ArticleNpj imaging2024
Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images.
Article in Npj imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Raman spectroscopic signatures of prostate cancer progression: correlation with gleason score.Molecular and cellular biochemistry · 2026Article
- The evolution of prostate cancer grading: from Gleason score to risk taxonomy and the artificial intelligence revolution.Virchows Archiv : an international journal of pathology · 2026Review
- Artificial Intelligence-Based Digital Histologic Classifier for Prostate Cancer Risk Stratification: Independent Blinded Validation in Patients Treated With Radical Prostatectomy.JCO clinical cancer informatics · 2025Article
- Development of a deep learning system for predicting biochemical recurrence in prostate cancer.BMC cancer · 2025Article
- Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review.Cancers · 2024Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malignancy grading of prostate cancer (PCa) is fundamental for risk stratification, patient counseling, and treatment decision-making. Deep learning has shown potential to improve the expert consensus for tumor grading, which relies on the Gleason score/grade grouping. However, the core problem of interobserver variability for the Gleason grading system remains unresolved. We developed a novel grading system for PCa and utilized artificial intelligence (AI) and multi-institutional international datasets from 2647 PCa patients treated with radical prostatectomy with a long follow-up of ≥10 years for biochemical recurrence and cancer-specific death. Through survival analyses, we evaluated the novel grading system and showed that AI could develop a tumor grading system with four risk groups independent from and superior to the current five grade groups. Moreover, AI could develop a scoring system that reflects the risk of castration resistant PCa in men who have experienced biochemical recurrence. Thus, AI has the potential to develop an effective grading system for PCa interpretable by human experts.
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.