Robust feature selection to predict tumor treatment outcome

Hongmei Mi (1) , Caroline Petitjean (2) , Bernard Dubray (2) , Pierre Vera (3) , Su Ruan (2)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 QuantIF-LITIS - Equipe Quantification en Imagerie Fonctionnelle
3 Service de médecine nucléaire [Rouen]

Recurrence of cancer after treatment increases the risk of death. The ability to predict the treatment outcome can help to design the treatment planning and can thus be beneficial to the patient. We aim to select predictive features from clinical and PET (positron emission tomography) based features, in order to provide doctors with informative factors so as to anticipate the outcome of the patient treatment.

Mots clés: Hierarchical forward feature selection; Positron emission tomography; Prediction; Prior knowledge; Small sample; Support vector machine

Domaines: Cancer

Hongmei Mi, Caroline Petitjean, Bernard Dubray, Pierre Vera, Su Ruan. Robust feature selection to predict tumor treatment outcome. Artificial Intelligence in Medicine, 2015, 64 (3), pp.195 - 204. ⟨10.1016/j.artmed.2015.07.002⟩. ⟨hal-01648103⟩