The Impact of the Variation of CT Scanner on the Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) using Radiomic Models

Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner model) and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on the computed tomography (CT)-derived radiomic features to predict association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired with two different scanner manufacturers. We demonstrate scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments revealed the effect of scanner manufacturers on the robustness of the radiomic features, and the extent of this dependency is reflected on the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reduce the impact of imaging parameters on radiomic features and consequently on the machine learning models.