Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm

doi:10.1093/jnci/djae139

Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm

2024-06-18

2

PRE

AI

C

Chien‐Kuang Cornelia Ding

Lia De Paula Oliveira

Christian P. Pavlovich

C

Claire M. de la Calle

T

Tamara L. Lotan *

DOI：10.1093/jnci/djae139

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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.

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Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm

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