Comparison of feature selection in radiomics for the prediction of overall survival after radiotherapy for hepatocellular carcinoma

Fontaine, Pierre (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; Université de Rennes, France) ; Riet, François-Georges (Université de rennes, France) ; Castelli, Joël (Univeristé de Rennes, France) ; Gnep, Khemara (Université de Rennes, France) ; Depeursinge, Adrien (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; Lausanne University Hospital (CHUV), Lausanne, Switzerland) ; Crevoisier, Renaud de (Université de Rennes, France) ; Acosta, Oscar (Université de Rennes, France)

Hepatocellular carcinoma (HCC) is the sixth more frequent cancer worldwide. This type of cancer has a poor overall survival rate mainly due to underlying cirrhosis and risk of recurrence outside the treated lesion. Quantitative imaging within a radiomics workflow may help assessing the probability of survival and potentially may allow tailoring personalized treatments. In radiomics a large amount of features can be extracted, which may be correlated across a population and very often can be surrogates of the same physiopathology. This issues are more pronounced and difficult to tackle with imbalanced data. Feature selection strategies are therefore required to extract the most informative with the increased predictive capabilities. In this paper, we compared different unsupervised and supervised strategies for feature selection in presence of imbalanced data and optimize them within a machine learning framework. Multi-parametric Magnetic Resonance Images from 81 individuals (19 deceased) treated with stereotactic body radiation therapy (SBRT) for inoperable (HCC) were analyzed. Pre-selection of a reduced set of features based on Affinity Propagation clustering (non supervised) achieved a significant improvement in AUC compared to other approaches with and without feature pre-selection. By including the synthetic minority over-sampling technique (SMOTE) for imbalanced data and Random Forest classification this workflow emerges as an appealing feature selection strategy for survival prediction within radiomics studies.

Note: Due to the COVID-19 outbreak, the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) venue in Montréal was cancelled. The proceedings of the online conference are however published according to the original schedule.

Conference Type:
short paper
Economie et Services
Institut Informatique de gestion
Montréal, Canada, 20-24 July 2020
Montréal, Canada
20-24 July 2020
Published in:
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Note: The file is under embargo until: 2022-07-20

 Record created 2021-01-11, last modified 2021-05-14

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