Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review.

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented.

Multiparametric MRI for prostate cancer detection: performance in patients with prostate-specific antigen values between 2.5 and 10 ng/mL.

Purpose: To assess the diagnostic performance of multiparametric MRI (mpMRI), in the detection of prostate cancer, including morphologic sequences (mMRI), diffusion-weighted imaging (DWI), and MR spectroscopy (MRS). Combined morphological and functional MRI scoring systems was used for urological–radiological work-up of patients with a prostate-specific antigen (PSA) value ≤ 10 ng/mL.

Materials And Methods: The study included 136 of 200 consecutive patients with PSA values between 2.