A simple-to-use nomogram for predicting the risk of radiation pneumonitis in patients with thoracic segment esophageal squamous cell carcinoma.

Abstract

Background:

Radiation pneumonitis (RP) is one of the most severe complications of radiotherapy (RT) or concurrent chemoradiotherapy for thoracic segment esophageal squamous cell carcinoma (TSESCC) with delayed diagnose by conventional computed tomography (CT). The study aimed to develop a nomogram to predict the risk of RP early.

Methods:

Data was collected from 174 patients with clinicopathologically confirmed TSESCC from October 2013 to June 2020. Procalcitonin (PCT), C-reactive protein (CRP), and interleukin-6 (IL-6) levels in serum were dynamically monitored during radiotherapy. Lasso analysis was used for feature screening before multivariate logistic regression analysis to reduce the multicollinearity of variables. A nomogram combined with biological factors and clinical signs for individualized risk assessment and precise prediction of RP was developed and assed the performance with respect to its calibration, discrimination.

Results:

Of the 174 patients, 30 patients developed RP (grade ≥2) while 144 patients did not. After variable screening by Lasso analysis and logistics multivariate regression analysis, the predictor variables that were finally retained in the nomogram prediction model included IL-6, CRP, and radiotherapy techniques. The model displayed good discrimination with an area under the curve (AUC) of 0.898 (95% CI: 0.849-0.947), with the sensitivity and specificity of 0.967 and of 0.736, respectively. This model also shows good calibration and clinical practical value. In addition, the study provided a web-based version of the dynamic nomogram to facilitate clinical application.

Conclusions:

The study provides a nomogram model containing IL-6, CRP, RT techniques, which could be conveniently used for individualized prediction of RP in patients with TSESCC during radiotherapy or concurrent chemoradiotherapy.

• Authors: Ting Qiu, Yuxia Deng, Haochun Guo, Haijun Zhang
• Journal: Translational cancer research (Transl Cancer Res) Vol. 11 Issue 10 Pg. 3754-3766 (Oct 2022) ISSN: 2219-6803 [Electronic] China
• PMID: 36388040 (Publication Type: Journal Article)
• Copyright: 2022 Translational Cancer Research. All rights reserved.