Association of a CT-based clinical and radiomics score of non-small cell lung cancer (NSCLC) with lymph node status and overall survival

Francesca Botta, Sara Raimondi, Lisa Rinaldi, Federica Bellerba, Federica Corso, Vincenzo Bagnardi, Daniela Origgi, Rocco Minelli, Giovanna Pitoni, Francesco Petrella, Lorenzo Spaggiari, Alessio G. Morganti, Filippo del Grande, Massimo Bellomi, Stefania Rizzo

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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 clinicalradiomic 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.

Original languageEnglish (US)
Article number1432
Issue number6
StatePublished - Jun 2020
Externally publishedYes


  • Computed tomography
  • Lung cancer
  • Lymph nodes
  • Overall survival
  • Radiomics
  • Reconstruction algorithms

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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