Dempster-Shafer theory based feature selection with sparse constraint for outcome prediction in cancer therapy

Chunfeng Lian (1) , Hua Li (2) , Thierry Denoeux (1) , Pierre Vera (3, 4) , Su Ruan (4)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 Washington University School of Medicine [Saint Louis, MO]
3 Service de médecine nucléaire [Rouen]
4 QuantIF-LITIS - Equipe Quantification en Imagerie Fonctionnelle

As a pivotal task in cancer therapy, outcome prediction is the foundation for tailoring and adapting a treatment planning. In this paper, we propose to use image features extracted from PET and clinical characteristics. Considering that both information sources are imprecise or noisy, a novel prediction model based on Dempster-Shafer theory is developed. Firstly, a specific loss function with sparse regularization is designed for learning an adaptive dissimilarity metric between feature vectors of labeled patients. Through minimizing this loss function, a linear low-dimensional transformation of the input features is then achieved; meanwhile, thanks to the sparse penalty, the influence of imprecise input features can also be reduced via feature selection. Finally, the learnt dissimilarity metric is used with the Evidential K-Nearest-Neighbor (EK-NN) classifier to predict the outcome. We evaluated the proposed method on two clinical data sets concerning to lung and esophageal tumors, showing good performance.

Mots clés: Outcome Prediction; PET; Feature Selection; Sparse Constraint; Dempster-Shafer Theory

Domaines: Cancer

Chunfeng Lian, Hua Li, Thierry Denoeux, Pierre Vera, Su Ruan. Dempster-Shafer theory based feature selection with sparse constraint for outcome prediction in cancer therapy. In MICCAI - International Workshop on Machine Learning in Medical Imaging, 2015, Munich, Germany. ⟨10.1007/978-3-319-24574-4_83⟩. ⟨hal-01659585⟩