Abstract
We present our contribution to the HECKTOR 2021 challenge. We created a Survival Random Forest model based on clinical features, and a few radiomics features that have been extracted with and without using the given tumor masks, for Task 3 and Task 2 of the challenge, respectively. To decide on which radiomics features to include into the model, we proceeded both to automatic feature selection, using several established methods, and to literature review of radiomics approaches for similar tasks. Our best performing model includes one feature selected from the literature (Metabolic Tumor Volume derived from the FDG-PET image), one via stability selection (Inverse Variance of the Gray Level Co-occurrence Matrix of the CT image), and one selected via permutation-based feature importance (Tumor Sphericity). This hybrid approach turns-out to be more robust to overfitting than models based on automatic feature selection. We also show that simple ROI definition for the radiomics features, derived by thresholding the Standard Uptake Value in the FDG-PET images, outperforms the given expert tumor delineation in our case.
S. Starke, D. Thalmeier, P. Steinbach and M. Piraud—Contributed equally to this work.
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Starke, S., Thalmeier, D., Steinbach, P., Piraud, M. (2022). A Hybrid Radiomics Approach to Modeling Progression-Free Survival in Head and Neck Cancers. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021. Lecture Notes in Computer Science, vol 13209. Springer, Cham. https://doi.org/10.1007/978-3-030-98253-9_25
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