Abstract
This paper proposes to use deep radiomic features (DRFs) from a convolutional neural network (CNN) to model fine-grained texture signatures in the radiomic analysis of recurrent glioblastoma (rGBM). We use DRFs to predict survival of rGBM patients with preoperative T1-weighted post-contrast MR images (n = 100). DRFs are extracted from regions of interest labelled by a radiation oncologist and used to compare between short-term and long-term survival patient groups. Random forest (RF) classification is employed to predict survival outcome (i.e., short or long survival), as well as to identify highly group-informative descriptors. Classification using DRFs results in an area under the ROC curve (AUC) of 89.15% (p < 0.01) in predicting rGBM patient survival, compared to 78.07% (p < 0.01) when using standard radiomic features (SRF). These results indicate the potential of DRFs as a prognostic marker for patients with rGBM.
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Chaddad, A., Zhang, M., Desrosiers, C., Niazi, T. (2020). Deep Radiomic Features from MRI Scans Predict Survival Outcome of Recurrent Glioblastoma. In: Mohy-ud-Din, H., Rathore, S. (eds) Radiomics and Radiogenomics in Neuro-oncology. RNO-AI 2019. Lecture Notes in Computer Science(), vol 11991. Springer, Cham. https://doi.org/10.1007/978-3-030-40124-5_4
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DOI: https://doi.org/10.1007/978-3-030-40124-5_4
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