NIMG-83. MULTIMODAL PET/MRI RADIOMICS AND CLINICAL PARAMETERS FOR OVERALL SURVIVAL PREDICTION IN PATIENTS WITH IDH WILDTYPE GLIOBLASTOMA

Abstract

Abstract BACKGROUND Currently, most radiomics studies on survival prediction in brain tumor patients are based on MRI only. The goal of our study was to evaluate multimodal radiomics derived from amino acid PET/MRI and clinical parameters for survival prediction in patients with newly diagnosed IDH wildtype glioblastoma. METHODS Sixty-three patients with newly diagnosed IDH wildtype glioblastoma were evaluated retrospectively. At initial diagnosis, all patients underwent structural MRI and O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET. Tumor volumes were automatically segmented using a deep learning-based tool followed by visual inspection. Predefined and deep radiomics features were extracted from both imaging modalities. Feature repeatability analyses and feature selection were performed to avoid overfitting. Cox regression models for overall survival were built from clinical parameters such as age or the extent of resection, radiomics features, and combinations thereof, and finally validated using 5-fold cross-validation. Further evaluation of the model in an external test dataset is ongoing. RESULTS The median overall survival was 12 months (range, 0-64 months). Higher age and larger FET PET tumor volumes were significantly correlated with shorter overall survival (age, r=-0.39, p< 0.001; volume, r=-0.31, p< 0.05). Models solely based on predefined FET PET or MRI radiomics features showed a similar mean concordance index (C-index) as the model based on clinical parameters (C-indices, 0.68±0.04; 0.64±0.03; and 0.69±0.08, respectively). Multimodal radiomics based on predefined and deep features yielded improved C-indices of 0.75±0.06 and 0.72±0.09, respectively. A model based on multimodal radiomics and clinical parameters achieved the best prognostic performance (C-index, 0.80±0.04). CONCLUSION Our results suggest an added clinical value of multimodal FET PET/MRI radiomics with clinical parameters for the non-invasive survival prediction in patients with IDH wildtype glioblastoma.