Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024
A simple prediction model for the risk of boron neutron capture therapy-induced nausea and vomiting in head and neck cancer.
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Sato Mariko, Hirose Katsumi
Аннотация
BACKGROUND AND PURPOSE: Head and neck cancer patients undergoing boron neutron capture therapy (BNCT) often experience BNCT-induced nausea and vomiting (BINV). This study aimed to construct a BINV risk prediction model.
MATERIALS AND METHODS: In this retrospective study, 237 patients were randomly divided into a training and test cohort. In the training cohort, a univariate analysis was performed to identify factors associated with BINV. Multivariate analysis was used to identify factors and calculate coefficients for the model. The Hosmer-Lemeshow test was used to assess the goodness of fit, and receiver operating characteristic curves were plotted to evaluate the accuracy of the model. For both the training and test cohort, the predictive model was used to generate the scores and calculate the sensitivity and specificity.
RESULTS: The incidence of nausea and vomiting was 50% and 18%, respectively. Female sex, younger age, non-squamous cell carcinoma, no prior chemotherapy, and beam entry from the face/lateral region were associated with the occurrence of BINV. The prediction model showed a good fit (P = 0.96) and performance (area under the curve = 0.75). The sensitivity and specificity were 83% and45 % for the training cohort (n = 193) and 86% and 59% for the test cohort (n = 44), respectively.
CONCLUSION: We developed a simple model that predicts BINV. This will enable appropriate care to be implemented based on increased risk to prevent its occurrence.
MATERIALS AND METHODS: In this retrospective study, 237 patients were randomly divided into a training and test cohort. In the training cohort, a univariate analysis was performed to identify factors associated with BINV. Multivariate analysis was used to identify factors and calculate coefficients for the model. The Hosmer-Lemeshow test was used to assess the goodness of fit, and receiver operating characteristic curves were plotted to evaluate the accuracy of the model. For both the training and test cohort, the predictive model was used to generate the scores and calculate the sensitivity and specificity.
RESULTS: The incidence of nausea and vomiting was 50% and 18%, respectively. Female sex, younger age, non-squamous cell carcinoma, no prior chemotherapy, and beam entry from the face/lateral region were associated with the occurrence of BINV. The prediction model showed a good fit (P = 0.96) and performance (area under the curve = 0.75). The sensitivity and specificity were 83% and45 % for the training cohort (n = 193) and 86% and 59% for the test cohort (n = 44), respectively.
CONCLUSION: We developed a simple model that predicts BINV. This will enable appropriate care to be implemented based on increased risk to prevent its occurrence.