Comparation of CT/PET/MRI in Deep Learning Models for Outcome Prediction in Oropharyngeal Cancer Patients

Background and Purpose: Due to superior soft tissue contrast, MRI may provide more prognostic information than CT/PET for outcome prediction. This study aims to compare the prognostic value of MRI with CT and PET in deep learning models for local control, regional control and overall survival in oropharyngeal cancer patients compared to a clinical benchmark model.Materials and Methods: A dataset comprising 266 oropharyngeal cancer patients was assembled. Each patient’s data includes pretreatment axial T1 and a coronal T2 MRI scan, CT and PET scans, gross tumor volume of the primary tumor, clinical parameters and information on local control, regional control and overall survival. Various 2D and 3D convolutional neural networks were trained using images of contoured gross tumor volume with and without a margin for outcome prediction.Results: The 2D models using T2 images within a bounding box region determined by the gross tumor volume achieved concordance index of 0.88 and 0.75 for local control and overall survival prediction, respectively. Additionally, MRI-based models achieved higher concordance indexes than CT- or PET-based models for local control prediction. In comparison to a clinical benchmark model, the T2-based 2D model showed improved local control prediction (concordance index: 0.88 vs. 0.80), and combining the T2 prediction with clinical model improved overall survival prediction (concordance index 0.81 vs. 0.78). Furthermore, the clinical + MRI models showed enhanced performance compared to a clinical routine survival risk stratification system.Conclusions: MRI-based deep learning models can improve prediction of local control and overall survival in oropharyngeal cancer.