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Title: Predicting disease progression from the rate of bodyweight change in nasopharyngeal carcinoma patient during radiotherapy
Authors: Sun, J 
Lam, SKE 
Teng, X 
Zhang, J 
Lee, FKH
Yip, CWY
Chow, JCH
Lee, VHF
Sun, Y
Cai, J 
Issue Date: 2025
Source: Scientific reports, 2025, v. 15, 7490
Abstract: Bodyweight loss is a common occurrence in Nasopharyngeal Carcinoma (NPC) patients during Radiotherapy (RT). Previous studies found that the prognostic value of percentage weight loss (pWL) during RT is not credible. We aimed to develop a novel progression predictor surrogated to pWL by modelling all bodyweight records measured during the treatment interval. This retrospective study included two independent hospitals of 624 patients. The Predicted Progression Probability (PPP) was obtained from deep learning-guided differential equation solution, model by the patient’s age, sex, body height, and the weekly measured bodyweight records. The performance of PPP in predicting disease progression was assessed, its association with prognosis and adjuvant chemotherapy response was evaluated. The PPP was learnt from the training cohort (N = 257) with 7 weeks of bodyweight records. The prediction performance was validated with 367 patients of the testing cohort sub-divided according to the number of bodyweight records found. The area under of curve for patients with 7 weeks (N = 155), 6 weeks (N = 176), and 5 weeks bodyweight records (N = 32) were 0.76, 0.73, and 0.95 respectively. PPP was significantly associated with progression-free and remained an independent prognostic factor adjusting for clinicopathologic variables in multivariate analysis in all study cohort (adjusted hazard ratio [HR] range: 2.50–7.04, all p < 0.001). Patients with high-PPP derived progression benefit from adjuvant chemotherapy (HR: 0.41–0.54, all p < 0.03), whereas those with low-PPP did not for both cohorts. The trajectory of bodyweight change during RT is more robust than the pWL to give a progression prediction after RT. The PPP is a reliable predictor for estimating the risk of residual diseases after RT course, which also helps to predict adjuvant chemotherapy response in locally advanced NPC patients.
Keywords: Adjuvant chemotherapy
Concurrent chemoradiotherapy
Deep learning
Differential equations
Nasopharyngeal carcinoma
Weight loss
Publisher: Nature Publishing Group
Journal: Scientific reports 
EISSN: 2045-2322
DOI: 10.1038/s41598-025-88810-x
Rights: Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The following publication Sun, J., Lam, S.k.E., Teng, X. et al. Predicting disease progression from the rate of bodyweight change in nasopharyngeal carcinoma patient during radiotherapy. Sci Rep 15, 7490 (2025) is available at https://doi.org/10.1038/s41598-025-88810-x.
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