

AUTHORS

Soheila Koopaee 1 , Anahita Fathi Kazerooni 1 , Mahyar Ghafoori 2 , Mohamad Reza Alviri 1 , Kamal Hoseini 1 , Fakhereh Pashaei 1 , Hamidreza Saligheh Rad 3 , *

1 Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran

2 Department of Radiology, Iran University of Medical Sciences, Tehran, Iran

3 Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran

How to Cite: Koopaee S, Fathi Kazerooni A, Ghafoori M, Alviri M R, Hoseini K, et al. Diagnostic Accuracy of Multi-Parametric Magnetic Resonance Imaging for Differentiation of Benign and Malignant Lesions of Prostate Using Radiomics Analysis, Iran J Radiol. 2019 ; 16(Special Issue):e99135. doi: 10.5812/iranjradiol.99135.

ARTICLE INFORMATION

Iranian Journal of Radiology: 16 (Special Issue); e99135
Published Online: December 10, 2019
Article Type: Abstract
Received: October 26, 2019
Accepted: December 10, 2019
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Abstract

Background: Prostate cancer is the second most common cancer-related cause of death in men. Accurate diagnosis of prostate cancer plays an important role in decreasing mortality rates. European Association of Urology (EAU) suggests multiparametric MRI (mp-MRI) of the prostate as a noninvasive method to evaluate prostate lesions. To leverage the interbreeder variability in the interpretation of mp-MRI, computer-aided diagnostic (CAD) systems can be used for automatic detection and characterization of prostate lesions.

Objectives: The goal of this article was to design a quantification method based on mp-MRI for the discrimination of benign and malignant prostatic lesions with MR imaging/transrectal ultrasonography fusion-guided biopsy as a reference for pathology validation.

Methods: Mp-MR images, including T1- and T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast enhancement imaging (DCE) MRI were acquired at 1.5T from 27 patients. Then, 106 radiomic features (first-order histogram (FOH), gray-level co-occurrence matrix (GLCM), run-length matrix (RLM), and Gabor filters) were calculated from mp-MRI. Statistical analysis was performed using receiver-operating-characteristic curve analysis for feature filtering, linear discriminant analysis (LDA) for feature extraction, and leave-one-out cross-validation for evaluation of the method in the differentiation of benign and malignant lesions.

Results: An accuracy of 96.6% was achieved for discriminating benign and malignant prostate lesions from a subset of texture features derived from ADC and DCE maps (radiomics-based method) with sensitivity and specificity of 100% and 85.7%, respectively.

Conclusion: A radiomic quantification method based on T2-weighted images, ADC maps, and quantitative and semiquantitative DCE maps can discriminate benign from malignant prostate lesions with promising accuracy. This method is helpful to avoid unnecessary biopsies in patients and may provide information for CAD systems for the classifications of prostate lesions as an auto-detection technique.

Copyright © 2019, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
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