Francesca Botta1, Sara Raimondi2, Lisa Rinaldi1, Federica Bellerba2, Federica Corso2, Vincenzo Bagnardi3, Daniela Origgi1, Rocco Minelli4, Giovanna Pitoni4, Francesco Petrella5,6, Lorenzo Spaggiari5,6, Alessio G Morganti7, Filippo Del Grande8, Massimo Bellomi6,9, Stefania Rizzo7,8. 1. Medical Physics, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy. 2. Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy. 3. Department of Statistics and Quantitative Methods, University of Milano-Bicocca, 20126 Milan, Italy. 4. Post-graduate School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy. 5. Department of Thoracic Surgery, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy. 6. Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20122 Milan, Italy. 7. Department of Experimental, Diagnostic and Specialty Medicine-DIMES, University of Bologna, 40126 Bologna, Italy. 8. Clinica di Radiologia EOC, Istituto Imaging della Svizzera Italiana (IIMSI), Via Tesserete 46, 6900 Lugano, Switzerland. 9. Department of Radiology, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy.
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.
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.
Authors: Xiushan Zheng; Bo He; Yunhai Hu; Min Ren; Zhiyuan Chen; Zhiguang Zhang; Jun Ma; Lanwei Ouyang; Hongmei Chu; Huan Gao; Wenjing He; Tianhu Liu; Gang Li Journal: Front Public Health Date: 2022-07-18
Authors: Damiano Caruso; Michela Polici; Marta Zerunian; Antonella Del Gaudio; Emanuela Parri; Maria Agostina Giallorenzi; Domenico De Santis; Giulia Tarantino; Mariarita Tarallo; Filippo Maria Dentice di Accadia; Elsa Iannicelli; Giovanni Maria Garbarino; Giulia Canali; Paolo Mercantini; Enrico Fiori; Andrea Laghi Journal: Cancers (Basel) Date: 2022-07-15 Impact factor: 6.575