ArticleJCO clinical cancer informatics2025
Artificial Intelligence-Based Digital Histologic Classifier for Prostate Cancer Risk Stratification: Independent Blinded Validation in Patients Treated With Radical Prostatectomy.
Article in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- 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.Future oncology (London, England) · 2026Article
- Cancer in transition: discovery of tumor-intrinsic transcriptional programs shaping the immune and microenvironmental landscape.Biomarker research · 2026Review
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- The Evolving Landscape of Novel and Old Biomarkers in Localized High-Risk Prostate Cancer: State of the Art, Clinical Utility, and Limitations Toward Precision Oncology.Journal of personalized medicine · 2025Review
- Biomarkers in Localized Prostate Cancer: From Diagnosis to Treatment.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
purposeArtificial intelligence (AI) tools that identify pathologic features from digitized whole-slide images (WSIs) of prostate cancer (CaP) generate data to predict outcomes. The objective of this study was to evaluate the clinical validity of an AI-enabled prognostic test, PATHOMIQ_PRAD, using a clinical cohort from the Cleveland Clinic.
methodsWe conducted a retrospective analysis of PATHOMIQ_PRAD using CaP WSIs from patients who underwent radical prostatectomy (RP) between 2009 and 2022 and did not receive adjuvant therapy. Patients also had Decipher genomic testing available. WSIs were deidentified, anonymized, and outcomes were blinded. Patients were stratified into high-risk and low-risk categories on the basis of predetermined thresholds for PATHOMIQ_PRAD scores (0.45 for biochemical recurrence [BCR] and 0.55 for distant metastasis [DM]).
resultsThe study included 344 patients who underwent RP with a median follow-up of 4.3 years. Both PathomIQ and Decipher scores were associated with rates of biochemical recurrence-free survival (BCRFS; PathomIQ score >0.45
conclusionThese findings independently validate PATHOMIQ_PRAD as a reliable predictor of clinical risk in the postprostatectomy setting. PATHOMIQ_PRAD therefore merits prospective evaluation as a risk stratification tool to select patients for adjuvant or early salvage interventions.
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