Future oncology (London, England) · 2026
Development and validation of a digital pathology artificial intelligence (DPAI)-derived risk score predicting Gleason grade group reclassification for patients who are candidates for active surveillance.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Mabey Brent, Lenz Lauren H, Schiewer Matthew J, Rayford Walter, Muhammad Hassan, Huang Wei, Finch Robert, Nakamoto Christina, Kouros-Mehr Hosein, Jasper Jeff, Basu Hirak, Feng Chao, Sharma Anurag, Wilding George, Roy Rajat, Muzzey Dale, Gutin Alexander
Аннотация
AIMS: Active surveillance (AS) allows selected men with localized prostate cancer to defer curative therapy and reduce treatment morbidity. Conversion from AS to treatment is commonly triggered by Gleason grade group (GGG) upgrading on confirmatory biopsy. We developed and validated a digital pathology artificial intelligence (DPAI)-derived risk score to predict GGG upgrading in AS-eligible patients.
MATERIALS AND METHODS: The DPAI model was trained using histopathology image features from diagnostic biopsies of 998 patients and validated in an independent cohort of 296 patients meeting criteria for AS. Logistic regression estimated the probability of confirmatory-biopsy GGG increase, and feature selection identified the most predictive variables.
RESULTS: AI-GUR (Artificial Intelligence-Gleason Upgrade Risk) predicted GGG reclassification at confirmatory biopsy (OR 1.60; p = 0.0003) and provided information beyond conventional stratification (risk group, CAPRA) and cribriform morphology (all p < 0.01). Predicted risks were similar across time from diagnosis (~10-15% to ~85% at 1, 1.5, or 2 years; p for time = 0.50), consistent with initial biopsy mischaracterization rather than time-dependent progression.
CONCLUSIONS: AI-GUR provides individualized estimates of confirmatory-biopsy GGG upgrading for AS candidates. Using DPAI may improve shared decision-making by complementing standard clinicopathologic tools and molecular testing using the same biopsy specimen, while informing the likelihood of grade upgrade at confirmation.
MATERIALS AND METHODS: The DPAI model was trained using histopathology image features from diagnostic biopsies of 998 patients and validated in an independent cohort of 296 patients meeting criteria for AS. Logistic regression estimated the probability of confirmatory-biopsy GGG increase, and feature selection identified the most predictive variables.
RESULTS: AI-GUR (Artificial Intelligence-Gleason Upgrade Risk) predicted GGG reclassification at confirmatory biopsy (OR 1.60; p = 0.0003) and provided information beyond conventional stratification (risk group, CAPRA) and cribriform morphology (all p < 0.01). Predicted risks were similar across time from diagnosis (~10-15% to ~85% at 1, 1.5, or 2 years; p for time = 0.50), consistent with initial biopsy mischaracterization rather than time-dependent progression.
CONCLUSIONS: AI-GUR provides individualized estimates of confirmatory-biopsy GGG upgrading for AS candidates. Using DPAI may improve shared decision-making by complementing standard clinicopathologic tools and molecular testing using the same biopsy specimen, while informing the likelihood of grade upgrade at confirmation.