Radiation oncology (London, England) · 2025
Development and validation of a machine learning-based model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhong Fangyan, Zhou Wenchao, Gao Xiying, Li Dangchi, Zeng Jianming, Xiong Xiaopeng, Lu Tianzhu, Gong Xiaochang, Xiao Yun, Li Jingao
Аннотация
BACKGROUND AND PURPOSE: This study aims to develop a robust and user-friendly prediction model for radiation-induced hypothyroidism (RIHT) in nasopharyngeal carcinoma (NPC) patients.
MATERIALS AND METHODS: NPC patients treated with IMRT between Jan. 2019 and Dec. 2021 were randomly assigned to a training cohort (n = 328) and a validation cohort (n = 141) at a ratio of 7:3. A total of 33 clinical and dose-volume variables were collected. Significant variables (p < 0.05) were identified through univariate Cox analysis and further refined using a 101-combination machine learning (ML) framework to develop a robust predictive model. The model was subsequently simplified through multivariate Cox analysis and a nomogram. Finally, the performance of the model was evaluated using the C-index, calibration plots, and decision curve analysis.
RESULTS: Using a 101-combination ML framework, we developed a predictive model for RIHT in NPC. The Coxboost + RSF method with 11 predictors achieved the best performance (C-index: 0.91 [training], 0.71 [validation]). A simplified five-variable model (pre-treatment TSH, TSH-to-thyroid-volume ratio, age, V45, V20) was created via multi-cox regression, with a C-index of 0.80 [training] and 0.71 [validation]. High-risk patients had significantly higher three-year RIHT incidences (72.3% vs. 18.6%, p < 0.0001) in the training cohort, and 67.9% versus 24.4% (p < 0.0001) in the validation cohort. The model showed strong calibration and confirmed clinical utility through decision curve analysis, supporting its use in personalized treatment planning.
CONCLUSION: We developed a ML framework to identify key predictive factors for RIHT, which was simplified into a five-variable model for clinical use, offering a robust tool for predicting RIHT risk in decision-making.
MATERIALS AND METHODS: NPC patients treated with IMRT between Jan. 2019 and Dec. 2021 were randomly assigned to a training cohort (n = 328) and a validation cohort (n = 141) at a ratio of 7:3. A total of 33 clinical and dose-volume variables were collected. Significant variables (p < 0.05) were identified through univariate Cox analysis and further refined using a 101-combination machine learning (ML) framework to develop a robust predictive model. The model was subsequently simplified through multivariate Cox analysis and a nomogram. Finally, the performance of the model was evaluated using the C-index, calibration plots, and decision curve analysis.
RESULTS: Using a 101-combination ML framework, we developed a predictive model for RIHT in NPC. The Coxboost + RSF method with 11 predictors achieved the best performance (C-index: 0.91 [training], 0.71 [validation]). A simplified five-variable model (pre-treatment TSH, TSH-to-thyroid-volume ratio, age, V45, V20) was created via multi-cox regression, with a C-index of 0.80 [training] and 0.71 [validation]. High-risk patients had significantly higher three-year RIHT incidences (72.3% vs. 18.6%, p < 0.0001) in the training cohort, and 67.9% versus 24.4% (p < 0.0001) in the validation cohort. The model showed strong calibration and confirmed clinical utility through decision curve analysis, supporting its use in personalized treatment planning.
CONCLUSION: We developed a ML framework to identify key predictive factors for RIHT, which was simplified into a five-variable model for clinical use, offering a robust tool for predicting RIHT risk in decision-making.