本研究旨在评估放射组学预测在患有明胶质性肾细胞癌（ccRCC）的患者中针对一系列分子、基因组和临床靶点的表现，并展示新颖的特征选择策略和亚段分割对模型可解释性的影响。使用TRACERx肾癌研究（NCT03226886）首批招募的101例患者的增强CT扫描，建立了放射组学分类模型，用于预测20个分子、组织病理学和临床靶点变量。采用手动3D分割结合自动亚段分割，从核心、边缘、高强化和低强化亚区域以及整个肿瘤中生成放射组学特征。比较了两种分类模型流程：传统流程反映常见的放射组学实践，以及提出的流程，包括两个新颖的特征选择步骤，旨在提高模型可解释性。对于两种流程，采用嵌套交叉验证估计预测性能和调整模型超参数，并采用排列测试评估估计性能度量的统计显著性。通过评估交叉验证折叠中的模型变异性，进一步进行模型稳健性评估。 使用任何流程，11个靶点的分类性能都是显著的（p < 0.05, H0:AUROC = 0.5），对于这些靶点，两种流程的AUROC都在±0.05范围内，除了一个靶点，提出的流程的性能增加了> 0.1。其中5个靶点（组织学坏死、肾静脉侵犯存在、总体组织学阶段、线性进化亚型和9p21.3体细胞变异标记丧失）的AUROC>0.8。使用提出的流程导出的模型比传统流程包含更少的特征组，从而实现更简单的模型解释而不损失性能。在预测肉瘤样分化和肿瘤分期的存在时，亚段分割导致性能或解释性的改善。 采用提出的流程，包括新颖的特征选择方法，能够实现更可解释性的模型，而不牺牲预测性能。 TRACERx肾脏研究（NCT03226886）。© 2023. 国际癌症影像学协会（ICIS）。

The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on model interpretability.Contrast-enhanced CT scans from the first 101 patients recruited to the TRACERx Renal Cancer study (NCT03226886) were used to derive radiomics classification models to predict 20 molecular, histopathology and clinical target variables. Manual 3D segmentation was used in conjunction with automatic sub-segmentation to generate radiomics features from the core, rim, high and low enhancing sub-regions, and the whole tumour. Comparisons were made between two classification model pipelines: a Conventional pipeline reflecting common radiomics practice, and a Proposed pipeline including two novel feature selection steps designed to improve model interpretability. For both pipelines nested cross-validation was used to estimate prediction performance and tune model hyper-parameters, and permutation testing was used to evaluate the statistical significance of the estimated performance measures. Further model robustness assessments were conducted by evaluating model variability across the cross-validation folds.Classification performance was significant (p < 0.05, H0:AUROC = 0.5) for 11 of 20 targets using either pipeline and for these targets the AUROCs were within ± 0.05 for the two pipelines, except for one target where the Proposed pipeline performance increased by > 0.1. Five of these targets (necrosis on histology, presence of renal vein invasion, overall histological stage, linear evolutionary subtype and loss of 9p21.3 somatic alteration marker) had AUROC > 0.8. Models derived using the Proposed pipeline contained fewer feature groups than the Conventional pipeline, leading to more straightforward model interpretations without loss of performance. Sub-segmentations lead to improved performance and/or improved interpretability when predicting the presence of sarcomatoid differentiation and tumour stage.Use of the Proposed pipeline, which includes the novel feature selection methods, leads to more interpretable models without compromising prediction performance.NCT03226886 (TRACERx Renal).© 2023. International Cancer Imaging Society (ICIS).