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10 March 2020 Integrating deep transfer learning and radiomics features in glioblastoma multiforme patient survival prediction
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Abstract
Glioblastoma multiforme (GBM) is the largest and most genetically and phenotypically heterogeneous category of primary brain tumors. Numerous novel chemical, targeted molecular and immune-active therapies in trial produce promising responses in a small disparate subset of patients but which patient will respond to which therapy remains unpredictable. Reliable imaging biomarkers for prediction and early detection of treatment response and survival are critical needs in neuro-oncology. In this study, brain tumor MRI 'deep features' extracted via transfer learning techniques were combined with features derived from an explicitly designed radiomics model to search for MRI markers predictive of overall survival (OS) in GBM patients. Two pre-trained convolutional neural network (CNN) models were utilized as the deep learning models and the elastic net-Cox model was performed to distinguish GBM patients into two survival groups. Two patient cohorts were included in this study. One was 50 GBM patients from our hospital and the other was 128 GBM patients from the Cancer Genome Atlas (TCGA) and the Cancer Image Archive (TCIA). The combined feature framework was predictive of OS in both data set with log-rank test p-value < 0.05 and may merit further study for reproducible prediction of treatment response.
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Wei Han, Lei Qin, Camden Bay, Xin Chen, Kun-Hsing Yu, Angie Li, Xiaoyin Xu, and Geoffrey S. Young "Integrating deep transfer learning and radiomics features in glioblastoma multiforme patient survival prediction", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132S (10 March 2020); https://doi.org/10.1117/12.2549325
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