Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Medical Image Analysis Année : 2016

Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction

Résumé

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.
Fichier principal
Vignette du fichier
mia2016final.pdf (756.6 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01324553 , version 1 (01-06-2016)

Identifiants

Citer

Chunfeng Lian, Su Ruan, Thierry Denoeux, Fabrice Jardin, Pierre Vera. Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Medical Image Analysis, 2016, 32, pp.257-268. ⟨10.1016/j.media.2016.05.007⟩. ⟨hal-01324553⟩
143 Consultations
459 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More