Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models



Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However, all the assessment suffers from poor reproducibility among the readers. The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI.
Materials and methods
Thirty two patients who had high prostate specific antigen level were recruited. The prostate biopsies considered as the reference to differentiate between 66 benign and 36 malignant prostate lesions. 181 features were extracted from each modality. K-nearest neighbors, artificial neural network, decision tree, and linear discriminant analysis were used for machine-learning study. The leave-one-out cross-validation method was used to prevent overfitting and build robust models.
Radiomics analysis showed that T2 …

Radiomic, machine-learning, prostate cancer, mp-MRI

Ghazaleh Jamshidi, Ali Abbasian Ardakani, Mahyar Ghafoori, Farshid Babapour Mofrad, Hamidreza Saligheh Rad

Magnetic Resonance Materials in Physics, Biology and Medicine
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