The purpose of this study was to establish a methodology and technology for the development of an MRI-based radiomic signature for prognosis of overall survival (OS) in nasopharyngeal cancer from non-endemic areas. The signature was trained using 1072 features extracted from the main tumor in T1-weighted and T2-weighted images of 142 patients. A model with 2 radiomic features was obtained (RAD model). Tumor volume and a signature obtained by training the model on permuted survival data (RADperm model) were used as a reference. A 10-fold cross-validation was used to validate the signature. Harrel's C-index was used as performance metric. A statistical comparison of the RAD, RADperm and volume was performed using Wilcoxon signed rank tests. The C-index for the RAD model was higher compared to the one of the RADperm model (0.69±0.08 vs 0.47±0.05), which ensures absence of overfitting. Also, the signature obtained with the RAD model had an improved C-index compared to tumor volume alone (0.69±0.08 vs 0.65±0.06), suggesting that the radiomic signature provides additional prognostic information.
This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE