Development and Validation of a Nomogram for Predicting Radiation-Induced Temporal Lobe Injury in Nasopharyngeal Carcinoma

Overview

Journal Front Oncol

Specialty Oncology

Date 2020 Dec 28

PMID 33363025

Citations 7

Authors

Wenqiang Guan

Kang Xie

Yixin Fan

Stefan Lin

Rui Huang

Qianlong Tang

Ailin Chen

Yanqiong Song

Jinyi Lang

Peng Zhang

Affiliations

Soon will be listed here.

Abstract

Background: The purpose was to develop and validate a nomogram for prediction on radiation-induced temporal lobe injury (TLI) in patients with nasopharyngeal carcinoma (NPC).

Methods: The prediction model was developed based on a primary cohort that consisted of 194 patients. The data was gathered from January 2008 to December 2010. Clinical factors associated with TLI and dose-volume histograms for 388 evaluable temporal lobes were analyzed. Multivariable logistic regression analysis was used to develop the predicting model, which was conducted by R software. The performance of the nomogram was assessed with calibration and discrimination. An external validation cohort contained 197 patients from January 2011 to December 2013.

Results: Among the 391 patients, 77 patients had TLI. Prognostic factors contained in the nomogram were Dmax (the maximum point dose) of temporal lobe, D1cc (the maximum dose delivered to a volume of 1 ml), T stage, and neutrophil-to-lymphocyte ratios (NLRs). The Internal validation showed good discrimination, with a C-index of 0.847 [95%CI 0.800 to 0.893], and good calibration. Application of the nomogram in the external validation cohort still obtained good discrimination (C-index, 0.811 [95% CI, 0.751 to 0.870]) and acceptable calibration.

Conclusions: This study developed and validated a nomogram, which may be conveniently applied for the individualized prediction of TLI.

Citing Articles

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Hou J, He Y, Li H, Lu Q, Lin H, Zeng B Front Neurol. 2024; 15:1344324.

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Predictive accuracy of machine learning for radiation-induced temporal lobe injury in nasopharyngeal carcinoma patients: a systematic review and meta-analysis.

Li Y, Gong F, Guo Y, Ng W, Mejia M, Nei W Transl Cancer Res. 2023; 12(9):2361-2370.

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Deep learning-based precise prediction and early detection of radiation-induced temporal lobe injury for nasopharyngeal carcinoma.

OuYang P, Zhang B, Guo J, Liu J, Li J, Peng Q EClinicalMedicine. 2023; 58:101930.

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MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

Huang L, Yang Z, Zeng Z, Ren H, Jiang M, Hu Y Front Neurol. 2023; 14:1135978.

PMID: 37006478 PMC: 10060957. DOI: 10.3389/fneur.2023.1135978.

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