Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival

Abstract

Background: To evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in lung cancer (LC) patients; to evaluate whether CT reconstruction algorithms may influence the model performance. Methods: patients operated on for LC 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. Results: 270 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. Conclusions: 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; CTs reconstructed with Iterative Reconstructions (IR) algorithm showed the best model performance.

A Rare Case of Adrenal Arterio-Venous Malformation (AVM): A Case Report

“…Furthermore, the rate of the arterialized flow within the venous system must also be identified. However ultrasound has some limitations regarding the definition of the extent of the lesion, which can be overcome by using CT which can provide useful informations on the involvement of adjacent organs (Botta et al, 2020). Furthermore, CT with contrast enhancer is able to give an accurate mapping and measurement of arteries, veins and nidus, as well as guaranteeing an assessment of flow patterns for the use of interventional radiology or for surgical planning (Lee et al, 2015).…”

The Challenge of Choosing the Best Classification Method in Radiomic Analyses: Recommendations and Applications to Lung Cancer CT Images

“…After reproducibility and stability analysis, a subset of 168 radiomic features was selected to be included in the main analyses. More details about image acquisition, radiomic features extraction and selection can be found in [ 29 ].…”

“…Starting from the first radiomic study by Aerts et al [ 2 ], numerous further studies were conducted to evaluate radiomic features of Computed Tomography (CT) images in a lung cancer setting, therefore the combination of this imaging technique and cancer type is one of the most investigated in the literature, with the most data collected so far. In our institute, we previously obtained data on around 300 patients with Non-Small-Cell Lung Cancer (NSCLC) to investigate the association between radiomic features of CT images and lung cancer outcomes (lymph node involvement and survival) [ 29 ].…”

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

“…In the relatively recent radiomics approach, quantitative analysis of radiological images (mainly CT [37][38][39], magnetic resonance imaging (MRI) [40][41][42], and positron emission tomography (PET) [43] images, but also ultrasounds [44], mammograms [45], and radiography) by extraction of a large number of image features (up to a few hundred or thousands) can be combined with ML classifiers to produce prognostic and predictive models [39].…”