AI-powered radiomics: revolutionizing detection of urologic malignancies.
Journal
Current opinion in urology
ISSN: 1473-6586
Titre abrégé: Curr Opin Urol
Pays: United States
ID NLM: 9200621
Informations de publication
Date de publication:
01 Jan 2024
01 Jan 2024
Historique:
medline:
7
12
2023
pubmed:
1
11
2023
entrez:
1
11
2023
Statut:
ppublish
Résumé
This review aims to highlight the integration of artificial intelligence-powered radiomics in urologic oncology, focusing on the diagnostic and prognostic advancements in the realm of managing prostate, kidney, and bladder cancers. As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation. Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.
Identifiants
pubmed: 37909882
doi: 10.1097/MOU.0000000000001144
pii: 00042307-990000000-00121
doi:
Types de publication
Review
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-7Informations de copyright
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
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