ArticleNPJ digital medicine2024
Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this 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.
Clinical Validation of ClarityDX Prostate as a Reflex Test to Prostate Specific Antigen (PSA) to Refine the Prediction of Clinically-significant Prostate Cancer
Establishing the Clinical Utility of ClarityDX Prostate
Who cites it
6 citing papers in PubMed.
- Identification of clinical phenotypes and development of a predictive model for rapid lung function decline in COPD patients.Annals of medicine · 2026Article
- Predicting clinically significant prostate cancer with or without digital rectal exam and MRI data using ClarityDX Prostate models.NPJ digital medicine · 2026Article
- Development and Deployment of a Machine Learning Model to Triage the Use of Prostate MRI (ProMT-ML) in Patients With Suspected Prostate Cancer.Journal of magnetic resonance imaging : JMRI · 2026Article
- Utility of the PROSTest, a Novel Blood-Based Molecular Assay, Versus PSA for Prostate Cancer Stratification and Detection of Disease.The Prostate · 2026Article
- Computational frameworks for enhanced extracellular vesicle biomarker discovery.Experimental & molecular medicine · 2026Review
- Development and validation of a tool to assess the knowledge, awareness, and practice toward the screening of prostate cancer among adult men.Journal of education and health promotion · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa. Integrating the ClarityDX platform with blood-based biomarkers for clinically significant PCa and clinical biomarker data from a 3448-patient cohort, we developed a test to stratify patients' risk of csPCa; called ClarityDX Prostate. When predicting high risk cancer in the validation cohort, ClarityDX Prostate showed 95% sensitivity, 35% specificity, 54% positive predictive value, and 91% negative predictive value, at a ≥ 25% threshold. Using ClarityDX Prostate at this threshold could avoid up to 35% of unnecessary prostate biopsies. ClarityDX Prostate showed higher accuracy for predicting the risk of csPCa than PSA alone and the tested model-based risk calculators. Using this test as a reflex test in men with elevated PSA levels may help patients and their healthcare providers decide if a prostate biopsy is necessary.
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.