OBJECTIVE: The aim of this study was to develop a nomogram for radiation-induced hypothyroidism (RHT) prediction. METHODS: We collected data from 164 patients with nasopharyngeal carcinoma (NPC) in our previous prospective study. Biochemical hypothyroidism was defined as a serum thyroid-stimulating hormone level greater than the normal value. We collected both clinical and dose-volume factors. A univariate Cox regression analysis was performed to identify RHT risk factors. Optimal predictors were selected according to the least absolute shrinkage and selection operator (LASSO). We then selected the Cox regression models that best balanced the prediction performance and practicability to build a nomogram for RHT prediction. RESULTS: There were 38 (23.2%) patients who developed RHT, and the median follow-up was 24 months. The univariate Cox regression analysis indicated that gender, minimum dose, mean dose (Dmean) and V25-V60 [Vx (%), the percentage of thyroid volume receiving >x Gy] of the thyroid were significantly associated with RHT. The variables of gender, receiving chemotherapy or not (chemo), Dmean and V50 were selected using the LASSO analysis. A nomogram based on a three-variable (gender, chemo and V50) Cox regression model was constructed, and its concordance index was 0.72. Good accordance between prediction and observation was showed by calibration curves in the probability of RHT at 18, 24 and 30 months. CONCLUSION: This study built a nomogram for RHT in NPC survivors by analyzing both clinical and dose-volume parameters using LASSO. Thus, the individual dose constraint could be achieved in a visual format. Advances in knowledge: This study used LASSO to more accurately address the multicollinear problem between variables. The resulting nomogram will help physicians predict RHT.
OBJECTIVE: The aim of this study was to develop a nomogram for radiation-induced hypothyroidism (RHT) prediction. METHODS: We collected data from 164 patients with nasopharyngeal carcinoma (NPC) in our previous prospective study. Biochemical hypothyroidism was defined as a serum thyroid-stimulating hormone level greater than the normal value. We collected both clinical and dose-volume factors. A univariate Cox regression analysis was performed to identify RHT risk factors. Optimal predictors were selected according to the least absolute shrinkage and selection operator (LASSO). We then selected the Cox regression models that best balanced the prediction performance and practicability to build a nomogram for RHT prediction. RESULTS: There were 38 (23.2%) patients who developed RHT, and the median follow-up was 24 months. The univariate Cox regression analysis indicated that gender, minimum dose, mean dose (Dmean) and V25-V60 [Vx (%), the percentage of thyroid volume receiving >x Gy] of the thyroid were significantly associated with RHT. The variables of gender, receiving chemotherapy or not (chemo), Dmean and V50 were selected using the LASSO analysis. A nomogram based on a three-variable (gender, chemo and V50) Cox regression model was constructed, and its concordance index was 0.72. Good accordance between prediction and observation was showed by calibration curves in the probability of RHT at 18, 24 and 30 months. CONCLUSION: This study built a nomogram for RHT in NPC survivors by analyzing both clinical and dose-volume parameters using LASSO. Thus, the individual dose constraint could be achieved in a visual format. Advances in knowledge: This study used LASSO to more accurately address the multicollinear problem between variables. The resulting nomogram will help physicians predict RHT.
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