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Title: Machine learning in prostate MRI for prostate cancer: current status and future opportunities
Authors: Li, Huanye
Lee, Chau Hung
Chia, David
Lin, Zhiping
Huang, Weimin
Tan, Cher Heng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Li, H., Lee, C. H., Chia, D., Lin, Z., Huang, W. & Tan, C. H. (2022). Machine learning in prostate MRI for prostate cancer: current status and future opportunities. Diagnostics, 12(2), 289-.
Journal: Diagnostics
Abstract: Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.
ISSN: 2075-4418
DOI: 10.3390/diagnostics12020289
Schools: School of Electrical and Electronic Engineering 
Lee Kong Chian School of Medicine (LKCMedicine) 
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles
LKCMedicine Journal Articles

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