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

Modified MXene Aerogel With Broadband Microwave Absorption Inspired by Melanophila Acuminata Beetle.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Large-scale multi-sequence pretraining for generalizable MRI analysis in versatile clinical applications.

Nature biomedical engineering·2026
Same author

AARS2-mediated lactylation of ULK1 promotes autophagy-dependent progression of clear cell renal cell carcinoma.

Autophagy·2026
Same author

The E3 ligase TRIM29 drives renal ischemia-reperfusion injury by targeting DUSP10 for proteasomal degradation.

Cell death & disease·2026
Same author

Association between concomitant ACEI/ARBs use and survival of patients with prostate cancer receiving abiraterone acetate: A post-hoc analysis of two randomized trials.

Prostate cancer and prostatic diseases·2026
Same author

A universal foundation model for grounded biomedical image interpretation.

Nature communications·2026

Related Experiment Video

Updated: Jun 3, 2026

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

Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical

Yunhan Luo1,2, Yatian Wang3, Xiangpeng Zou1,2

  • 1Department of Urology, Sun Yat-sen University Cancer Center, Guangzhou, China.

Nature Communications
|May 28, 2026
PubMed
Summary

A new deep learning model predicts rapid kidney function decline after radical nephrectomy for complex kidney cancer. This tool aids urologists in choosing between partial and radical nephrectomy, potentially preserving renal function.

More Related Videos

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

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

Related Experiment Videos

Last Updated: Jun 3, 2026

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

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

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

Area of Science:

  • Nephrology
  • Oncology
  • Artificial Intelligence

Background:

  • Deciding between partial nephrectomy (PN) and radical nephrectomy (RN) for complex renal cell carcinoma (RCC) is challenging.
  • Rapid glomerular filtration rate (GFR) decline post-RN indicates abnormal renal function and impacts treatment choices.

Purpose of the Study:

  • To develop and validate a multimodal deep learning model to predict rapid GFR decline after RN.
  • To assist urologists in treatment decisions for complex RCC patients.

Main Methods:

  • Retrospective analysis of contrast-enhanced computed tomography images and clinical data from 1621 patients.
  • Development of a multimodal deep learning model for predicting GFR decline.
  • External validation of the model's predictive performance.

Main Results:

  • The deep learning model achieved an area under the curve of 0.788-0.873 in external test sets.
  • The model successfully stratified patients into high- and low-risk groups for chronic kidney disease progression.
  • The model shows potential for aiding clinical decision-making in complex RCC cases.

Conclusions:

  • A deep learning model can predict rapid GFR decline after RN with high accuracy.
  • This predictive tool can support urologists in selecting nephrectomy strategies for complex RCC.
  • The model may help preserve renal function by guiding the decision towards PN when feasible.