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

Regenerative Polysulfide-Scavenging Layers Enabling Lithium-Sulfur Batteries with High Energy Density and Prolonged Cycling Life.

ACS nano·2017
Same author

PdAuCu Nanobranch as Self-Repairing Electrocatalyst for Oxygen Reduction Reaction.

ChemSusChem·2017
Same author

Trapdoor spiders of the genus <i>Cyclocosmia</i> Ausserer, 1871 from China and Vietnam (Araneae, Ctenizidae).

ZooKeys·2017
Same author

The complete genome sequence, occurrence and host range of Tomato mottle mosaic virus Chinese isolate.

Virology journal·2017
Same author

Tunneling nanotubes promote intercellular mitochondria transfer followed by increased invasiveness in bladder cancer cells.

Oncotarget·2017
Same author

Assessment of histopathological features of needle biopsy in recurrent prostate cancer following salvage high-intensity focused ultrasound.

Canadian Urological Association journal = Journal de l'Association des urologues du Canada·2017

Related Experiment Video

Updated: Jan 8, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

1.1K

Optimizing recurrence prediction and risk stratification in prostate cancer using a 2.5D deep learning model: a

Fan Li1, Ruishan Liu1, Pei Wang2

  • 1Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.

International Journal of Surgery (London, England)
|December 19, 2025
PubMed
Summary

A new deep learning fusion (DLF) model accurately predicts prostate cancer recurrence after surgery. This AI tool aids in identifying high-risk patients for optimized treatment and surveillance strategies.

Keywords:
2.5DMRIdeep learningprostate cancertransformer

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

470
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

7.3K

Related Experiment Videos

Last Updated: Jan 8, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

1.1K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

470
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

7.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate cancer (PCa) recurrence post-surgery is a major clinical challenge.
  • Accurate prediction of biochemical recurrence (BCR) is crucial for patient management.
  • Current prediction methods require enhancement for improved accuracy.

Purpose of the Study:

  • To develop and validate a deep learning fusion (DLF) model for preoperative prediction of BCR in PCa.
  • To assess the DLF model's capability for risk stratification.
  • To compare the DLF model's performance against existing clinical and machine learning models.

Main Methods:

  • A Transformer-based deep learning (DL) model was developed using multi-parametric MRI (mpMRI) data (T2WI, ADC, DWI, CE-T1WI).
  • A DLF model integrated DL scores with clinical variables.
  • Performance was evaluated using ROC curves, DeLong test, DCA, and calibration curves in a cohort of 923 PCa patients.

Main Results:

  • The DLF model achieved high predictive performance with AUCs of 0.938 (internal validation) and 0.935 (external test set).
  • The DLF model significantly outperformed clinical models (CAPRA, Clinical) and other machine learning models.
  • The DLF model effectively stratified patients into high- and low-risk groups, demonstrating significant prognostic differences.

Conclusions:

  • The developed DLF model shows strong potential for predicting early postoperative recurrence in PCa.
  • This AI-driven approach can aid in identifying high-risk patients for tailored treatment and surveillance.
  • The DLF model offers a valuable tool for optimizing prostate cancer management strategies.