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Review

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

Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
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Academic Editor: Peter Gibbs
Diagnostics 2021, 11(6), 959; https://doi.org/10.3390/diagnostics11060959
Received: 6 May 2021 / Revised: 19 May 2021 / Accepted: 21 May 2021 / Published: 26 May 2021
(This article belongs to the Special Issue Machine Learning Advances in MRI of Cancer)
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. View Full-Text
Keywords: artificial intelligence; machine learning; radiomics; deep learning; prostate neoplasms; computer-aided diagnosis; magnetic resonance imaging artificial intelligence; machine learning; radiomics; deep learning; prostate neoplasms; computer-aided diagnosis; magnetic resonance imaging
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MDPI and ACS Style

Twilt, J.J.; van Leeuwen, K.G.; Huisman, H.J.; Fütterer, J.J.; de Rooij, M. Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics 2021, 11, 959. https://doi.org/10.3390/diagnostics11060959

AMA Style

Twilt JJ, van Leeuwen KG, Huisman HJ, Fütterer JJ, de Rooij M. Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics. 2021; 11(6):959. https://doi.org/10.3390/diagnostics11060959

Chicago/Turabian Style

Twilt, Jasper J., Kicky G. van Leeuwen, Henkjan J. Huisman, Jurgen J. Fütterer, and Maarten de Rooij. 2021. "Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review" Diagnostics 11, no. 6: 959. https://doi.org/10.3390/diagnostics11060959

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