Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Myeloid sarcoma of the sternum with regional lymph node involvement in a 6-year-old child: a case report.

Journal of cardiothoracic surgery·2026

Same author

Uncharted territory: assessing antibiotic adverse drug events from walk-in clinics at an academic healthcare system.

Antimicrobial stewardship & healthcare epidemiology : ASHE·2026

Same author

Quantitative radiomics and morphometry analysis of brain sub-regions reveals neurodevelopmental alterations in children with Tetralogy of Fallot.

Brain research bulletin·2026

Same author

Reproducibility of MRI Radiomics Measurements in Men with Prostate Cancer Undergoing Active Surveillance.

Cancers·2026

Same author

Impulsive responding increases during a laboratory model of a cocaine binge in individuals who use cocaine: A preliminary study.

Addiction and substance abuse·2026

Same author

Association Between Availability of Fruits and Vegetables in Neighborhood Food Stores and Weight Among Residents of Low-Income Urban Public Housing: Cross-Sectional Study.

JMIR formative research·2026

Search research articles

Related Experiment Video

Updated: Jun 22, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Techniques and predictive models to improve prostate cancer detection.

Michael P Herman¹, Philip Dorsey, Majnu John

¹Department of Urology, New York Presbyterian Hospital-Weill Cornell Medical Center, New York, New York, USA.

|June 23, 2009

Summary

Prostate-specific antigen (PSA) screening is controversial. Complex statistical models improve prostate cancer risk prediction accuracy beyond simple PSA tests, aiding patient counseling and clinical trial design.

More Related Videos

A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance

A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance

Published on: November 7, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Related Experiment Videos

Last Updated: Jun 22, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance

A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance

Published on: November 7, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Oncology
Medical Statistics
Biomarker Research

Background:

Prostate-specific antigen (PSA) screening for prostate cancer remains controversial due to limitations in predictive value.
Previous modifications to PSA measurements, such as PSA density and isoforms, have yielded limited success in improving accuracy.
Accurate prostate cancer risk assessment is crucial for patient management and clinical trial stratification.

Purpose of the Study:

To review methods for modifying PSA measurements and predictive models for prostate cancer detection.
To describe the mathematical principles, strengths, and weaknesses of these advanced techniques.
To assess the accuracy of these models compared to traditional methods and clinical judgment.

Main Methods:

Review of literature on PSA modifications and statistical/computational predictive models.
Analysis of mathematical underpinnings of various risk assessment techniques.
Evaluation of the accuracy and clinical utility of these models in prostate cancer detection.

Main Results:

Complex statistical and computational models demonstrate improved accuracy in predicting prostate cancer risk compared to physician judgment.
Modified PSA measurements alone have shown limited success in enhancing predictive value.
These advanced models offer a more nuanced approach to risk assessment than standard PSA testing.

Conclusions:

Advanced statistical and computational models are essential tools for improving prostate cancer risk assessment.
Understanding the design and limitations of these models is critical to prevent inappropriate application and misinterpretation.
These models are vital for patient counseling and stratifying participants in clinical trials for prostate cancer research.