title: Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review.
abstract: 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. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.
authors.affiliation: Department of Diagnostic Imaging, metabolic and radiant Therapy, National Cancer Institute of Naples "Pascale Foundation", Via Mariano Semmola 80131, Naples, Italy.|Department of Electrical Engineering and Information Technologies, University 'Federico II', Via Claudio 80125, Naples, Italy.
authors.forename: Roberta
authors.initials: R
authors.lastname: Fusco
authors.affiliation: Department of Electrical Engineering and Information Technologies, University 'Federico II', Via Claudio 80125, Naples, Italy.
authors.forename: Mario
authors.initials: M
authors.lastname: Sansone
authors.affiliation: Department of Diagnostic Imaging, metabolic and radiant Therapy, National Cancer Institute of Naples "Pascale Foundation", Via Mariano Semmola 80131, Naples, Italy.
authors.forename: Salvatore
authors.initials: S
authors.lastname: Filice
authors.affiliation: Department of Diagnostic Imaging, metabolic and radiant Therapy, National Cancer Institute of Naples "Pascale Foundation", Via Mariano Semmola 80131, Naples, Italy.
authors.forename: Guglielmo
authors.initials: G
authors.lastname: Carone
authors.affiliation: Department of Diagnostic Imaging, metabolic and radiant Therapy, National Cancer Institute of Naples "Pascale Foundation", Via Mariano Semmola 80131, Naples, Italy.
authors.forename: Daniela Maria
authors.initials: DM
authors.lastname: Amato
authors.affiliation: Department of Electrical Engineering and Information Technologies, University 'Federico II', Via Claudio 80125, Naples, Italy.
authors.forename: Carlo
authors.initials: C
authors.lastname: Sansone
authors.affiliation: Department of Diagnostic Imaging, metabolic and radiant Therapy, National Cancer Institute of Naples "Pascale Foundation", Via Mariano Semmola 80131, Naples, Italy.
authors.forename: Antonella
authors.initials: A
authors.lastname: Petrillo
keywords: Breast cancer; Classification; Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI); Patter recognition approach