Prognostic Image Biomarkers for Nasopharyngeal Cancer Patients Treated With (Chemo)Radiation

Purpose/Objective(s): Image biomarkers of the contoured Gross Tumorvolume (GTV) in computed tomography (CT) images could be related to tumor biology and prognosis. This study aimed to develop and validate a model to predict overall survival (OS) of nasopharyngeal cancer (NPC)patients, based on tumor Image biomarkers in addition to clinical parameters.Such a model could be used as a pre-treatment tool to support clinical decision-making.Materials/Methods: This retrospective study was composed of 296 NPC patients treated with (chemo-) radiation therapy. In total, 134 Image biomarkers representing tumor image intensity, shape and texture and clinical parameters (TNM staging, WHO score, dose parameters, age and gender)were analyzed. Using forward selection Cox proportional hazards regression analysis was performed to create a survival model. The concordance index (c-index) was determined to assess the model’s discriminative power.We compared the Kaplan-Meier survival curves between patients with predicted values below and above the median with a log-rank test. Subsequently,the model was externally validated in 310 head and neck cancer(HN) patients (mainly or opharyngeal and laryngeal).Results: Firstly, a multi variable model was developed based on clinical features only (c-index 0.67). Four independent prognostic factors were identified: age, T stage, N stage and D98 (minimum dose in 98% of theGTV). Secondly, another multivariable model was developed based on Image biomarkers only (c-index 0.68). Two variables were identified as most important prognostic factors: “volume density,” describing the compactness of the tumor, and “Run Length Non-Uniformity,” a measurefor intratumor heterogeneity. Interestingly, when Image biomarkers and clinical features were combined in the model, the c-index of the model improved to 0.73, which was significantly better than that of models with Image biomarkers or clinical features alone (Table 1). The performance of the model was also good (c-index Z 0.70) when externally validated inHN patients. The Kaplan-Meier survival curves were significantly different between high and low predicted values both in the training group (the probability of OS after 3 years were 91% and 73%, P<0.01) and in the validation group (the probability of OS after 3 years were 75% and 48%,P<0.01). Conclusion: A predictive model for the survival of nasopharyngeal cancer patients was developed based on the CT image biomarkers ‘volume density’and ‘Run Length Non-Uniformity’ in addition to clinical parameters.The addition of the image biomarkers improved the model performance even in an external validation set consisting of patients with different tumor locations.