External validation of a CT-based radiomics signature in oropharyngeal cancer: Assessing sources of variation

Background and purpose: Radiomics is a high-throughput approach that allows for quantitative analysis of imaging data for prognostic applications. Medical images are used in oropharyngeal cancer (OPC) diag-nosis and treatment planning and these images may contain prognostic information allowing for treat-ment personalization. However, the lack of validated models has been a barrier to the translation of radiomic research to the clinic. We hypothesize that a previously developed radiomics model for risk stratification in OPC can be validated in a local dataset.Materials and methods: The radiomics signature predicting overall survival incorporates features derived from the primary gross tumor volume of OPC patients treated with radiation +/-chemotherapy at a single institution (n = 343). Model fit, calibration, discrimination, and utility were evaluated. The signature was compared with a clinical model using overall stage and a model incorporating both radiomics and clinical data. A model detecting dental artifacts on computed tomography images was also validated.Results: The radiomics signature had a Concordance index (C-index) of 0.66 comparable to the clinical model's C-index of 0.65. The combined model significantly outperformed (C-index of 0.69, p = 0.024) the clinical model, suggesting that radiomics provides added value. The dental artifact model demon-strated strong ability in detecting dental artifacts with an area under the curve of 0.87.Conclusion: This work demonstrates model performance comparable to previous validation work and provides a framework for future independent and multi-center validation efforts. With sufficient valida-tion, radiomic models have the potential to improve traditional systems of risk stratification, treatment personalization and patient outcomes.(c) 2022 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 178 (2023) 109434

Automated summary

This study validated a radiomics model for risk stratification in oropharyngeal cancer, which showed comparable performance to the clinical model and outperformed when combined with clinical data. Additionally, a model for detecting dental artifacts on computed tomography images was also validated.