Abstract
In this paper, we explored predictive performance of region-specific radiomic models for overall survival classification task in BraTS 2019 dataset. We independently trained three radiomic models: single-region model which included radiomic features from whole tumor (WT) region only, 3-subregions model which included radiomic features from non-enhancing tumor (NET), enhancing tumor (ET), and edema (ED) subregions, and 6-subregions model which included features from the left and right cerebral cortex, the left and right cerebral white matter, and the left and right lateral ventricle subregions. A 3-subregions radiomics model relied on a physiology-based subdivision of WT for each subject. A 6-subregions radiomics model relied on an anatomy-based segmentation of tumor-affected regions for each subject which is obtained by a diffeomorphic registration with the Harvard-Oxford subcortical atlas. For each radiomics model, a subset of most predictive features was selected by ElasticNetCV and used to train a Random Forest classifier. Our results showed that a 6-subregions radiomics model outperformed the 3-subregions and WT radiomic models on the BraTS 2019 training and validation datasets. A 6-subregions radiomics model achieved a classification accuracy of 47.1% on the training dataset and a classification accuracy of 55.2% on the validation dataset. Among the single subregion models, Edema radiomics model and Left Lateral Ventricle radiomics model yielded the highest classification accuracy on the training and validation datasets.
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Acknowledgement
This work was in part supported by a grant from the Higher Education Commission of Pakistan that has funded the National Center in Big Data and Cloud Computing and the Clinical and Translational Imaging Lab at LUMS. The authors wish to thank Syed Talha Bukhari in providing the multi-class segmentation maps for the BraTS 2019 validation dataset.
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Shaheen, A., Burigat, S., Bagci, U., Mohy-ud-Din, H. (2020). Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features. In: Kia, S.M., et al. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. MLCN RNO-AI 2020 2020. Lecture Notes in Computer Science(), vol 12449. Springer, Cham. https://doi.org/10.1007/978-3-030-66843-3_25
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DOI: https://doi.org/10.1007/978-3-030-66843-3_25
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