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

Feasibility and exploratory outcomes of immersive VR intervention on brain functional networks and clinical symptoms in ADHD children: A pilot study.

Psychiatry research. Neuroimaging·2026
Same author

Neural Correlates of Virtual Reality Intervention in Children With Attention-Deficit/Hyperactivity Disorder: A Resting-State fMRI Study Based on Percent Amplitude of Fluctuation.

Brain and behavior·2026
Same author

Multiparametric MRI-derived biomarkers for preoperative prediction of recurrence and/or metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Quantitative imaging in medicine and surgery·2026
Same author

MRI Habitat Analysis for Preoperative Prediction of Perineural Invasion and Prognostic Stratification in Rectal Cancer.

Journal of magnetic resonance imaging : JMRI·2026
Same author

An intelligent fusion model for Ki-67 prediction in non-small cell lung cancer: A cloud-based prediction system integrating radiomics.

European journal of radiology·2026
Same author

Clinical evaluation of deep learning accelerated lumbar T2-weighted and fat-suppressed MRI sequences.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026

Related Experiment Video

Updated: Jul 24, 2025

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

6.9K

Deep learning-based radiomic nomograms for predicting Ki67 expression in prostate cancer.

Shuitang Deng1, Jingfeng Ding2, Hui Wang1

  • 1Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234 Gucui Road, Zhejiang Province, 310012, Hangzhou, China.

BMC Cancer
|July 8, 2023
PubMed
Summary

Deep learning models accurately predict Ki67 expression in prostate cancer (PCa) using multiparametric MRI. These models offer valuable preoperative prognostic data for surgical planning.

Keywords:
Deep learningKi67PrognosisProstate cancer

More Related Videos

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K

Related Experiment Videos

Last Updated: Jul 24, 2025

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

6.9K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K

Area of Science:

  • Radiology
  • Oncology
  • Artificial Intelligence

Background:

  • Prostate cancer (PCa) management benefits from accurate Ki67 expression prediction.
  • Multiparametric magnetic resonance imaging (MRI) offers potential for non-invasive assessment.

Purpose of the Study:

  • To evaluate the efficacy of a deep learning model based on multiparametric MRI for preoperative Ki67 expression prediction in PCa.
  • To compare the performance of deep learning models against traditional clinical models.

Main Methods:

  • Retrospective analysis of 229 PCa patients from two centers.
  • Extraction and selection of deep learning features from multiparametric MRI sequences (DWI, T2WI, CE-T1WI).
  • Development and validation of clinical, deep learning (DLRS-Resnet, DLRS-Inception, DLRS-Densenet), and joint (Nomogram) models.

Main Results:

  • Deep learning and joint models achieved high predictive performance with AUCs ranging from 0.939 to 0.993.
  • Deep learning and joint models significantly outperformed the clinical model (AUCs 0.794-0.75) (p < 0.01).
  • Specific comparisons showed Nomogram-Resnet superior to DLRS-Resnet, with other models showing no significant differences.

Conclusions:

  • Developed deep learning models provide accurate, easy-to-use tools for preoperative Ki67 prediction in PCa.
  • These models can furnish physicians with crucial prognostic information prior to surgery.