Development and Validation of a Nomogram for Predicting Radiation Dermatitis in Cervical Cancer Radiotherapy

Corresponding author: CHANG Xin, 1732651699@qq.com

DOI: 10.12201/bmr.202604.00056

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  Abstract: Objective: To investigate the incidence of radiation dermatitis (RD) in patients with cervical cancer during radiotherapy, and to construct and validate a nomogram prediction model for RD in this population. Methods: A total of 110 patients with cervical cancer who underwent radiotherapy at the Affiliated Tumor Hospital of Guangxi Medical University from September to December 2025 were prospectively enrolled and divided into RD and non-RD groups. Univariate analysis was used to identify potential influencing factors. Variable selection was performed using LASSO regression combined with binary logistic regression. The final prediction model was determined by comparing the discrimination and calibration of models with different variable combinations while considering model parsimony. Internal validation was conducted using the Bootstrap method, and the models discrimination, calibration, and clinical utility were evaluated. Results: A total of 110 patients with cervical cancer were included, and the incidence of RD was 82.8% (91/110). The prognostic nutritional index (PNI) was an independent influencing factor for RD in patients undergoing radiotherapy for cervical cancer, with an optimal cutoff value of 46.525. Following staged variable selection, age, surgery, and PNI were ultimately included in the nomogram prediction model. Model validation results showed an AUC of 0.840 (95% CI: 0.744–0.936); the Hosmer–Lemeshow test yielded χ2 = 6.965 (P = 0.540); the calibration curve closely aligned with the ideal curve, with a Brier score of 0.1066; and the model demonstrated good net benefit within the 10%–40% predicted risk range. Conclusion: The nomogram prediction model for RD in patients undergoing radiotherapy for cervical cancer, constructed based on age, surgery, and PNI, exhibits good predictive performance and clinical utility, facilitating early identification of high-risk individuals and timely intervention in clinical practice.

  Key words: Cervical cancer; Radiation dermatitis; Influencing factors; Prognostic nutritional index; Risk prediction model; Nomogram

  Submit time: 8 April 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2026-03-29	10.12201/bmr.202604.00056V1	Download

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