我们的目的是开发和外部验证基于 CT 的深度学习放射组学模型，用于预测透明细胞肾细胞癌 (ccRCC) 患者的总生存期 (OS)，并研究放射组学与肿瘤异质性和微环境的关联。临床病理数据和对比-收集了来自三个机构的 512 名 ccRCC 患者的增强 CT 图像。从 3D 感兴趣区域中总共提取了 3566 个深度学习放射组学特征。我们生成了深度学习放射组学评分 (DLRS)，并使用 TCIA 的外部队列验证了该评分。根据 DLRS 将患者分为高分组和低分组。相应TCGA队列的测序数据用于揭示不同放射组学评分组之间肿瘤异质性和微环境的差异。更重要的是，使用单变量和多变量Cox回归来识别术后OS不良的独立危险因素。通过结合 DLRS 和临床病理学特征开发了一个组合模型。 SHapley Additive exPlanation 方法用于解释预测结果。在多变量 Cox 回归分析中，DLRS 被确定为 OS 不良的独立危险因素。研究了不同放射组学评分组的基因组景观。两组之间肿瘤细胞和肿瘤微环境的异质性存在显着差异。在测试队列中，组合模型具有出色的预测性能，1年、3年和5年OS的AUC（95%CI）分别为0.879（0.868-0.931）、0.854（0.819-0.899）和0.831（0.813-0.868） ）， 分别。通过组合模型分层的不同组之间的生存时间存在显着差异。该模型显示出良好的区分度和校准能力，优于现有的预后模型（所有 p 值 < 0.05）。组合模型可以通过结合 DLRS 和显着的临床病理特征来预测 ccRCC 患者的预后。放射组学特征可以反映肿瘤异质性和微环境。© 2024。作者。

We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment.The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results.At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05).The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.© 2024. The Author(s).