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 (...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-39809-9 |
| _version_ | 1797560135049019392 |
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| author | Michail E. Klontzas Emmanouil Koltsakis Georgios Kalarakis Kiril Trpkov Thomas Papathomas Na Sun Axel Walch Apostolos H. Karantanas Antonios Tzortzakakis |
| author_facet | Michail E. Klontzas Emmanouil Koltsakis Georgios Kalarakis Kiril Trpkov Thomas Papathomas Na Sun Axel Walch Apostolos H. Karantanas Antonios Tzortzakakis |
| author_sort | Michail E. Klontzas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:56:09Z |
| format | Article |
| id | doaj.art-971a38f5bed94b6aac829869de7a05cf |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:56:09Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-971a38f5bed94b6aac829869de7a05cf2023-11-20T09:12:02ZengNature PortfolioScientific Reports2045-23222023-08-0113111110.1038/s41598-023-39809-9A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasiaMichail E. Klontzas0Emmanouil Koltsakis1Georgios Kalarakis2Kiril Trpkov3Thomas Papathomas4Na Sun5Axel Walch6Apostolos H. Karantanas7Antonios Tzortzakakis8Department of Medical Imaging, University Hospital of HeraklionDepartment of Diagnostic Radiology, Karolinska University HospitalDivision of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska InstitutetDepartment of Pathology and Laboratory Medicine, Alberta Precision Labs, Cumming School of Medicine, University of CalgaryInstitute of Metabolism and Systems Research, University of BirminghamResearch Unit Analytical Pathology, German Research Center for Environmental Health, Helmholtz Zentrum MünchenResearch Unit Analytical Pathology, German Research Center for Environmental Health, Helmholtz Zentrum MünchenDepartment of Medical Imaging, University Hospital of HeraklionDivision of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska InstitutetAbstract 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.https://doi.org/10.1038/s41598-023-39809-9 |
| spellingShingle | Michail E. Klontzas Emmanouil Koltsakis Georgios Kalarakis Kiril Trpkov Thomas Papathomas Na Sun Axel Walch Apostolos H. Karantanas Antonios Tzortzakakis A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia Scientific Reports |
| title | A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia |
| title_full | A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia |
| title_fullStr | A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia |
| title_full_unstemmed | A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia |
| title_short | A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia |
| title_sort | pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia |
| url | https://doi.org/10.1038/s41598-023-39809-9 |
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