CT texture analysis for predicting pseudoprogression in metastatic clear cell renal cell carcinoma during PD-1 inhibitor therapy

Abstract

Objective: To evaluate the effectiveness of enhanced CT texture feature analysis in predicting pseudoprogression in patients with metastatic clear cell renal cell carcinoma (mccRCC) undergoing programmed cell death protein 1 (PD-1) inhibitor therapy. Methods: A cross-sectional study. Data from 32 patients with mccRCC were retrospectively collected who received monotherapy with PD-1 inhibitors after standard treatment failure at Henan Cancer Hospital, from June 2015 to January 2021. Clinical information and enhanced CT images were analyzed to assess target lesion response. The lesions were divided into pseudoprogression and non-pseudoprogression groups. Manual segmentation of target lesions was performed using ITK-Snap software on baseline enhanced CT, and texture analysis was conducted using A.K. software to extract feature parameters. Differences in texture features between the pseudoprogression and non-pseudoprogression groups were analyzed using univariate and multivariate logistic regression. A predictive model for pseudoprogression was constructed, and its performance was evaluated using ROC curve analysis. Results: A total of 32 patients with 89 lesions were included in the study. Statistical analysis revealed significant differences in seven texture features between the pseudoprogression and non-pseudoprogression groups. These features included"original_ngtdm_Strength"(0.49 vs. -0.61,P=0.006), "wavelet-HLH_glszm_ZonePercentage"(0.67 vs. -0.22,P=0.024),"wavelet-LHL_ngtdm_Strength"(1.20 vs. -0.51,P=0.002), "wavelet-HLL_gldm_LargeDependenceEmphasis"(-0.84 vs. 0.19,P=0.002), "wavelet-HLH_glcm_Id" (-0.30 vs. 0.43,P=0.037),"wavelet- HLH_glrlm_RunPercentage"(0.45 vs. -0.01,P=0.032),"wavelet-LHH_firstorder_Skewness"(0.25 vs. -0.27, P=0.011). Based on these features, a pseudoprogression prediction model was developed with a P-value of 0.000 2 and an odds ratio of 0.045 (95%CI 0.009-0.227). The model exhibited a high predictive performance with an AUC of 0.907 (95%CI 0.817-0.997) according to ROC curve analysis. Conclusions: Enhanced CT texture feature analysis shows promise in predicting lesion pseudoprogression in patients with metastatic ccRCC undergoing PD-1 inhibitor therapy. The developed predictive model based on texture features demonstrates good performance and may assist in evaluating treatment response in these patients.