Related Experiment Video
Updated: Jan 9, 2026
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
A preoperative CT-based radiological score for predicting recurrence in papillary renal cell carcinoma: a multicenter
Xiaoxia Li1, Chenchen Dai2, Jianyi Qu2
1Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
Objectives:
This study aims to establish a radiological model derived from preoperative computed tomography (CT) to predict the likelihood of papillary renal cell carcinoma (PRCC) recurrence after surgical intervention.
Materials And Methods:
A retrospective multicenter study initially enrolled 384 patients, with 266 eligible for analysis from four centers following partial nephrectomy or radical resection for PRCC. Twelve distinct categories of CT features were evaluated. To assess reproducibility, interobserver variability in radiological assessment was evaluated. A Cox proportional hazards model was employed to identify significant radiological predictors and construct a risk score system. The model's performance was evaluated through Harrell's Concordance Index (C-index), and its effectiveness was compared with that of several histopathologic prognostic systems.
Results:
A total of 266 patients were included, comprising a training dataset from one center (n = 152) and an external validation dataset from three other centers (n = 114). Inter-reader agreement was moderate to excellent for the radiological parameters (k = 0.43-0.94). Tumor margin regularity and regional lymph node size on CT scans were found to be independently associated with tumor recurrence (subdistribution hazard ratios ranging from 5.34 to 28.11; p-values ranging from < 0.001 to 0.028) and were incorporated into the predictive model. The model demonstrated superior predictive accuracy for tumor recurrence in the validation set compared to existing prognostic systems (C-index: 0.95 vs. 0.74-0.92; p-values ranging from < 0.001 to 0.08).
Conclusion:
A radiological score that combines tumor margin regularity and regional lymph node size predicts PRCC recurrence, demonstrating superior performance compared to existing prognostic systems.
Critical Relevance Statement:
This CT-based scoring system outperforms existing models in prognostic accuracy, aiding clinicians in personalized risk stratification and optimizing treatment decisions for patients.
Key Points:
The preoperative CT features are associated with the prognosis of papillary renal cell carcinoma (PRCC). Tumor irregularity and lymph node size on CT scans independently predict the postoperative recurrence of PRCC. A CT scoring system that incorporates these two features demonstrates superior prognostic accuracy compared to existing models.
