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

Redefining clinical success in minimally invasive surgery for BPH: A composite endpoint integrating ejaculatory function, urinary improvement, and safety metrics.

Urologia·2026

Same author

Delphi consensus on ex vivo fluorescence confocal microscopy in robot-assisted radical prostatectomy.

BJU international·2026

Same author

Beyond satisfaction scores: quantifying communication quality in oncology care.

Prostate cancer and prostatic diseases·2026

Same author

External validation of the prostatic inflammation nomogram to predict prostatic inflammation in men with lower urinary tract symptoms.

Minerva urology and nephrology·2026

Same author

Beyond One-Size-Fits-All Active Surveillance for Low-Risk Prostate Cancer: Risk-Adapted Follow-Up, De-Escalation Pathways, and Focal Therapy as Tailored Strategy.

Diagnostics (Basel, Switzerland)·2026

Same author

Improved Early Urinary Continence After Robot-Assisted Radical Prostatectomy Using a Modified Vesicourethral Anastomosis with Posterior Musculofascial Reconstruction: A Prospective Comparative Study.

Journal of clinical medicine·2026

Same journal

Evidence-Based Clinical Recommendations for the Appropriate Use of Diagnostic Tests in Pediatric Allergology: Focus on Asthma, Rhinoconjunctivitis, and Keratoconjunctivitis Vernal.

Journal of clinical medicine·2026

Same journal

Surgical and Transcatheter Approach of a Failed Mitral Valve Repair: A Comprehensive Review on Selecting the Most Suitable Approach.

Journal of clinical medicine·2026

Same journal

Hybrid Metaheuristic Feature Selection for Breast Cancer Detection in Digital Mammography: A Feasibility Study with Nested Validation, Benchmarking, and External Stress Testing.

Journal of clinical medicine·2026

Same journal

Identity Transformation and the Role of Accountability in Recovery from Problematic Pornography Use: A Phenomenological-Hermeneutical Study.

Journal of clinical medicine·2026

Same journal

Does Early Surgical Treatment in Degenerative Cervical Myelopathy Have a Favorable Clinical Outcome and Impact on Quality of Life?

Journal of clinical medicine·2026

Same journal

Shear Wave Elastography in Musculoskeletal Imaging: A Narrative Review.

Journal of clinical medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 23, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study.

Enrico Checcucci¹, Samanta Rosati², Sabrina De Cillis³

¹Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, 10060 Turin, Italy.

Journal of Clinical Medicine

|July 14, 2023

Summary

This summary is machine-generated.

This study introduces a personalized predictive model using machine learning to identify prostate cancer (PCa) before biopsy. The fuzzy logic + support vector machine model accurately predicts PCa, potentially avoiding unnecessary procedures.

Keywords:

artificial intelligence machine learning prostate biopsy prostate cancer

More Related Videos

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

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

Related Experiment Videos

Last Updated: Jul 23, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

Area of Science:

Urology
Medical Imaging
Machine Learning

Background:

Prostate cancer (PCa) diagnosis often involves invasive procedures like fusion biopsy (FB).
Accurate pre-biopsy risk stratification is crucial for patient management and resource optimization.

Purpose of the Study:

To develop and validate a personalized predictive model (PPM) using machine learning (ML) for identifying patients with suspected prostate cancer (PCa) after multiparametric MRI (mpMRI).
To assess the performance of a fuzzy inference system (FIS) combined with support vector machine (SVM) against other ML methods and standard diagnostic tools.

Main Methods:

Utilized a dataset of 1448 patients for training and 181 for validation, all undergoing mpMRI and FB.
Developed and compared four ML methods: FIS, SVM, k-nearest neighbors (KNN), and self-organizing maps (SOMs).
Evaluated a fuzzy logic (FL) + SVM system against logistic regression (LR) and standard diagnostic tools, focusing on metrics like AUC, NPV, specificity, sensitivity, and accuracy.

Main Results:

The FIS + SVM model showed performance comparable to LR in AUC but superior to other tools.
On the training set, FIS + SVM achieved the highest NPV (78.5%) and specificity (92.1% vs. 83% for LR).
In the validation set, FIS + SVM outperformed other methods with an NPV of 60.7%, sensitivity of 90.8%, and accuracy of 69.1%.

Conclusions:

A validated personalized predictive model (PPM) using FIS + SVM was successfully developed to predict PCa probability before FB.
This tool can help avoid unnecessary biopsies in approximately 15% of cases.
The model offers a promising approach for improving the diagnostic pathway for suspected prostate cancer.