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

Impact of preexisting interstitial lung disease on pneumonitis and survival during first-line immune checkpoint inhibitor-based therapy in non-small cell lung cancer.

European journal of cancer (Oxford, England : 1990)·2026
Same author

<i>Connexin 43</i> enhances liver metastatic ability of GIST cells <i>in vivo</i>.

Pathology oncology research : POR·2026
Same author

Senescence-associated tertiary lymphoid structures in Sjögren's disease model nishiura mice.

Mechanisms of ageing and development·2026
Same author

Systemic Inflammation Modifies the Efficacy of Bevacizumab Added to Atezolizumab Plus Chemotherapy for Advanced Non-Squamous Non-Small Cell Lung Cancer: A Multicenter Retrospective Study.

Thoracic cancer·2026
Same author

Macroscopic Tumor Color as a Surrogate for Biological Diversity in Clear Cell Renal Cell Carcinoma.

Anticancer research·2026
Same author

Steroid-Refractory Chronic Graft-Versus-Host Disease-Associated Isolated Myositis Successfully Treated With Ruxolitinib: A Case Report.

EJHaem·2026

Related Experiment Video

Updated: Apr 30, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

1.0K

Recurrence Risk Stratification in Clear Cell Renal Cell Carcinoma Using TNM 2017 and WHO/ISUP Grade.

Kosuke Hamada1, Takeshi Yamasaki2, Chisato Ohe3,4

  • 1Department of Urology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.

Anticancer Research
|April 28, 2026
PubMed
Summary
This summary is machine-generated.

Updated TNM 2017 classification and WHO/ISUP grading improve recurrence risk prediction for clear cell renal cell carcinoma (ccRCC). These advancements enhance prognostic accuracy and aid in postoperative risk stratification for localized ccRCC patients.

Keywords:
Renal cell carcinomaTNMgradere-evaluationrecurrencerisk

More Related Videos

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

9.9K
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

892

Related Experiment Videos

Last Updated: Apr 30, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

1.0K
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

9.9K
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

892

Area of Science:

  • Urology
  • Oncology
  • Pathology

Background:

  • Established recurrence risk models for clear cell renal cell carcinoma (ccRCC) require re-evaluation.
  • The tumor, node, metastasis (TNM) 2017 classification and the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system are key updates.

Purpose of the Study:

  • To re-evaluate ccRCC recurrence risk models using TNM 2017 and WHO/ISUP grading.
  • To assess if these updates improve the predictive accuracy of recurrence risk models.

Main Methods:

  • Retrospective analysis of 295 non-metastatic ccRCC patients.
  • Re-evaluation of pathological features using TNM 2009, TNM 2017, Fuhrman, and WHO/ISUP grading systems.
  • Assessment of recurrence risk stratification using Leibovich models and prognostic accuracy via C-index.

Main Results:

  • TNM 2017 led to more frequent pathological upstaging to pT3a in cT1 cases (18.9% vs. 16.0%).
  • TNM 2017 and WHO/ISUP grade showed superior prognostic performance compared to TNM 2009 and Fuhrman grade, respectively.
  • The Leibovich 2003 model improved with updated classifications, and the Leibovich 2018 model achieved the highest predictive accuracy (C-index, 0.870).

Conclusions:

  • Re-evaluation with TNM 2017 and WHO/ISUP grading improves predictive accuracy for localized ccRCC recurrence.
  • These updated classifications aid in postoperative risk stratification for ccRCC patients.