- June 27, 2016
- Category: Nuclear Medicine, Scientific Publications
Desbordes P1,2, Ruan S1, Modzelewski R1,3, Vauclin S2, Vera P1,3, Gardin I1,3
1 LITIS – QUANTIF – EA4108, Université de Rouen, France
2 DOSISOFT, Cachan, France
3 CENTRE HENRI BECQUEREL, Rouen, France
Presented at RFIA 2016
In oncology, many features can be extracted from PET images to describe tumours. We propose a new feature selection strategy based on random forests (RF) to detect the best predictive subsets of features. Our method is performed in 3 steps. First, a Spearman correlation analysis is carried out to eliminate the correlated features. Then, the achievement of importance rankings of the remaining features according to the RF is done. Finally, the best subsets of features are defined through a genetic algorithm (GA). Our method is evaluated by RF classification based on data from 65 patients with an oesophageal cancer. A comparison with other features selection strategies is performed. Our method gives more accurate results than those evaluated.