Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Is There a Golden Hour for Thrombectomy in Intermediate-Risk Pulmonary Embolism? Insights From SYMPHONY-PE.

Circulation. Cardiovascular interventions·2026
Same author

Application of Sparse Autoencoders to Enhance Mechanistic Interpretability of Large Language Models in Medicine.

JMIR AI·2026
Same author

Reply to 'Comment on novel MRI-US fusion with advanced annotation in focal cryoablation for prostate cancer'.

BJU international·2026
Same author

Understanding Current Trends and Advances in Transarterial Radioembolization Dosimetry.

Diagnostics (Basel, Switzerland)·2026
Same author

Navigating the Frontier of artificial intelligence implementation in radiology - part 1: Performance assessment.

The neuroradiology journal·2025
Same author

Glue (n-Butyl Cyanoacrylate) for Prostate Artery Embolization: Development of a Glue Penetration Score and Association with Clinical Outcomes.

Journal of vascular and interventional radiology : JVIR·2025

Related Experiment Video

Updated: Jun 16, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.2K

Predicting Early Outcomes of Prostatic Artery Embolization Using n-Butyl Cyanoacrylate Liquid Embolic Agent: A

Burak Berksu Ozkara1, David Bamshad1, Ramita Gowda2

  • 1Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Diagnostics (Basel, Switzerland)
|June 13, 2025
PubMed
Summary

Machine learning accurately predicts short-term success for prostatic artery embolization (PAE) in benign prostatic hyperplasia patients. Key factors include pre-procedure symptom score and embolization volume, enabling personalized treatment assessments.

Keywords:
IPSSartificial intelligencemachine learningprostate artery embolization

More Related Videos

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

3.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jun 16, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.2K
Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

3.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • Urology
  • Interventional Radiology
  • Medical Informatics

Background:

  • Prostatic artery embolization (PAE) is a growing treatment for benign prostatic hyperplasia (BPH)-related lower urinary tract symptoms.
  • A subset of patients do not achieve clinical improvement after PAE.
  • Machine learning (ML) offers potential for predicting PAE outcomes.

Purpose of the Study:

  • To develop and validate a ML model for predicting short-term favorable outcomes after PAE.
  • To identify key clinical variables influencing PAE success.

Main Methods:

  • Retrospective cohort study of 109 patients undergoing PAE with nBCA glue.
  • A binary classification model predicted a >9-point reduction in International Prostate Symptom Score (IPSS) at 7 weeks.
  • SHapley Additive Explanations (SHAP) identified influential features.

Main Results:

  • The ML model achieved an AUROC of 0.821 and AUPRC of 0.851.
  • Key predictors included pre-PAE IPSS, prior therapy, embolization volume, preoperative quality of life, and age.
  • The model demonstrated promising accuracy (0.676) and calibration.

Conclusions:

  • The developed ML model shows potential for predicting early PAE outcomes.
  • Interpretable ML models can support precision medicine for individualized risk assessment in PAE.
  • This approach may optimize treatment selection for BPH patients.