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Diagnostics
Volume 12
Issue 4
10.3390/diagnostics12040799
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Review

Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review

by
Henrik J. Michaely
1,*,
Giacomo Aringhieri
2,3,
Dania Cioni
2,3 and
Emanuele Neri
2,3
1
Medical Faculty Mannheim, University of Heidelberg, 69120 Heidelberg, Germany
2
Academic Radiology, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
3
Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122 Milano, Italy
*
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(4), 799; https://doi.org/10.3390/diagnostics12040799
Submission received: 22 February 2022 / Revised: 19 March 2022 / Accepted: 23 March 2022 / Published: 24 March 2022
(This article belongs to the Topic Artificial Intelligence in Cancer Diagnosis and Therapy)
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Abstract

Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.
Keywords: prostate cancer; multiparametric prostate MRI; biparametric prostate MRI; deep-learning; radiomics; artificial intelligence; cancer detection; PIRADS prostate cancer; multiparametric prostate MRI; biparametric prostate MRI; deep-learning; radiomics; artificial intelligence; cancer detection; PIRADS

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MDPI and ACS Style

Michaely, H.J.; Aringhieri, G.; Cioni, D.; Neri, E. Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review. Diagnostics 2022, 12, 799. https://doi.org/10.3390/diagnostics12040799

AMA Style

Michaely HJ, Aringhieri G, Cioni D, Neri E. Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review. Diagnostics. 2022; 12(4):799. https://doi.org/10.3390/diagnostics12040799

Chicago/Turabian Style

Michaely, Henrik J., Giacomo Aringhieri, Dania Cioni, and Emanuele Neri. 2022. "Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review" Diagnostics 12, no. 4: 799. https://doi.org/10.3390/diagnostics12040799

APA Style

Michaely, H. J., Aringhieri, G., Cioni, D., & Neri, E. (2022). Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review. Diagnostics, 12(4), 799. https://doi.org/10.3390/diagnostics12040799

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