A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia

Abstract

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.

Small Renal Masses: Developing a Robust Radiomic Signature

“…In particular, our radiomic signature can better identify both benign and malignant lesions succeeding in the aim of decreasing the overtreatment and of better delineating a malignancy risk stratification and subsequent approach for malignant SRMs. Moreover, these data can be implemented with clinical, deep learning, radiometabolomics, SPECT and transcriptomics data [ 29 , 30 , 31 , 32 , 33 ] to improve performances. Klontzas et al [ 32 ] showed that the radiomics-only performance for distinguishing benign from malignant renal masses was 70%, while the integration of radiomics and metabolomics increased the performance in differentiating malignant lesions (solid, cystic or mixed) to at least 86%.…”

“…Moreover, these data can be implemented with clinical, deep learning, radiometabolomics, SPECT and transcriptomics data [ 29 , 30 , 31 , 32 , 33 ] to improve performances. Klontzas et al [ 32 ] showed that the radiomics-only performance for distinguishing benign from malignant renal masses was 70%, while the integration of radiomics and metabolomics increased the performance in differentiating malignant lesions (solid, cystic or mixed) to at least 86%. Furthermore, Klontzas et al [ 30 ], by combining the 99m Tc Sestamibi uptake with radiomics in distinguishing benign oncocytic neoplasia, increased the diagnostic accuracy and improved positive and negative predictive value.…”

“…In particular, o radiomic signature can better identify both benign and malignant lesions succeeding the aim of decreasing the overtreatment and of better delineating a malignancy r stratification and subsequent approach for malignant SRMs. Moreover, these data can implemented with clinical, deep learning, radiometabolomics, SPECT a transcriptomics data [29][30][31][32][33] to improve performances. Klontzas et al [32] showed that t The tumor histotypes of the 17 patients belonging to the test set and the ones that are mislabelled are reported in Supplementary Materials (Table S1).…”

“…Moreover, these data can implemented with clinical, deep learning, radiometabolomics, SPECT a transcriptomics data [29][30][31][32][33] to improve performances. Klontzas et al [32] showed that t The tumor histotypes of the 17 patients belonging to the test set and the ones that are mislabelled are reported in Supplementary Materials (Table S1). The data in Table S1 represents a rough/tentative failure analysis of incorrectly classified cases, limited to the influence of the histotype of the success or failure of SRM classification.…”

Development and Validation of a Prediction Model for Differentiation of Benign and Malignant Fat-Poor Renal Tumors Using CT Radiomics

“…Jie Xu et al showed that combining radiomics features with clinical data, including demographic information and clinical history, can improve the prediction performance of machine learning algorithms compared to utilizing radiomics features alone [22]. Klontzas et al leveraged both radiomics and metabolomics features to develop a machine learning model, resulting in improved prediction performance [23]. We expect that the integration of data from multiple modalities into our machine learning model could further enhance its predictive capacity.…”