Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients.

Journal: The Prostate

PMID: 34783053

Abstract

BACKGROUND: Due to the low cancer-detection rate in patients with PIRADS category 3 lesions, we created machine learning (ML) models to facilitate decision-making about whether to perform prostate biopsies or monitor clinical information without biopsy results.

Authors

Pubordee Aussavavirojekul

Division of Urology, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Apirak Hoonlor

Computer Science Academic Group, Faculty of Information and Communication Technology, Mahidol University, Bangkok, Thailand.
Sittiporn Srinualnad

Division of Urology, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.

Keywords

Aged Humans Image-Guided Biopsy Machine Learning Magnetic Resonance Imaging Male Prostate Prostate-Specific Antigen Prostatic Neoplasms Public Reporting of Healthcare Data Quality Improvement Research Design Risk Assessment Sensitivity and Specificity Ultrasonography, Interventional Unnecessary Procedures

External Resources

View on PubMed Access via DOI PubMed (34783053)

Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals