Clinical‑radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors

Negreros-Osuna,Adrián A.; Ramírez‑Mendoza,Diego A.; Casas-Murillo,Claudio; Guerra‑Cepeda,Abraham; Hernández‑Barajas,David; Elizondo-Riojas,Guillermo

doi:10.3892/ol.2022.13566

Clinical‑radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors

Authors:
- Adrián A. Negreros-Osuna
- Diego A. Ramírez‑Mendoza
- Claudio Casas-Murillo
- Abraham Guerra‑Cepeda
- David Hernández‑Barajas
- Guillermo Elizondo-Riojas
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Affiliations: Department of Radiology, School of Medicine and University Hospital ‘Dr. José Eleuterio González’, Autonomous University of Nuevo Leon, Monterrey, NL 64460, México, Department of Radiology, School of Medicine and University Hospital ‘Dr. José Eleuterio González’, Autonomous University of Nuevo Leon, Monterrey, NL 64460, México, Department of Oncology, School of Medicine and University Hospital ‘Dr. José Eleuterio González’, Autonomous University of Nuevo Leon, Monterrey, NL 64460, México
Published online on: October 26, 2022 https://doi.org/10.3892/ol.2022.13566
Article Number: 446
Copyright: © Negreros-Osuna et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Renal cancer has a global incidence and mortality of 2.2 and 1.8%, respectively. Up to 30% of these patients are intrinsically resistant to tyrosine kinase inhibitors (TKI). The National Comprehensive Cancer Network guidelines do not include any predictive factors regarding response to systemic therapy with TKI in recurrent and advanced diseases. The present study aimed to explore whether a model based on radiomics could predict treatment response in patients with advanced kidney cancer treated with TKIs. The current study included 62 patients with advanced kidney cancer (stages 3 and 4) that underwent a CT scan in the arterial phase from March 2016 to November 2020. Texture analysis was run on the largest cross‑sectional area of the primary tumor from each CT scan. A total of three different models were built from radiomics features and clinical data to analyze them by logistic regression and determine whether they correlated with the response to TKI. A receiver operating characteristic curve analysis was performed in each model to calculate the area under the curve (AUC) and the 95% confidence interval (CI). Significant radiomics features and clinical variables were identified and then a clinical model was created (AUC=0.90; sensitivity 75%; specificity 82.35%; CI 95%, 0.78‑1.00), a radiomic model (AUC=0.66; sensitivity 16.67%; specificity 89.47%, CI 95%, 0.45‑0.87) and a combined model (AUC=0.94; sensitivity 83.33%; specificity 94.12%; CI 95%, 0.84‑1.00). Overall, models based on clinical data and radiomics could anticipate response to systemic therapy with TKI in patients with advanced kidney cancer.

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