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

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.

Predicting First-Line VEGFR-TKI Resistance and Survival in Metastatic Clear Cell Renal Cell Carcinoma Using a Clinical-Radiomic Nomogram

“…Negreros-Osuna AA et al proposed a clinical-radiomics model to predict response to TKI therapy in advanced kidney cancer in a 62-patient cohort. This combined model reached AUC of 0.94 with sensitivity of 83.33% and speci city of 94.12% [19].…”

Delta Radiomics Model Predicts Lesion-Level Responses to Tyrosine Kinase Inhibitors in Patients with Advanced Renal Cell Carcinoma: A Preliminary Result

“…Previous studies used the average feature values of all the lesions in each patient to predict patient-level response to TKIs [ 3 , 4 ]. A recent study used AP radiomic features of a CT image to predict treatment response to TKIs [ 9 ]. The model did not achieve the best performance (AUC = 0.66).…”