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Related Experiment Video

Updated: May 17, 2026

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

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

Published on: September 27, 2024

A Meta-Analysis-Based Risk Score for Predicting Radiation Pneumonitis After Thoracic Radiation Therapy for Lung

Jiming Yang¹, Ting Zhu¹, Jianliang Zhou¹

¹Radiotherapy and Chemotherapy Center, The First Affiliated Hospital of Ningbo University, Ningbo, 315000, China.

International Journal of Radiation Oncology, Biology, Physics

|May 15, 2026

Summary

A new risk score accurately predicts radiation pneumonitis (RP) in lung cancer patients after thoracic radiotherapy. This validated tool aids in personalized treatment and resource allocation.

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Last Updated: May 17, 2026

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

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

Published on: September 27, 2024

Area of Science:

Oncology
Radiation Oncology
Medical Informatics

Background:

Radiation pneumonitis (RP) is a significant complication following thoracic radiotherapy for lung cancer.
Accurate prediction of RP risk is crucial for personalized treatment strategies and mitigating adverse events.

Purpose of the Study:

To develop and validate a robust risk score for predicting radiation pneumonitis (RP) in lung cancer patients undergoing thoracic radiotherapy.

Main Methods:

A meta-analysis identified consistent risk factors, which were used to construct an initial scoring system.
An Asian calibration cohort (N=218) was used to refine the model and establish optimal risk-stratification cut-offs.
External validation was performed in two independent Asian cohorts (N=69, N=81) to assess the model's discrimination and clinical applicability.

Main Results:

The final risk score incorporated seven variables: smoking, chemotherapy, tumor location, pulmonary comorbidities, renin-angiotensin system inhibitor use, lung V5, and lung V20.
The score demonstrated strong predictive performance with an Area Under the Curve (AUC) of 0.831 in the calibration cohort and robust AUCs of 0.881 and 0.845 in external validation cohorts.
The validated risk score effectively stratified patients into high- and low-risk groups for grade ≥2 RP, with significant relative risks observed in the high-risk group.

Conclusions:

A meta-analysis-based risk score for predicting radiation pneumonitis has been developed and validated in Asian populations.
This scoring system offers reliable risk stratification, potentially enhancing personalized management and optimizing resource allocation for lung cancer patients receiving thoracic radiotherapy.
The availability of a web-based calculator facilitates the clinical implementation of this risk assessment tool.