Liu Xiaohui, Han Xiaowei, Zhang Guozheng, Zhu Xisong, Zhang Wen, Wang Xu, Wu Chenghao
The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.
The Afliated Cancer Hospital of University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.
Abdom Radiol (NY). 2025 Mar 1. doi: 10.1007/s00261-025-04857-4.
Nuclear grading of clear cell renal cell carcinoma (ccRCC) plays a crucial role in diagnosing and managing the disease.
To develop and validate a CT-based Delta-Radiomics model for preoperative assessment of nuclear grading in renal clear cell carcinoma.
This retrospective analysis included surgical cases of 146 ccRCC patients from two medical centers from December 2018 to December 2023, with 117 patients from Hospital and 29 from the *Hospital Affiliated to University of **. Radiomic features were extracted from whole-abdomen CT images, and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used for feature selection. The Multi-Layer Perceptron (MLP) approach was employed to construct five predictive models (RAD_NE, RAD_AP, RAD_VP, RAD_Delta1, RAD_Delta2). The models were evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity, while clinical utility was assessed through Decision Curve Analysis (DCA).
A total of 1,834 radiomic features were extracted from the three phases of the CT images for each model. The models demonstrated strong classification performance, with AUC values ranging from 0.837 to 0.911 in the training set and 0.608 to 0.869 in the test set. The Rad_Delta1 and Rad_Delta2 models demonstrated advantages in predicting ccRCC pathological grading.The AUC value of the Rad_Delta1 is 0.911in the training set and 0.771 in the external verifcation set.The AUC value of the Rad_Delta2 is 0.881 in the training set and0.608 in the external verifcation set. DCA curves confirmed the clinical applicability of these models.
CT-based delta-radiomics shows potential in predicting the pathological grading of clear cell renal cell carcinoma (ccRCC).
透明细胞肾细胞癌（ccRCC）的核分级在该疾病的诊断和管理中起着至关重要的作用。
开发并验证一种基于CT的Delta-放射组学模型，用于术前评估肾透明细胞癌的核分级。
这项回顾性分析纳入了2018年12月至2023年12月期间来自两个医疗中心的146例ccRCC患者的手术病例，其中117例来自某医院，29例来自*大学附属医院。从全腹CT图像中提取放射组学特征，并使用最小绝对收缩和选择算子（LASSO）算法进行特征选择。采用多层感知器（MLP）方法构建五个预测模型（RAD_NE、RAD_AP、RAD_VP、RAD_Delta1、RAD_Delta2）。使用曲线下面积（AUC）、准确性、敏感性和特异性对模型进行评估，同时通过决策曲线分析（DCA）评估临床实用性。
每个模型从CT图像的三个阶段共提取了1834个放射组学特征。这些模型表现出强大的分类性能，训练集中的AUC值范围为0.837至0.911，测试集中为0.608至0.869。Rad_Delta1和Rad_Delta2模型在预测ccRCC病理分级方面表现出优势。Rad_Delta1在训练集中的AUC值为0.911，在外部验证集中为0.771。Rad_Delta2在训练集中的AUC值为0.881，在外部验证集中为0.608。DCA曲线证实了这些模型的临床适用性。
基于CT的Delta-放射组学在预测透明细胞肾细胞癌（ccRCC）的病理分级方面显示出潜力。
