目的：评估增强CT纹理特征分析在转移性透明细胞肾细胞癌（mccRCC）患者接受程序性细胞死亡蛋白1（PD-1）抑制剂治疗期间预测假性进展的有效性。方法：一项横断面研究。回顾性收集河南肿瘤医院2015年6月至2021年1月期间接受标准治疗失败后单用PD-1抑制剂治疗的32例mccRCC患者的临床信息和增强CT影像进行分析，以评估靶病灶反应。病灶被分为假性进展组和非假性进展组。基线增强CT上使用ITK-Snap软件进行靶病灶手工分割，并使用A.K.软件进行纹理分析提取特征参数。使用单变量和多变量逻辑回归分析假性进展组与非假性进展组之间的纹理特征差异。构建假性进展预测模型，并使用ROC曲线分析评估其性能。结果：共纳入32名患者的89个病灶进行了研究。统计分析显示，假性进展组与非假性进展组之间的七个纹理特征存在显著差异。这些特征包括"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)。基于这些特征，构建了一个假性进展预测模型，其P值为0.0002，Odds比为0.045（95%置信区间0.009-0.227）。该模型展示了较高的预测性能，根据ROC曲线分析的AUC为0.907（95%置信区间0.817-0.997）。结论：增强CT纹理特征分析显示了在接受PD-1抑制剂治疗的转移性ccRCC患者中预测病灶假性进展方面的潜力。基于纹理特征开发的预测模型展示了良好的性能，并可能有助于评估这些患者的治疗反应。

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.