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

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 b...

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Autores principales: Twilt, Jasper J., van Leeuwen, Kicky G., Huisman, Henkjan J., Fütterer, Jurgen J., de Rooij, Maarten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229869/
https://www.ncbi.nlm.nih.gov/pubmed/34073627
http://dx.doi.org/10.3390/diagnostics11060959
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author Twilt, Jasper J.
van Leeuwen, Kicky G.
Huisman, Henkjan J.
Fütterer, Jurgen J.
de Rooij, Maarten
author_facet Twilt, Jasper J.
van Leeuwen, Kicky G.
Huisman, Henkjan J.
Fütterer, Jurgen J.
de Rooij, Maarten
author_sort Twilt, Jasper J.
collection PubMed
description 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.
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spelling pubmed-82298692021-06-26 Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review Twilt, Jasper J. van Leeuwen, Kicky G. Huisman, Henkjan J. Fütterer, Jurgen J. de Rooij, Maarten Diagnostics (Basel) Review 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. MDPI 2021-05-26 /pmc/articles/PMC8229869/ /pubmed/34073627 http://dx.doi.org/10.3390/diagnostics11060959 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Twilt, Jasper J.
van Leeuwen, Kicky G.
Huisman, Henkjan J.
Fütterer, Jurgen J.
de Rooij, Maarten
Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review
title Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review
title_full Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review
title_fullStr Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review
title_full_unstemmed Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review
title_short Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review
title_sort artificial intelligence based algorithms for prostate cancer classification and detection on magnetic resonance imaging: a narrative review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229869/
https://www.ncbi.nlm.nih.gov/pubmed/34073627
http://dx.doi.org/10.3390/diagnostics11060959
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