Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2022 Feb 25

PMID 35204380

Authors

Huanye Li

Chau Hung Lee

David Chia

Zhiping Lin

Weimin Huang

Cher Heng Tan

Affiliations

Soon will be listed here.

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.

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