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

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Survival Prediction for Postoperative Patients With Kidney Cancer Based on Computed Tomography Radiomics:

Guizhen He^1,2, Liwen Lai^1,2, Guoan Yu^1,2

¹Department of Nephrology, The Third People's Hospital of Jingdezhen, 76 Taoyang Road, Xinchang, East Suburb, Zhushan District, Jingdezhen, 333000, China, 86 18779803522, 86 0798-8410812.

JMIR Medical Informatics

|October 21, 2025

Summary

This study developed a radiomics nomogram using CT scans to predict kidney cancer survival in postoperative patients. The model integrates radiomic features and clinical data, offering a noninvasive tool for personalized treatment decisions.

Keywords:

predictive survival computed tomography radiomics kidney cancer nomogram postoperative

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Area of Science:

Oncology
Medical Imaging
Radiology

Background:

Kidney cancer prognosis requires accurate assessment for effective postoperative management.
Radiomics shows potential in cancer prognosis, but comprehensive models integrating radiomic and clinical data for kidney cancer survival are limited.

Purpose of the Study:

To develop and validate a computed tomography (CT) radiomics-based nomogram for predicting overall survival in postoperative kidney cancer patients.
To integrate radiomic features with clinical parameters for enhanced predictive accuracy.

Main Methods:

Extracted radiomic features from CT images of 207 postoperative kidney cancer patients.
Utilized z-score standardization and GLMNet for feature processing.
Applied least absolute shrinkage and selection operator-Cox regression for feature selection and model development.
Validated the nomogram's predictive ability using 10-fold cross-validation, receiver operating characteristic curves, calibration curves, and Kaplan-Meier analysis.

Main Results:

Identified five key radiomic features for the predictive model.
The developed nomogram demonstrated good predictive performance, as indicated by receiver operating characteristic and calibration curves.
Kaplan-Meier analysis revealed significantly shorter overall survival in the high-risk group compared to the low-risk group.

Conclusions:

The CT-derived radiomics nomogram effectively integrates radiomic features and clinical variables for kidney cancer survival prediction.
The nomogram provides a noninvasive, quantitative tool for personalized postoperative management and clinical decision-making in kidney cancer patients.