ArticleDiagnostics (Basel, Switzerland)2022
AI Model for Prostate Biopsies Predicts Cancer Survival.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions.Prostate international · 2025Review
- The Role of Artificial Intelligence in the Evaluation of Prostate Pathology.Pathology international · 2025Review
- Development of a deep learning system for predicting biochemical recurrence in prostate cancer.BMC cancer · 2025Article
- Robust, credible, and interpretable AI-based histopathological prostate cancer grading.medRxiv : the preprint server for health sciences · 2024Article
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Article
- Harnessing machine learning to predict prostate cancer survival: a review.Frontiers in oncology · 2024Review
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016-2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016-2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist's diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist's grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11-8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15-1.7; G5 OR: 5.58; 95% CI 0.45-3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2-1.8; G5 OR: 4.09; 95% CI 0.22-3). Algorithm-detected grade group 3-5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1-2 (HR: 5.91; 95% CI 1.96-17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.