Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2022
A novel nomogram to predict overall survival in head and neck cancer survivors with radiation-induced brain necrosis.
СтатьяРандомизированное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Pan Dong, Rong Xiaoming, Zhu Yingying, Ng Wai Tong, Chen Dongping, Li Honghong, Xu Yongteng, Tang Yamei, Radiation Oncology Palliative Care Collaborative Group
Аннотация
BACKGROUND AND PURPOSE: The study aimed to develop and validate a novel nomogram to predict overall survival in head and neck cancer survivors following the diagnosis of radiation-induced brain necrosis (RN).
MATERIALS AND METHODS: We included head and neck cancer survivors with RN from a radiation complications registry study. A total of 495 eligible patients were 7:3 randomly allocated to a training cohort and an internal validation cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select significant predictors of post-RN survival in the training cohort, and a multivariable Cox model was used to develop the nomogram. The performance of the nomogram was assessed using the internal validation cohort and externally validated using additional 88 RN patients.
RESULTS: We identified five predictors of post-RN survival using the training data: age, tumor progression before RN, lower cranial nerves injury, bilateral necrosis, and history of stroke. The nomogram showed favorable performance in the internal validation cohort (C-index 0.761, 95% CI 0.676 to 0.847) and in the external validation cohort (C-index 0.795, 95% CI 0.717 to 0.874). The decision curve analysis indicated that the nomogram was clinically useful when the probabilities of death ranging from 1% to 48% at 1 year, from 3% to 50% at 3 years, and exceeding 2% at 5 years after being diagnosed with RN.
CONCLUSION: In this LASSO-Cox model-based nomogram study, we developed and validated an easily applied model to predict overall survival in head and neck cancer survivors following an RN diagnosis.
MATERIALS AND METHODS: We included head and neck cancer survivors with RN from a radiation complications registry study. A total of 495 eligible patients were 7:3 randomly allocated to a training cohort and an internal validation cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select significant predictors of post-RN survival in the training cohort, and a multivariable Cox model was used to develop the nomogram. The performance of the nomogram was assessed using the internal validation cohort and externally validated using additional 88 RN patients.
RESULTS: We identified five predictors of post-RN survival using the training data: age, tumor progression before RN, lower cranial nerves injury, bilateral necrosis, and history of stroke. The nomogram showed favorable performance in the internal validation cohort (C-index 0.761, 95% CI 0.676 to 0.847) and in the external validation cohort (C-index 0.795, 95% CI 0.717 to 0.874). The decision curve analysis indicated that the nomogram was clinically useful when the probabilities of death ranging from 1% to 48% at 1 year, from 3% to 50% at 3 years, and exceeding 2% at 5 years after being diagnosed with RN.
CONCLUSION: In this LASSO-Cox model-based nomogram study, we developed and validated an easily applied model to predict overall survival in head and neck cancer survivors following an RN diagnosis.