Related Experiment Video
Updated: Sep 11, 2025

06:46
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
376
Prognostic Stratification of pN1 Prostate Cancer After Radical Prostatectomy: A Competing Risk Analysis from a
Alexander Giesen1,2, Daimantas Milonas1,3, Annouschka Laenen4
1Department of Urology, University Hospitals Leuven, Leuven, Belgium.
European Urology Open Science
|August 18, 2025
Summary
Prostate cancer patients with positive lymph nodes (pN1) have varied outcomes. A new model using pT stage, surgical margins, and lymph node count stratifies risk, aiding personalized treatment decisions.
Area of Science:
- Oncology
- Urology
- Pathology
Background:
- Prostate cancer (PCa) with lymph node-positive (pN1) status is highly variable.
- Accurate prognostic grouping is essential for pN1 patients.
- Cancer-related mortality (CRM) risk stratification is needed.
Purpose of the Study:
- To assess CRM in different prognostic groups of pN1 patients.
- To develop a prognostic model based on pathological PCa characteristics and lymph node count.
- To predict CRM considering competing risks.
Main Methods:
- Retrospective, multicentre cohort study of 894 pN1 patients.
- Identification and pooling of independent predictors for CRM.
- Construction of a prognostic model using pT stage, surgical margin (SM) status, and lymph node-positive (LN+) count.
Main Results:
- A three-group prognostic model was developed (favorable, intermediate, poor).
- Key predictors included pT stage, SM status, and LN+ count.
- 10-year cumulative CRM rates were 12%, 32%, and 58% for the respective groups (p < 0.005).
Conclusions:
- The pN1 patient population exhibits significant heterogeneity.
- Primary cancer characteristics remain key drivers of CRM in pN1 disease.
- The model aids in personalized clinical decision-making for pN1 patients post-surgery.
Related Concept Videos
Cancer Survival Analysis
453
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
453
Comparing the Survival Analysis of Two or More Groups
285
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
285

