Deep Learning-based Artificial Intelligence Applications in Prostate MRI: Brief Summary

Overview

Journal Br J Radiol

Publisher Oxford University Press

Specialty Radiology

Date 2021 Dec 3

PMID 34860562

Citations 16

Authors

Baris Turkbey

Masoom A Haider

Affiliations

Soon will be listed here.

Abstract

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.

Citing Articles

A pilot study of AI-assisted reading of prostate MRI in Organized Prostate Cancer Testing.

Thimansson E, Zackrisson S, Jaderling F, Alterbeck M, Jiborn T, Bjartell A Acta Oncol. 2024; 63:816-821.

PMID: 39473176 PMC: 11541807. DOI: 10.2340/1651-226X.2024.40475.

A Comprehensive Review of Artificial Intelligence in Prostate Cancer Care: State-of-the-Art Diagnostic Tools and Future Outlook.

Agrawal S, Vagha S Cureus. 2024; 16(8):e66225.

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Artificial intelligence in the management of prostate cancer.

Khanna R, Martinez A, Raison N, Ourselin S, Briganti A, Montorsi F Nat Rev Urol. 2024; 22(3):125-126.

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The impact of prostate volume estimation on the risk-adapted biopsy decision based on prostate-specific antigen density and magnetic resonance imaging score.

Baudewyns A, Guenzel K, Halinski A, Dariane C, Delavar G, Anract J World J Urol. 2024; 42(1):322.

PMID: 38747982 DOI: 10.1007/s00345-024-04962-x.

T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study.

Jin L, Yu Z, Gao F, Li M Insights Imaging. 2024; 15(1):111.

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