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

Background: This study aimed to develop and internally validate computed tomography (CT)-based radiomic models to predict the lesion-level short-term response to tyrosine kinase inhibitors (TKIs) in patients with advanced renal cell carcinoma (RCC). Methods: This retrospective study included consecutive patients with RCC that were treated using TKIs as the first-line treatment. Radiomic features were extracted from noncontrast (NC) and arterial-phase (AP) CT images. The model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results: A total of 36 patients with 131 measurable lesions were enrolled (training: validation = 91: 40). The model with five delta features achieved the best discrimination capability with AUC values of 0.940 (95% CI, 0.890-0.990) in the training cohort and 0.916 (95% CI, 0.828-1.000) in the validation cohort. Only the delta model was well calibrated. The DCA showed that the net benefit of the delta model was greater than that of the other radiomic models, as well as that of the treat-all and treat-none criteria. Conclusions: Models based on CT delta radiomic features may help predict the short-term response to TKIs in patients with advanced RCC and aid in lesion stratification for potential treatments.

Automated summary

This study aimed to develop and validate radiomic models based on computed tomography (CT) to predict the short-term response to tyrosine kinase inhibitors (TKIs) in patients with advanced renal cell carcinoma (RCC). The models showed promising discrimination capability and were well calibrated, suggesting their potential usefulness in guiding treatment decisions.