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Abstract #3166

Classifier fusion improves prostate cancer detection using MP-MRI

Ghazaleh Jamshidi1, Ali Abbasian Ardakani2, Farshid Babapour Mofrad1, Hamidreza Saligheh Rad3,4, and Mahyar Ghafoori5
11- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran (Islamic Republic of), 22- Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Islamic Republic of), 3Tehran University of Medical Science, Tehran, Iran (Islamic Republic of), 4Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), 53- Department of Radiology, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran (Islamic Republic of)


Multiparametric MRI (MP-MRI) has been widely used for detection of Prostate Cancer (PCa). In this study, we propose a new method using MP-MRI including T2-weighted (T2W) and dynamic contrast enhanced (DCE-) MRI for detection of PCa. 32 patients who had high prostate specific antigen (PSA) level recruited. We generated predictive models by extracting radiomics features and classifying benign and malignant lesions. The feature scores are evaluated with Relieff feature selection for each of the modalities. The fused classifier using decision template method showed the highest performance with accuracy, specificity, and sensitivity of 100.

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