The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review
Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/18/6428 |
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| author | Ronan Thenault Kevin Kaulanjan Thomas Darde Nathalie Rioux-Leclercq Karim Bensalah Marie Mermier Zine-eddine Khene Benoit Peyronnet Shahrokh Shariat Benjamin Pradère Romain Mathieu |
| author_facet | Ronan Thenault Kevin Kaulanjan Thomas Darde Nathalie Rioux-Leclercq Karim Bensalah Marie Mermier Zine-eddine Khene Benoit Peyronnet Shahrokh Shariat Benjamin Pradère Romain Mathieu |
| author_sort | Ronan Thenault |
| collection | DOAJ |
| description | Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine. |
| first_indexed | 2024-03-10T16:18:55Z |
| format | Article |
| id | doaj.art-be8dcb8a607141b5bc0ceee155f350a1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:18:55Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-be8dcb8a607141b5bc0ceee155f350a12023-11-20T13:49:58ZengMDPI AGApplied Sciences2076-34172020-09-011018642810.3390/app10186428The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic ReviewRonan Thenault0Kevin Kaulanjan1Thomas Darde2Nathalie Rioux-Leclercq3Karim Bensalah4Marie Mermier5Zine-eddine Khene6Benoit Peyronnet7Shahrokh Shariat8Benjamin Pradère9Romain Mathieu10IRSET, 35000 Rennes, FranceDepartment of Urology, Service d’urologie, Rennes University Hospital, Hôpital Pontchaillou, 2, rue Henri Le Guilloux, 35000 Rennes, FranceSciLicium, 5 la hurbinais, 35850 Gévezé, FranceDepartment of Urology, Service d’urologie, Rennes University Hospital, Hôpital Pontchaillou, 2, rue Henri Le Guilloux, 35000 Rennes, FranceDepartment of Urology, Service d’urologie, Rennes University Hospital, Hôpital Pontchaillou, 2, rue Henri Le Guilloux, 35000 Rennes, FranceDepartment of Urology, Service d’urologie, Rennes University Hospital, Hôpital Pontchaillou, 2, rue Henri Le Guilloux, 35000 Rennes, FranceDepartment of Urology, Service d’urologie, Rennes University Hospital, Hôpital Pontchaillou, 2, rue Henri Le Guilloux, 35000 Rennes, FranceDepartment of Urology, Service d’urologie, Rennes University Hospital, Hôpital Pontchaillou, 2, rue Henri Le Guilloux, 35000 Rennes, FranceDepartment of Urology, Medical University Vienna, General Hospital, 1090 Vienna, AustriaDepartment of Urology, Medical University Vienna, General Hospital, 1090 Vienna, AustriaIRSET, 35000 Rennes, FranceArtificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.https://www.mdpi.com/2076-3417/10/18/6428artificial intelligencemachine learningdeep learning artificial neural networknatural-language processingprostate cancercomputer-aided diagnosis |
| spellingShingle | Ronan Thenault Kevin Kaulanjan Thomas Darde Nathalie Rioux-Leclercq Karim Bensalah Marie Mermier Zine-eddine Khene Benoit Peyronnet Shahrokh Shariat Benjamin Pradère Romain Mathieu The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review Applied Sciences artificial intelligence machine learning deep learning artificial neural network natural-language processing prostate cancer computer-aided diagnosis |
| title | The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review |
| title_full | The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review |
| title_fullStr | The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review |
| title_full_unstemmed | The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review |
| title_short | The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review |
| title_sort | application of artificial intelligence in prostate cancer management what improvements can be expected a systematic review |
| topic | artificial intelligence machine learning deep learning artificial neural network natural-language processing prostate cancer computer-aided diagnosis |
| url | https://www.mdpi.com/2076-3417/10/18/6428 |
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