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.
Highlights
The lung represents the second most frequent site of cancer each year, and the first cause of death from cancer [1]
The best local control and decreased risk of residual lesions guaranteed by the dissection of a high number of lymph node (LN) is associated with greater trauma for patients, such as prolonged air leaks, excessive chest tube drainage and prolonged hospitalization [2,3,4]
The purpose of this study was to evaluate whether a model based on quantitative CT radiomic and clinical features of lung cancer patients may be associated with LN status and with overall survival (OS); a secondary purpose was to assess the influence of CT reconstruction algorithms on the quantitative parameters and on the performance of the predictive models
Summary
The lung represents the second most frequent site of cancer each year, and the first cause of death from cancer [1]. A method that could non-invasively predict the presence of LN metastasis would be helpful to guide systematic dissection and might be considered, for example, in patients with small tumours and no apparent enlarged LNs [5], or in the presence of many co-morbidities. 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. Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, after dividing the two groups according to reconstruction algorithms
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