Department of Medical Imaging, University Hospital of Heraklion, Crete, Heraklion, Greece.
Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Crete, Heraklion, Greece.
Sci Rep. 2023 Aug 3;13(1):12594. doi: 10.1038/s41598-023-39809-9.
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.
在影像学和组织病理学上区分良性肾嗜酸细胞瘤和恶性肾细胞癌（RCC）是一个关键问题，这也是每天临床面临的挑战。本文旨在展示一种新的方法，即整合代谢组学和放射组学特征（RF）来区分良性嗜酸细胞瘤和恶性肾肿瘤。为此，前瞻性收集了 33 个肾肿瘤（14 个肾嗜酸细胞瘤和 19 个 RCC）并进行了组织病理学特征分析。基质辅助激光解吸/电离质谱成像（MALDI-MSI）用于提取代谢组学数据，而 CT 扫描则用于提取 RF。统计集成用于生成多组学特征的多层次网络社区。用于区分两组的关键代谢物和 RF（中心度差异>0.1）用于通路富集分析和机器学习分类器（XGboost）开发。接收者操作特征（ROC）曲线和曲线下面积（AUC）用于评估分类器性能。放射代谢组学分析显示良性和恶性肾肿瘤之间的网络节点配置存在差异。有 14 个节点（6 个 RF 和 8 个代谢物）对于区分两组至关重要。联合放射代谢组学模型的 AUC 为 86.4%，而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）和差异富集的代谢途径。总之，放射代谢组学整合已被提出作为评估疾病实体的一种方法。在我们的案例研究中，该方法确定了在区分良性嗜酸细胞瘤和恶性肾肿瘤方面重要的 RF 和代谢物，突出了两组之间差异表达的途径。放射代谢组学鉴定的关键代谢物和 RF 可用于改善肾肿瘤的识别和区分。
