Analysis of prognostic factors for survival after curative resections of clear-cell renal cell carcinoma based on Bayesian networks
8 affiliations
- 1Department of Urology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital
- 2Department of Infectious Disease, The First Affiliated Hospital of Fujian Medical University
- 3Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital
- 4Department of Urology, Zhangzhou Affiliated Hospital of Fujian Medical University
- 5Department of Urology, The Second Affiliated Hospital of Fujian Medical University
- 6Department of Urology, Fujian Medical University Union Hospital
- 7Department of Anesthesiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital
- 8Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital
Abstract
Background: Accurate risk stratification following curative resection of non-metastatic clear cell renal cell carcinoma (nm-ccRCC) remains clinically challenging. This study aims to develop and validate Bayesian network (BN) models to predict survival in patients with nm-ccRCC.
Methods: A dual-center retrospective cohort was randomly stratified into a training set (N = 326) and an internal validation set (N = 140). External validation was performed using an independent external cohort (N = 170). BN models were constructed based on clinicopathologic characteristics that demonstrated statistical significance in univariate analyses of the training cohort and were assessed using receiver operating characteristic (ROC) curves, confusion matrices, calibration and decision curve analysis.
Results: The Bayesian analyses identified T stages as the principal determinant of 5-year overall survival (OS), while ISUP grades emerged as the dominant predictor for 3-year disease-free survival (DFS). The 5-year OS and 3-year DFS models achieved areas under the ROC curve (AUCs) of 0.878 and 0.867 in the internal validation cohort and 0.838 and 0.846 in the external validation cohort. Calibration slopes were close to unity with small intercepts across cohorts, and decision curve analysis showed net benefit exceeding that of treating all or no patients across a wide range of thresholds.
Conclusions: This study establishes BNs for the first time as a clinically actionable tool for postoperative risk prediction in nm-ccRCC, with promising discriminative and calibrated performance supporting their potential use in patient counseling, risk-stratified surveillance, and the selection of candidates for adjuvant therapy or clinical trials. Future prospective, multicenter studies are warranted to extend these predictions to a 5-year DFS endpoint and to refine the decision threshold for clinical translation.
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BibTeX
@article{zhan2026analysis,
title = {{Analysis of prognostic factors for survival after curative resections of clear-cell renal cell carcinoma based on Bayesian networks}},
author = {Bin Zhan and Yachun Chen and Hongqian Ke and Sixing Huang and Siwei Xu and Yi Huang and Xiaobao Chen and Xuru Zhang and Yuxin Wu and Wei Jiang and Ronghui Lin and Tianqi Lin and Shushang Chen},
journal = {Frontiers in Oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1877632},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1877632/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Analysis of prognostic factors for survival after curative resections of clear-cell renal cell carcinoma based on Bayesian networks
AU - Bin Zhan
AU - Yachun Chen
AU - Hongqian Ke
AU - Sixing Huang
AU - Siwei Xu
AU - Yi Huang
AU - Xiaobao Chen
AU - Xuru Zhang
AU - Yuxin Wu
AU - Wei Jiang
AU - Ronghui Lin
AU - Tianqi Lin
AU - Shushang Chen
PY - 2026
DA - 2026/10/28
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1877632
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1877632/full
AB - Background: Accurate risk stratification following curative resection of non-metastatic clear cell renal cell carcinoma (nm-ccRCC) remains clinically challenging. This study aims to develop and validate Bayesian network (BN) models to predict survival in patients with nm-ccRCC. Methods: A dual-center retrospective cohort was randomly stratified into a training set (N = 326) and an internal validation set (N = 140). External validation was performed using an independent external cohort (N = 170). BN models were constructed based on clinicopathologic characteristics that demonstrated statistical significance in univariate analyses of the training cohort and were assessed using receiver operating characteristic (ROC) curves, confusion matrices, calibration and decision curve analysis. Results: The Bayesian analyses identified T stages as the principal determinant of 5-year overall survival (OS), while ISUP grades emerged as the dominant predictor for 3-year disease-free survival (DFS). The 5-year OS and 3-year DFS models achieved areas under the ROC curve (AUCs) of 0.878 and 0.867 in the internal validation cohort and 0.838 and 0.846 in the external validation cohort. Calibration slopes were close to unity with small intercepts across cohorts, and decision curve analysis showed net benefit exceeding that of treating all or no patients across a wide range of thresholds. Conclusions: This study establishes BNs for the first time as a clinically actionable tool for postoperative risk prediction in nm-ccRCC, with promising discriminative and calibrated performance supporting their potential use in patient counseling, risk-stratified surveillance, and the selection of candidates for adjuvant therapy or clinical trials. Future prospective, multicenter studies are warranted to extend these predictions to a 5-year DFS endpoint and to refine the decision threshold for clinical translation.
SN - 2234-943X
ER -