Jay Kumar Raghavan Nair1, Martin Vallières2, Marco A Mascarella3, Nagi El Sabbagh4, Carl Frédéric Duchatellier5, Anthony Zeitouni4, George Shenouda5, Jeffrey Chankowsky5. 1. Department of Radiology, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada; Department of Radiology, McGill University Health Centre, Montreal, Quebec, Canada. Electronic address: jay_drishti@yahoo.com. 2. Medical Physics Unit, McGill University, Montreal, Quebec, Canada. 3. Department of Otolaryngology, McGill University Health Centre, Montreal, Quebec, Canada; Epidemiology, Biostatistics and Occupational Health, McGill University Health Centre, Montreal, Quebec, Canada. 4. Department of Otolaryngology, McGill University Health Centre, Montreal, Quebec, Canada. 5. Department of Radiology, McGill University Health Centre, Montreal, Quebec, Canada.
Abstract
BACKGROUND: The personalization of oncologic treatment using radiomic signatures is mounting in nasopharyngeal carcinoma (NPC). We ascertain the predictive ability of 3D volumetric magnetic resonance imaging (MRI) texture features on NPC disease recurrence. METHODS: A retrospective study of 58 patients with NPC undergoing primary curative-intent treatment was performed. Forty-two image texture features were extracted from pre-treatment MRI in addition to clinical factors. A multivariate logistic regression was used to model the texture features. A receiver operating characteristic curve on 100 bootstrap samples was used to maximize generalizability to out-of-sample data. A Cox proportional model was used to predict disease recurrence in the final model. RESULTS: A total of 58 patients were included in the study. MRI texture features predicted disease recurrence with an area under the curve (AUC), sensitivity, and specificity of 0.79, 0.73, and 0.71, respectively. Loco-regional recurrence was predicted with AUC, sensitivity, and specificity of 0.82, 0.73 and 0.74 respectively while prediction for distant metastasis had an AUC, sensitivity, and specificity of 0.92, 0.79 and 0.84, respectively. Texture features on MRI had a hazard ratio of 4.37 (95% confidence interval 1.72-20.2) for disease recurrence when adjusting for age, sex, smoking, and TNM staging. CONCLUSION: Texture features on MRI are independent predictors of NPC recurrence in patients undergoing curative-intent treatment.
BACKGROUND: The personalization of oncologic treatment using radiomic signatures is mounting in nasopharyngeal carcinoma (NPC). We ascertain the predictive ability of 3D volumetric magnetic resonance imaging (MRI) texture features on NPC disease recurrence. METHODS: A retrospective study of 58 patients with NPC undergoing primary curative-intent treatment was performed. Forty-two image texture features were extracted from pre-treatment MRI in addition to clinical factors. A multivariate logistic regression was used to model the texture features. A receiver operating characteristic curve on 100 bootstrap samples was used to maximize generalizability to out-of-sample data. A Cox proportional model was used to predict disease recurrence in the final model. RESULTS: A total of 58 patients were included in the study. MRI texture features predicted disease recurrence with an area under the curve (AUC), sensitivity, and specificity of 0.79, 0.73, and 0.71, respectively. Loco-regional recurrence was predicted with AUC, sensitivity, and specificity of 0.82, 0.73 and 0.74 respectively while prediction for distant metastasis had an AUC, sensitivity, and specificity of 0.92, 0.79 and 0.84, respectively. Texture features on MRI had a hazard ratio of 4.37 (95% confidence interval 1.72-20.2) for disease recurrence when adjusting for age, sex, smoking, and TNM staging. CONCLUSION: Texture features on MRI are independent predictors of NPC recurrence in patients undergoing curative-intent treatment.