Khene Zine-Eddine, Bhanvadia Raj, Tachibana Isamu, Sharma Prajwal, Trevino Ivan, Graber William, Bertail Theophile, Fleury Raphael, Acosta Oscar, De Crevoisier Renaud, Bensalah Karim, Lotan Yair, Margulis Vitaly
Department of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA.
Department of Urology, University of Rennes, Rennes, France.
Jpn J Radiol. 2025 Feb 5. doi: 10.1007/s11604-025-01740-6.
To investigate the effect of CT enhancement phase on radiomics features for predicting post-surgical recurrence of clear cell renal cell carcinoma (ccRCC).
This retrospective study included 144 patients who underwent radical or partial nephrectomy for ccRCC. Preoperative multiphase abdominal CT scans (non-contrast, corticomedullary, and nephrographic phases) were obtained for each patient. Automated segmentation of renal masses was performed using the nnU-Net framework. Radiomics signatures (RS) were developed for each phase using ensembles of machine learning-based models (Random Survival Forests [RSF], Survival Support Vector Machines [S-SVM], and Extreme Gradient Boosting [XGBoost]) with and without feature selection. Feature selection was performed using Affinity Propagation Clustering. The primary endpoint was disease-free survival, assessed by concordance index (C-index).
The study included 144 patients. Radical and partial nephrectomies were performed in 81% and 19% of patients, respectively, with 81% of tumors classified as high grade. Disease recurrence occurred in 74 patients (51%). A total of 1,316 radiomics features were extracted per phase per patient. Without feature selection, C-index values for RSF, S-SVM, XGBoost, and Penalized Cox models ranged from 0.43 to 0.61 across phases. With Affinity Propagation feature selection, C-index values improved to 0.51-0.74, with the corticomedullary phase achieving the highest performance (C-index up to 0.74).
The results of our study indicate that radiomics analysis of corticomedullary phase contrast-enhanced CT images may provide valuable predictive insight into recurrence risk for non-metastatic ccRCC following surgical resection. However, the lack of external validation is a limitation, and further studies are needed to confirm these findings in independent cohorts.
探讨CT增强期对预测透明细胞肾细胞癌（ccRCC）术后复发的影像组学特征的影响。
这项回顾性研究纳入了144例行ccRCC根治性或部分肾切除术的患者。为每位患者获取术前腹部多期CT扫描（平扫、皮髓质期和肾盂期）。使用nnU-Net框架对肾肿块进行自动分割。使用基于机器学习的模型（随机生存森林[RSF]、生存支持向量机[S-SVM]和极端梯度提升[XGBoost]）的集成，在有和没有特征选择的情况下，为每个阶段开发影像组学特征（RS）。使用亲和传播聚类进行特征选择。主要终点是无病生存期，通过一致性指数（C指数）评估。
该研究纳入了144例患者。分别有81%和19%的患者接受了根治性和部分肾切除术，81%的肿瘤为高级别。74例患者（51%）出现疾病复发。每位患者每个阶段共提取1316个影像组学特征。在没有特征选择的情况下，RSF、S-SVM、XGBoost和惩罚Cox模型在各阶段的C指数值范围为0.43至0.61。通过亲和传播特征选择，C指数值提高到0.51 - 0.74，皮髓质期表现最佳（C指数高达0.74）。
我们的研究结果表明，皮髓质期对比增强CT图像的影像组学分析可能为手术切除后非转移性ccRCC的复发风险提供有价值的预测见解。然而，缺乏外部验证是一个局限性，需要进一步研究在独立队列中证实这些发现。
