Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients

Abstract

Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas.

Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

“…A number of studies focused on the use of this tool for prognostication and survival prediction. Recently, Bologna et al reported that MRI-based radiomics in patients with nasopharyngeal cancer can improve the prognostic capability when added to the clinical features [42]. Another study also concluded that there is the possibility to use extracted radiomic features of locally advanced non-small cell lung cancers to predict PFS [43].…”

Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics

“…Denoising was then performed for all images. To compensate for intensity non-uniformities due to variations in the magnetic field, an N4 bias field correction was performed ( 14 , 15 ). The hybrid white-stripe method was then used for signal intensity normalization ( 16 ).…”

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

“…In [ 93 , 94 ], MRI images were used to build datasets that included 327 and 136 patients with NPC, respectively. LASSO and recursive feature elimination were used to select features in [ 93 ].…”

“…The author constructed five models to predict progression-free survival using the univariate Cox proportional hazard model, and the best C-index in the validation set was 0.874. A total of 530 stable features were extracted, and 67 non-redundant features were selected in [ 94 ]. Four predictive models were constructed based on the Cox proportional hazard model, and the C-index of the best model was 0.72.…”