Development and Validation of Interpretable Machine Learning Models for Clinically Significant Prostate Cancer Diagnosis in Patients With Lesions of PI-RADS v2.1 Score ≥3.

Journal: Journal of magnetic resonance imaging : JMRI

PMID: 38363125

Abstract

BACKGROUND: For patients with PI-RADS v2.1 ≥ 3, prostate biopsy is strongly recommended. Due to the unsatisfactory positive rate of biopsy, improvements in clinically significant prostate cancer (csPCa) risk assessments are required.

Authors

Mingjian Ruan

Department of Urology, Peking University First Hospital, Beijing, China.
Yi Liu

Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China.
Kaifeng Yao

Department of Urology, Peking University First Hospital, Beijing, China.
Kexin Wang

Clifford Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yu Fan

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA. YFan1@mdanderson.org.
Shiliang Wu

Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, PR China.
XiaoYing Wang

Keywords

Aged Biopsy Diffusion Magnetic Resonance Imaging Echo-Planar Imaging Humans Image Interpretation, Computer-Assisted Machine Learning Magnetic Resonance Imaging Male Middle Aged Prostate Prostate-Specific Antigen Prostatic Neoplasms Reproducibility of Results Retrospective Studies Risk Assessment ROC Curve

External Resources

View on PubMed Access via DOI PubMed (38363125)

Development and Validation of Interpretable Machine Learning Models for Clinically Significant Prostate Cancer Diagnosis in Patients With Lesions of PI-RADS v2.1 Score ≥3.

Abstract

Authors

Keywords

External Resources

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