肾细胞肿瘤和肾细胞癌（RCC）的成像和组织病理学鉴别是一个临床上经常遇到的关键问题。本文旨在展示一种将代谢组学与放射学特征（RF）相结合的新方法，用于区分良性肾腺细胞瘤和恶性肾肿瘤。为此，我们前瞻性收集了33例肾肿瘤（14例肾腺细胞瘤和19例RCC），并进行了组织病理学鉴定。利用基质辅助激光解吸/电离质谱成像（MALDI-MSI）提取代谢组学数据，同时从同一肿瘤的CT扫描中提取RF。统计学集成用于生成多层次网络特征的全基因组社群。用于区分两组（Δ中心性>0.1）的代谢物和RF，用于通路富集分析和机器学习分类器（XGboost）开发。使用受试者工作特征曲线（ROC）和曲线下面积（AUC）来评估分类器的性能。放射代谢组学分析展示了良性和恶性肾肿瘤之间的网络节点配置差异。有14个节点（6个RF和8个代谢物）在区分两组之间起到关键作用。组合放射代谢组学模型的AUC为86.4％，而仅使用代谢组学和放射组学分类器的AUC分别为72.7％和68.2％。对显著代谢物节点的分析确定了三个不同的肿瘤簇（恶性、良性和混合）和差异富集的代谢通路。总之，我们提出了放射代谢组学集成作为评估疾病实体的方法。在我们的案例研究中，该方法确定了在区分良性肾腺细胞瘤和恶性肾肿瘤方面至关重要的RF和代谢物，突出显示了两组之间差异表达的通路。放射代谢组学确定的关键代谢物和RF可用于改善对肾肿瘤的鉴别和识别。© 2023. Springer Nature Limited.

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.© 2023. Springer Nature Limited.