Wu Chunying, Xi Yuzhen, Hu Juanjuan, Li Guodong, Wang Xu, Jiao Xiaofei, Ding Zhongxiang, Sun Weiying
Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China.
Department of Radiology, 903th RD Hospital of PLA, Hangzhou, China.
BMC Nephrol. 2025 Jul 1;26(1):296. doi: 10.1186/s12882-025-04268-z.
This study aims to evaluate the predictive value of CT radiomics combined with clinical-imaging features for the WHO/ISUP pathological grade of clear cell renal cell carcinoma(ccRCC).
In this multicenter retrospective study enrolled 169 patients (110 males, 59 females) with pathological confirmed ccRCC between November 2017 and February 2022. Based on the WHO/ISUP pathological grading criteria, patients were stratified into two groups: low-grade (grades I-II, n = 93) and high-grade (grades III-IV, n = 76). Three-dimensional tumor segmentation was performed on CT cortical-phase images using ITK-SNAP software. The segmented data were subsequently processed through the United Imaging Intelligent Scientific Research Platform for radiomic feature extraction and selection. Logistic regression analyses were conducted to identify independent predictive factors. Based on these factors, an optimized predictive model was developed through random forest classification and evaluated using calibration curves, ROC analysis, Delong test and decision curve analysis.
Significant differences were observed in tumor size, morphology, hemorrhage, necrosis, tumor thrombus, capsular invasion and tumor extending beyond the renal margin between the two groups. Logistic regression analysis identified tumor size, hemorrhage and tumor thrombus as independent predictors. Six radiomic features were selected to establish prediction model. In the training cohort, the combined model demonstrated superior discriminative performance, achieving an AUC of 0.895, compared to the radiomics model (AUC = 0.873) and the clinical-imaging model (AUC = 0.712). The combined model also exhibited strong predictive ability in both the validation cohort (AUC = 0.885) and the external cohort (AUC = 0.860). DeLong tests revealed statistically significant differences in AUC between the combined model and the clinical-imaging model (Z = 3.023, P = 0.002), as well as between the radiomics model and the clinical-imaging model (Z = 2.560, P = 0.010). However, no significant difference in AUC was found between the combined model and the radiomics model (Z = 1.627, P = 0.103). Decision curve analysis revealed the combined model yielded enhanced net benefit across threshold ranges (0.1-0.84).
The combined model based on CT radiomics combined with clinical- imaging features can effectively predict the WHO/ISUP pathological grade of ccRCC, providing more basis for the prognosis assessment of patients.
本研究旨在评估CT影像组学联合临床影像特征对透明细胞肾细胞癌（ccRCC）WHO/ISUP病理分级的预测价值。
在这项多中心回顾性研究中，纳入了2017年11月至2022年2月期间169例经病理确诊的ccRCC患者（男性110例，女性59例）。根据WHO/ISUP病理分级标准，将患者分为两组：低级别组（I-II级，n = 93）和高级别组（III-IV级，n = 76）。使用ITK-SNAP软件对CT皮质期图像进行三维肿瘤分割。随后，通过联影智能科研平台对分割数据进行处理，以提取和选择影像组学特征。进行逻辑回归分析以确定独立预测因素。基于这些因素，通过随机森林分类建立优化的预测模型，并使用校准曲线、ROC分析、德龙检验和决策曲线分析进行评估。
两组在肿瘤大小、形态、出血、坏死、肿瘤血栓、包膜侵犯和肿瘤超出肾边缘等方面存在显著差异。逻辑回归分析确定肿瘤大小、出血和肿瘤血栓为独立预测因素。选择六个影像组学特征建立预测模型。在训练队列中，联合模型表现出更好的判别性能，AUC为0.895，优于影像组学模型（AUC = 0.873）和临床影像模型（AUC = 0.712）。联合模型在验证队列（AUC = 0.885）和外部队列（AUC = 0.860）中也表现出较强的预测能力。德龙检验显示联合模型与临床影像模型之间的AUC存在统计学显著差异（Z = 3.023，P = 0.002），以及影像组学模型与临床影像模型之间的AUC存在统计学显著差异（Z = 2.560，P = 0.010）。然而，联合模型与影像组学模型之间的AUC未发现显著差异（Z = 1.627，P = 0.103）。决策曲线分析显示联合模型在阈值范围（0.1-0.84）内产生了更高的净效益。
基于CT影像组学联合临床影像特征的联合模型能够有效预测ccRCC的WHO/ISUP病理分级，为患者的预后评估提供更多依据。
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