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

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.目的： 评估经程序性死亡受体1（PD-1）抑制剂治疗的转移性肾透明细胞癌（mccRCC）患者增强CT纹理特征分析对病灶假性进展的预测效能。 方法： 横断面研究。回顾性收集自2015年6月至2021年1月在河南省肿瘤医院标准治疗失败后进行单药PD-1抑制剂治疗的32例mccRCC患者的资料。男21例、女11例，年龄34~82岁。分析患者临床信息及治疗过程中增强CT图像，进行靶病灶应答评估，分为假性进展组与非假性进展组。采用ITK-Snap软件在基线增强CT原始DICOM数据上手动分割靶病灶、基于Pyradiomics v2.2标准采用A.K.软件提供特征参数，进行纹理分析。比较病灶假性进展组与非假性进展组的纹理特征差异，采用单因素、多因素logistic回归分析发现作为独立预测因子的纹理特征，并构建假性进展预测模型，采用受试者工作特征（ROC）曲线评价其预测效能。 结果： 32例患者89个病灶纳入本研究。经组间统计学分析，病灶假性进展组和非假性进展组之间在7个纹理特征上差异有统计学意义，这些纹理特征包括：“original_ngtdm_Strength”（0.49比-0.61，P=0.006）、“wavelet-HLH_glszm_ZonePercentage”（0.67比-0.22，P=0.024）、“wavelet-LHL_ngtdm_Strength”（1.20比-0.51，P=0.002）、“wavelet-HLL_gldm_LargeDependenceEmphasis”（-0.84比0.19，P=0.002）、“wavelet-HLH_glcm_Id”（-0.30比0.43，P=0.037），“wavelet-HLH_glrlm_RunPercentage”（0.45比-0.01，P=0.032），“wavelet-LHH_firstorder_Skewness”（0.25比-0.27，P=0.011）。将以上7个纹理特征作为独立预测因子构建预测假性进展预测模型，总体预测模型的P值为0.000 2，OR值为0.045（95%CI 0.009~0.227），ROC曲线分析示逻辑回归模型预测假性进展的AUC为0.907（95%CI 0.817~0.997）。 结论： 基线增强CT纹理特征分析有助于预测经PD-1抑制剂治疗的mccRCC患者假性进展病灶的发生，基于纹理特征建立的预测模型具有较好的假性进展预测效能。.