为了确定多期腹部CT的纹理特征分析能够可靠地预测肾肿瘤的良性和恶性、组织学亚型、病理分期、肾切除风险、病理分级和Ki67指数。共有1051名患者患有肾肿瘤，被分为内部验证组（来自四家不同医院的850名患者）和外部测试组（来自另一家当地医院的201名患者）。提出的框架由3D-UNet构建的3D肾脏和肿瘤分割模型、基于放射组学和图像降维的感兴趣区域特征提取器以及六个由XGBoost构建的分类器组成。使用一种称为SHAP的定量模型解释方法来探索每个特征的贡献。在内部验证集中，提出的多期腹部CT模型对良性和恶性、组织学亚型、病理分期、肾切除风险、病理分级和Ki67指数的预测具有稳健可靠性，AUROC值分别为0.88±0.1、0.90±0.1、0.91±0.1、0.89±0.1、0.84±0.1和0.88±0.1。外部测试集也展现了令人印象深刻的结果，相应的AUROC值分别为0.83±0.1、0.83±0.1、0.85±0.1、0.81±0.1、0.79±0.1和0.81±0.1。其中，一阶统计量、与肿瘤大小相关的形态学和与形状相关的肿瘤特征等放射组学特征对模型预测的贡献最大。腹部多期CT的自动纹理特征分析为多任务提供了可靠的预测，表明了临床应用的潜在价值。基于多期腹部CT的自动纹理特征分析框架可为多任务提供稳健可靠的预测。这些有价值的洞察力可以作为临床诊断和治疗的指导工具，使医学影像成为该过程中的重要组成部分。• 基于多期腹部CT的自动纹理特征分析框架能够更准确地预测肾肿瘤的良性和恶性、组织学亚型、病理分期、肾切除风险、病理分级和Ki67指数。• 进行了预测模型的定量解释，探索提取特征的贡献。• 该研究涉及来自5家医疗中心的1051名患者，结合异质的外部数据测试策略，可以无缝地应用于涉及新数据集的各种任务中。© 2023年。作者(们)，在欧洲放射学协会的独家许可下。

To determine whether the texture feature analysis of multi-phase abdominal CT can provide a robust prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor.A total of 1051 participants with renal tumor were split into the internal cohort (850 patients from four different hospitals) and the external testing cohort (201 patients from another local hospital). The proposed framework comprised a 3D-kidney and tumor segmentation model by 3D-UNet, a feature extractor for the regions of interest based on radiomics and image dimension reduction, and the six classifiers by XGBoost. A quantitative model interpretation method called SHAP was used to explore the contribution of each feature.The proposed multi-phase abdominal CT model provides robust prediction for benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in the internal validation set, with the AUROC values of 0.88 ± 0.1, 0.90 ± 0.1, 0.91 ± 0.1, 0.89 ± 0.1, 0.84 ± 0.1, and 0.88 ± 0.1, respectively. The external testing set also showed impressive results, with AUROC values of 0.83 ± 0.1, 0.83 ± 0.1, 0.85 ± 0.1, 0.81 ± 0.1, 0.79 ± 0.1, and 0.81 ± 0.1, respectively. The radiomics feature including the first-order statistics, the tumor size-related morphology, and the shape-related tumor features contributed most to the model predictions.Automatic texture feature analysis of abdominal multi-phase CT provides reliable predictions for multi-tasks, suggesting the potential usage of clinical application.The automatic texture feature analysis framework, based on multi-phase abdominal CT, provides robust and reliable predictions for multi-tasks. These valuable insights can serve as a guiding tool for clinical diagnosis and treatment, making medical imaging an essential component in the process.• The automatic texture feature analysis framework based on multi-phase abdominal CT can provide more accurate prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. • The quantitative decomposition of the prediction model was conducted to explore the contribution of the extracted feature. • The study involving 1051 patients from 5 medical centers, along with a heterogeneous external data testing strategy, can be seamlessly transferred to various tasks involving new datasets.© 2023. The Author(s), under exclusive licence to European Society of Radiology.