@inproceedings{9c746d3bcf7c4d11b27451df0d09662e,
title = "A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma",
abstract = "We proposed a pooling-based radiomics feature selection method and showed how it would be applied to the clinical question of predicting one-year survival in 130 patients treated for glioma by radiotherapy. The method combines filter, wrapper and embedded selection in a comprehensive process to identify useful features and build them into a potentially predictive signature. The results showed that non-invasive CT radiomics were able to moderately predict overall survival and predict WHO tumour grade. This study reveals an associative inter-relationship between WHO tumour grade, CT-based radiomics and survival, that could be clinically relevant.",
author = "Zhenwei Shi and Chong Zhang and Inge Compter and Maikel Verduin and Ann Hoeben and Danielle Eekers and Andre Dekker and Leonard Wee",
year = "2020",
month = feb,
day = "25",
doi = "10.1007/978-3-030-40124-5_8",
language = "English",
isbn = "978-3-030-40123-8",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Cham",
pages = "70--80",
editor = "H. Mohy-ud-Din and S. Rathore",
booktitle = "Radiomics and Radiogenomics in Neuro-oncology. RNO-AI 2019",
address = "Switzerland",
}