Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm.
J Natl Cancer Inst
; 116(10): 1683-1686, 2024 Oct 01.
Article
en En
| MEDLINE
| ID: mdl-38889303
ABSTRACT
Deep learning (DL)-based algorithms to determine prostate cancer (PCa) Grade Group (GG) on biopsy slides have not been validated by comparison to clinical outcomes. We used a DL-based algorithm, AIRAProstate, to regrade initial prostate biopsies in 2 independent PCa active surveillance (AS) cohorts. In a cohort initially diagnosed with GG1 PCa using only systematic biopsies (n = 138), upgrading of the initial biopsy to ≥GG2 by AIRAProstate was associated with rapid or extreme grade reclassification on AS (odds ratio = 3.3, P = .04), whereas upgrading of the initial biopsy by contemporary uropathologist reviews was not associated with this outcome. In a contemporary validation cohort that underwent prostate magnetic resonance imaging before initial biopsy (n = 169), upgrading of the initial biopsy (all contemporary GG1 by uropathologist grading) by AIRAProstate was associated with grade reclassification on AS (hazard ratio = 1.7, P = .03). These results demonstrate the utility of a DL-based grading algorithm in PCa risk stratification for AS.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias de la Próstata
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Algoritmos
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Espera Vigilante
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Clasificación del Tumor
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Aprendizaje Profundo
Límite:
Aged
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
J Natl Cancer Inst
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J. natl. cancer inst
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Journal of the national cancer institute
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos