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Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging
Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10236970/ https://www.ncbi.nlm.nih.gov/pubmed/37275303 http://dx.doi.org/10.4329/wjr.v15.i5.136 |
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author | Chervenkov, Lyubomir Sirakov, Nikolay Kostov, Gancho Velikova, Tsvetelina Hadjidekov, George |
author_facet | Chervenkov, Lyubomir Sirakov, Nikolay Kostov, Gancho Velikova, Tsvetelina Hadjidekov, George |
author_sort | Chervenkov, Lyubomir |
collection | PubMed |
description | Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance, that technology has certain unresolved limitations. In recent years artificial intelligence (AI) has been introduced to the field of radiology, providing new software solutions for prostate diagnostics. Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy. AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting & Data System classification. Finally, AI has facilitated the combination of data obtained from clinical, laboratory (prostate-specific antigen), imaging (magnetic resonance), and biopsy examinations, and in this way new regularities can be found which at the moment remain hidden. Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy, accuracy and efficiency of diagnosis and treatment of Pca. |
format | Online Article Text |
id | pubmed-10236970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-102369702023-06-03 Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging Chervenkov, Lyubomir Sirakov, Nikolay Kostov, Gancho Velikova, Tsvetelina Hadjidekov, George World J Radiol Minireviews Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance, that technology has certain unresolved limitations. In recent years artificial intelligence (AI) has been introduced to the field of radiology, providing new software solutions for prostate diagnostics. Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy. AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting & Data System classification. Finally, AI has facilitated the combination of data obtained from clinical, laboratory (prostate-specific antigen), imaging (magnetic resonance), and biopsy examinations, and in this way new regularities can be found which at the moment remain hidden. Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy, accuracy and efficiency of diagnosis and treatment of Pca. Baishideng Publishing Group Inc 2023-05-28 2023-05-28 /pmc/articles/PMC10236970/ /pubmed/37275303 http://dx.doi.org/10.4329/wjr.v15.i5.136 Text en ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. |
spellingShingle | Minireviews Chervenkov, Lyubomir Sirakov, Nikolay Kostov, Gancho Velikova, Tsvetelina Hadjidekov, George Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging |
title | Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging |
title_full | Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging |
title_fullStr | Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging |
title_full_unstemmed | Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging |
title_short | Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging |
title_sort | future of prostate imaging: artificial intelligence in assessing prostatic magnetic resonance imaging |
topic | Minireviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10236970/ https://www.ncbi.nlm.nih.gov/pubmed/37275303 http://dx.doi.org/10.4329/wjr.v15.i5.136 |
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