Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Diagnostic Performance of Prostate Cancer Disease-Specific Phenotypes Identified Using Real-World Databases: A Systematic Review.

Pharmacoepidemiology and drug safety - 1 Oct 2025

Vyas Ami, Kamat Shweta, Thomas Sadie, Gambino Connor, Brown Britny R, Raval Amit D

Abstract excerpt

BACKGROUND: Research using real-world databases (RWD) often requires the development of computable phenotypes based on clinical reasoning-based algorithms or prediction models with validation through a reference standard such as chart review. While there are studies reporting different phenotypes for key prostate cancer (PC) disease or outcomes, these have not been summarized systematically. OBJECTIVES: To...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Databases, Factual

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to BCR gene

Share to PC gene

Share to Phenotype

Share to Progression-Free Survival

Share to Prostatic Neoplasms

Share to Prostatic Neoplasms, Castration-Resistant

Source

Open in PubMed Open full text

Identifiers

PMID: 41093316
DOI: 10.1002/pds.70236
PMCID: PMC12527646

Genes

PC
BCR

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Diagnostic Performance of Prostate Cancer Disease-Specific Phenotypes Identified Using Real-World Databases: A Systematic Review.PMID 41093316Read ↗

Development and Evaluation of an Electronic Health Record-Derived Computable Phenotype to Identify Patients Undergoing Prostate Cancer Screening.Semantic proximity Read ↗

Identification and Validation of the Prognostic Impact of Metastatic Prostate Cancer Phenotypes.Semantic proximity Read ↗

Comparison of Real-World Outcomes between Patients with BRCA1/2-Positive and Homologous Recombination Repair-Negative Metastatic Castration-Sensitive Prostate CancerSemantic proximity Read ↗

Deep learning from routine histology improves risk stratification for biochemical recurrence in prostate cancerSemantic proximity Read ↗

Genomic Classifiers and Prognosis of Localized Prostate Cancer: A Systematic ReviewSemantic proximity Read ↗

Clinical Annotations for Prostate Cancer Research: Defining Data Elements, Creating a Reproducible Analytical Pipeline, and Assessing Data QualitySemantic proximity Read ↗

VALIDATION OF PROGRESS, A SIMPLE MACHINE-LEARNING DERIVED RISK STRATIFICATION SCORE FOR CASTRATION-RESISTANT PROSTATE CANCERSemantic proximity Read ↗

A bibliometric analysis of use of Machine learning and Artificial intelligence in Prostate Cancer DetectionSemantic proximity Read ↗

Real-world treatment patterns and genetic testing in a metastatic castration-resistant prostate cancer setting in EuropeSemantic proximity Read ↗

Incorporating Structured and Unstructured Data Sources to Identify and Characterize Hereditary Cancer Testing Among Veterans With Metastatic Castration-Resistant Prostate Cancer.Semantic proximity Read ↗

Clinical phenotypes of castration-resistant prostate cancer.Semantic proximity Read ↗

Analysis of the Role of Alkaline Phosphatase and Gleason Score in Predicting Prognosis and Risk of Bone Metastasis in Prostate CancerSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.