Radiomics of non small cell lung cancer: association between radiomics features, lymph nodal status and prognosis.

Abstract

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images able to offer information about prognosis of cancer patients. The radiomics process relies on a multi-step path that ends in the construction of a predictive model, tailored on specific outcomes. The main steps of the radiomics process are: image acquisition and reconstruction, segmentation, features extraction, model building. Each of these steps shows its own challenges to make the final model robust and reliable. \nPatients with Non-small cell lung cancer (NSCLC) have baseline computed tomography (CT) and/or fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) imaging for diagnosis and staging. \nThe aim of this study was to evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in NSCLC patients. Patients with a pathological stage up to T3N1 were retrospectively selected and divided into training and validation sets. For the prediction of positive LNs and OS, the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was used; univariable and multivariable logistic regression analysis assessed the association of clinical-radiomic variables and endpoints. All tests were repeated after dividing the groups according to the CT reconstruction algorithm. p-values < 0.05 were considered significant. \n270 patients were included and divided into training (n = 180) and validation sets (n = 90). Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, also after dividing the two groups according to reconstruction algorithms. In conclusion, a combined clinical–radiomics model was not superior to a single clinical or single radiomics model to predict positive LNs. A radiomics model was able to separate high-risk and low-risk patients for OS