[Artificial Intelligence and Radiomics in MRI-based Prostate Diagnostics]

Overview

Journal Radiologe

Specialty Radiology

Date 2019 Dec 6

PMID 31802148

Citations 6

Authors

Charlie Alexander Hamm

Nick Lasse Beetz

Lynn Jeanette Savic

Tobias Penzkofer

Affiliations

Soon will be listed here.

Abstract

Clinical/methodical Issue: In view of the diagnostic complexity and the large number of examinations, modern radiology is challenged to identify clinically significant prostate cancer (PCa) with high sensitivity and specificity. Meanwhile overdiagnosis and overtreatment of clinically nonsignificant carcinomas need to be avoided.

Standard Radiological Methods: Increasingly, international guidelines recommend multiparametric magnetic resonance imaging (mpMRI) as first-line investigation in patients with suspected PCa.

Methodical Innovations: Image interpretation according to the PI-RADS criteria is limited by interobserver variability. Thus, rapid developments in the field of automated image analysis tools, including radiomics and artificial intelligence (AI; machine learning, deep learning), give hope for further improvement in patient care.

Performance: AI focuses on the automated detection and classification of PCa, but it also attempts to stratify tumor aggressiveness according to the Gleason score. Recent studies present good to very good results in radiomics or AI-supported mpMRI diagnosis. Nevertheless, these systems are not widely used in clinical practice.

Achievements And Practical Recommendations: In order to apply these innovative technologies, a growing awareness for the need of structured data acquisition, development of robust systems and an increased acceptance of AI as diagnostic support are needed. If AI overcomes these obstacles, it may play a key role in the quantitative and reproducible image-based diagnosis of ever-increasing prostate MRI examination volumes.

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