Prognostic Power of Texture Based Morphological Operations in a Radiomics Study for Lung Cancer

TL;DR

This study introduces new radiomics features based on mathematical morphology for lung cancer prognosis, demonstrating their relevance alongside classical features in survival prediction.

Contribution

Proposes novel morphology-based radiomics features and evaluates their prognostic value in NSCLC using PCA and survival analysis.

Findings

32 features are relevant for survival prediction

5 new morphological features identified as prognostic

Classical and morphological features together improve prognostic models

Abstract

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.