  The importance of radiomics features for predicting patient outcome is now
well-established. Early study of prognostic features can lead to a more
efficient treatment personalisation. For this reason new radiomics features
obtained through mathematical morphology-based operations are proposed. Their
study is conducted on an open database of patients suffering from Nonsmall
Cells Lung Carcinoma (NSCLC). The tumor features are extracted from the CT
images and analyzed via PCA and a Kaplan-Meier survival analysis in order to
select the most relevant ones. Among the 1,589 studied features, 32 are found
relevant to predict patient survival: 27 classical radiomics features and five
MM features (including both granularity and morphological covariance features).
These features will contribute towards the prognostic models, and eventually to
clinical decision making and the course of treatment for patients.
