title: Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction.
abstract: As a vital task in cancer therapy, accurately predicting the treatment outcome is valuable for tailoring and adapting a treatment planning. To this end, multi-sources of information (radiomics, clinical characteristics, genomic expressions, etc) gathered before and during treatment are potentially profitable. In this paper, we propose such a prediction system primarily using radiomic features (e.g., texture features) extracted from FDG-PET images. The proposed system includes a feature selection method based on Dempster-Shafer theory, a powerful tool to deal with uncertain and imprecise information. It aims to improve the prediction accuracy, and reduce the imprecision and overlaps between different classes (treatment outcomes) in a selected feature subspace. Considering that training samples are often small-sized and imbalanced in our applications, a data balancing procedure and specified prior knowledge are taken into account to improve the reliability of the selected feature subsets. Finally, the Evidential K-NN (EK-NN) classifier is used with selected features to output prediction results. Our prediction system has been evaluated by synthetic and clinical datasets, consistently showing good performance.
authors.affiliation: Sorbonne Universités, Université de Technologie de Compiègne, CNRS, UMR 7253 Heudiasyc, France; Université de Rouen, QuantIF, EA 4108 LITIS, France.
authors.forename: Chunfeng
authors.initials: C
authors.lastname: Lian
authors.affiliation: Université de Rouen, QuantIF, EA 4108 LITIS, France. Electronic address: su.ruan@univ-rouen.fr.
authors.forename: Su
authors.initials: S
authors.lastname: Ruan
authors.affiliation: Sorbonne Universités, Université de Technologie de Compiègne, CNRS, UMR 7253 Heudiasyc, France.
authors.forename: Thierry
authors.initials: T
authors.lastname: Denœux
authors.affiliation: Université de Rouen, QuantIF, EA 4108 LITIS, France; Centre Henri-Becquerel, Department of Hematology, France.
authors.forename: Fabrice
authors.initials: F
authors.lastname: Jardin
authors.affiliation: Université de Rouen, QuantIF, EA 4108 LITIS, France; Centre Henri-Becquerel, Department of Nuclear Medicine, France.
authors.forename: Pierre
authors.initials: P
authors.lastname: Vera
mesh_terms: D000465:Algorithms; D019788:Fluorodeoxyglucose F18; D006801:Humans; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000628:therapy*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D016896:Treatment Outcome
chemical_list: D019788:Fluorodeoxyglucose F18
keywords: Cancer; Dempster–Shafer theory; Feature selection; Imbalanced learning; Outcome prediction; PET images