Prognostic Power of Texture Based Morphological Operations in a Radiomics Study for Lung Cancer
This research provides new prognostic features for lung cancer patients, potentially aiding in treatment personalization and clinical decision-making.
This study explored the prognostic power of new radiomics features derived from mathematical morphology-based operations in lung cancer patients. Out of 1,589 features analyzed, 32 were identified as relevant for predicting patient survival, including 27 classical radiomics features and 5 new morphological features.
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.