Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.

Jasper J Twilt,Kicky G Van Leeuwen,Henkjan J Huisman,Jurgen J Fütterer,Maarten De Rooij

doi:10.3390/diagnostics11060959

Abstract

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.

Highlights

With a worldwide estimation of 1.4 million new cases in 2020, prostate cancer (PCa) is the second most common malignancy among men worldwide [1]
In 2018 and 2019, several large prospective trials concluded that the use of magnetic resonance imaging (MRI) prior to biopsy increases the detection of clinically significantPCa, while decreasing detection of clinically insignificantPCa compared to transrectal ultrasound guided biopsy [4,5,6,7]
In this review we provide an overview of studies describing Artificial intelligence (AI) algorithms for prostate MRI analysis from January 2018 to February 2021, in which we differentiate applications for lesion classification and lesion detection for PCa

Summary

Introduction

With a worldwide estimation of 1.4 million new cases in 2020, prostate cancer (PCa) is the second most common malignancy among men worldwide [1]. In 2018 and 2019, several large prospective trials concluded that the use of magnetic resonance imaging (MRI) prior to biopsy increases the detection of (more aggressive) clinically significant (cs)PCa, while decreasing detection of (non-aggressive) clinically insignificant (cis)PCa compared to transrectal ultrasound guided biopsy [4,5,6,7]. For this reason, multiparametric (mp)MRI has been included in the guidelines of the European Association of Urology (EAU) to be performed prior to biopsy [8]. The most significant features are selected and used in the learning task of the ML algorithm [16]

Methods

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Discussion

Conclusion