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. 2024;20(31):2385-2395.
doi: 10.1080/14796694.2024.2384353. Epub 2024 Aug 6.

Risk factor and prediction model development for severe radiation-induced oral mucositis in head and neck tumors

Jiajia Wang  1 , Liqiong Gu  2 , Caixia Zhi  1 , Shujuan Yang  1
Affiliations

Risk factor and prediction model development for severe radiation-induced oral mucositis in head and neck tumors

Jiajia Wang et al. Future Oncol. 2024.

Abstract

Aim: This article aims to identify risk factors for severe radiation-induced oral mucositis (RIOM) in head and neck cancer (HNC) patients. In addition, we intend to establish a predictive model in patients undergoing intensity-modulated radiotherapy.Patients & methods: In this retrospective study, several HNC patients (n = 179) treated at Zhejiang Provincial People's Hospital from January 2019 to June 2023 were considered. The recruited subjects were divided into modeling and validation groups. The experimental data on clinical characteristics and treatment were collected and analyzed to identify predictive factors for severe RIOM based on the logistic regression approach.Results: The results indicated that severe RIOM occurred in 55.3% of patients. Accordingly, significant predictors included smoking history, diabetes, concurrent chemotherapy, cumulative radiation dose and weight loss of ≥5% in relative to admission weight. A nomogram based on these factors was validated, showing excellent predictive accuracy.Conclusion: In summary, the predictive model could effectively identify high-risk patients for severe RIOM, enabling the design of targeted interventions and improving patient management during radiotherapy.

Keywords: head and neck tumors; nomogram; oral mucosa; radiation therapy.

Plain language summary

[Box: see text].

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Conflict of interest statement

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Figures

Figure 1.
Figure 1.
The nomogram predicts severe radiation oral mucositis in HNC patients.
Figure 2.
Figure 2.
The ROC curve presents the nomogram prediction model in the internal validation cohort. ROC: Receiver operating characteristic.
Figure 3.
Figure 3.
The graph shows the calibration curve of the nomogram model.
Figure 4.
Figure 4.
The ROC curve presents the nomogram prediction model in the external validation cohort. ROC: Receiver operating characteristic.
See this image and copyright information in PMC

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