Harnessing artificial intelligence for prostate cancer management
Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved..
Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:5 |
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| Enthalten in: |
Cell reports. Medicine - 5(2024), 4 vom: 16. Apr., Seite 101506 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhu, Lingxuan [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 19.04.2024 Date Revised 26.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.xcrm.2024.101506 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM370831497 |
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| 520 | |a Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies | ||
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| 700 | 1 | |a Deng, Longxin |e verfasserin |4 aut | |
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| 700 | 1 | |a Pareek, Gyan |e verfasserin |4 aut | |
| 700 | 1 | |a Hyams, Elias |e verfasserin |4 aut | |
| 700 | 1 | |a Carneiro, Benedito A |e verfasserin |4 aut | |
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| 700 | 1 | |a Tan, Tao |e verfasserin |4 aut | |
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