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- Title
Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival.
- Authors
Botta, Francesca; Raimondi, Sara; Rinaldi, Lisa; Bellerba, Federica; Corso, Federica; Bagnardi, Vincenzo; Origgi, Daniela; Minelli, Rocco; Pitoni, Giovanna; Petrella, Francesco; Spaggiari, Lorenzo; Morganti, Alessio G.; Del Grande, Filippo; Bellomi, Massimo; Rizzo, Stefania
- 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.
- Subjects
LYMPHOMA risk factors; RISK of metastasis; LUNG cancer prognosis; ALGORITHMS; CANCER patients; COMPUTED tomography; MULTIVARIATE analysis; RISK assessment; STATISTICS; MULTIPLE regression analysis; RETROSPECTIVE studies; STATISTICAL models; DESCRIPTIVE statistics
- Publication
Cancers, 2020, Vol 12, Issue 6, p1432
- ISSN
2072-6694
- Publication type
Article
- DOI
10.3390/cancers12061432