A novel nomogram to predict overall survival in head and neck cancer survivors with radiation-induced brain necrosis. (March 2022)

• Record Type: Journal Article
• Authors: Pan, Dong; Rong, Xiaoming; Zhu, Yingying; Ng, Wai Tong; Chen, Dongping; Li, Honghong; Xu, Yongteng; Tang, Yamei
• Is Part Of: Radiotherapy and oncology. Volume 168(2022)
• Page Start: 121
• Page End: 129
• Publication Date: 2022-03
• Subjects: Head and neck cancer -- Radiotherapy -- Radiation-induced brain necrosis -- Nomogram -- Survival; BUN Blood urea nitrogen -- CI Confidence interval -- HDL High-density lipoprotein -- HR Hazard ratio -- hsCRP High-sensitivity C reaction protein -- IMRT Intensity-modulated radiation therapy -- IQR Interquartile range -- LASSO Least Absolute Shrinkage and Selection Operator -- LDL Low-density lipoprotein -- MRI Magnetic resonance imaging -- NPC Nasopharyngeal carcinoma -- RN Radiation-induced brain necrosis -- RT Radiotherapy; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2022.01.033
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Survival of cancer survivors with RN had not been well studied. A novel tool to predict post-RN survival in head and neck cancer survivors. The nomogram showed favorable calibration, discrimination and clinical utility. The model has ability to identify RN patients at high-risk of death. Abstract: 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.