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• W2904793394 abstract "Current diagnosis and treatment stratification of patients with suspected prostate cancer relies on a combination of histological and magnetic resonance imaging (MRI) findings. The aim of this article is to provide a brief overview of prostate pathological grading as well as the relevant aspects of multiparametric (MRI) mpMRI, before indicating the potential that magnetic resonance textural analysis (MRTA) offers within prostate cancer. A review of the evidence base on MRTA in prostate cancer will enable discussion of the utility of this field while also indicating recommendations to future research. Radiomic textural analysis allows the assessment of spatial inter-relationships between pixels within an image by use of mathematical methods. First-order textural analysis is better understood and may have more clinical validity than higher-order textural features. Textural features extracted from apparent diffusion coefficient maps have shown the most potential for clinical utility in MRTA of prostate cancers. Future studies should aim to integrate machine learning techniques to better represent the role of MRTA in prostate cancer clinical practice. Nomenclature should be used to reduce misidentification between first-order and second-order energy and entropy. Automated methods of segmentation should be encouraged in order to reduce problems associated with inclusion of normal tissue within regions of interest. The retrospective and small-scale nature of most published studies, make it difficult to draw meaningful conclusions. Future larger prospective studies are required to validate the textural features indicated to have potential in characterisation and/or diagnosis of prostate cancer before translation into routine clinical practice." @default.
• W2904793394 created "2018-12-22" @default.
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• W2904793394 date "2019-11-01" @default.
• W2904793394 modified "2023-10-02" @default.
• W2904793394 title "The value of MR textural analysis in prostate cancer" @default.
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• W2904793394 doi "https://doi.org/10.1016/j.crad.2018.11.007" @default.
• W2904793394 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30573283" @default.
• W2904793394 hasPublicationYear "2019" @default.
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