K-Means Clustering of Hyperpolarised 13 C-MRI Identifies Intratumoral Perfusion/Metabolism Mismatch in Renal Cell Carcinoma as the Best Predictor of the Highest Grade.
Simple Summary: This study describes a novel way to delineate intratumoral regions within clear cell renal cell carcinoma (ccRCC) based on the clustering of quantitative metabolic images from clinical hyperpolarised [1-13C]pyruvate MRI. We show that these clusters, combining metabolic and perfusion...
| Published in: | Cancers Vol. 17; no. 4; pp. 569 - 586 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
MDPI
Feb2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183336332&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183336332 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Feb2025 vid: 17 iid: 4 pid: 97109 pub: MDPI artinfo: ui: 183336332 183336332 183336332 10.3390/cancers17040569 183336332 ppf: 569 ppct: 17 formats: tig: atl: K-Means Clustering of Hyperpolarised 13 C-MRI Identifies Intratumoral Perfusion/Metabolism Mismatch in Renal Cell Carcinoma as the Best Predictor of the Highest Grade. aug: au: Horvat-Menih, Ines Khan, Alixander S. McLean, Mary A. Duarte, Joao Serrao, Eva Ursprung, Stephan Kaggie, Joshua D. Gill, Andrew B. Priest, Andrew N. Crispin-Ortuzar, Mireia Warren, Anne Y. Welsh, Sarah J. Mitchell, Thomas J. Stewart, Grant D. Gallagher, Ferdia A. affil: Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK sug: subj: Radioisotopes Diagnostic Use Carboxylic Acids Diagnostic Use Magnetic Resonance Imaging Methods Carcinoma, Renal Cell Classification Carcinoma, Renal Cell Physiopathology Carcinoma, Renal Cell Metabolism Human Contrast Media Diagnostic Use Machine Learning Algorithms Preoperative Care Perfusion Immunohistochemistry Staining and Labeling Sequence Analysis Methods Sensitivity and Specificity Predictive Value of Tests Biological Markers Analysis Gene Expression Biopsy Funding Source ab: Simple Summary: This study describes a novel way to delineate intratumoral regions within clear cell renal cell carcinoma (ccRCC) based on the clustering of quantitative metabolic images from clinical hyperpolarised [1-13C]pyruvate MRI. We show that these clusters, combining metabolic and perfusion metrics, predict the most aggressive tumour region with the highest specificity and outperform clusters derived from standard clinical perfusion imaging. The cluster representing perfusion/metabolism mismatch via imaging showed the highest metabolic dysregulation, with markers of aggressiveness identified through molecular analyses from collected tissue samples. This approach has the potential to guide biopsies to the most aggressive tumour regions, to reduce sampling error and undergrading, as well as to improve risk stratification and clinical management. Background: Early and accurate grading of renal cell carcinoma (RCC) improves patient risk stratification and has implications for clinical management and mortality. However, current diagnostic approaches using imaging and renal mass biopsy have limited specificity and may lead to undergrading. Methods: This study explored the use of hyperpolarised [1-13C]pyruvate MRI (HP 13C-MRI) to identify the most aggressive areas within the tumour of patients with clear cell renal cell carcinoma (ccRCC) as a method to guide biopsy targeting and to reduce undergrading. Six patients with ccRCC underwent presurgical HP 13C-MRI and conventional contrast-enhanced MRI. From the imaging data, three k-means clusters were computed by combining the kPL as a marker of metabolic activity, and the 13C-pyruvate signal-to-noise ratio (SNRPyr) as a perfusion surrogate. The combined clusters were compared to those derived from individual parameters and to those derived from the percentage of enhancement on the nephrographic phase (%NG). The diagnostic performance of each cluster was assessed based on its ability to predict the highest histological tumour grade in postsurgical tissue samples. The postsurgical tissue samples underwent immunohistochemical staining for the pyruvate transporter (monocarboxylate transporter 1, MCT1), as well as RNA and whole-exome sequencing. Results: The clustering approach combining SNRPyr and kPL demonstrated the best performance for predicting the highest tumour grade: specificity 85%; sensitivity 64%; positive predictive value 82%; and negative predictive value 68%. Epithelial MCT1 was identified as the major determinant of the HP 13C-MRI signal. The perfusion/metabolism mismatch cluster showed an increased expression of metabolic genes and markers of aggressiveness. Conclusions: This study demonstrates the potential of using HP 13C-MRI-derived metabolic clusters to identify intratumoral variations in tumour grade with high specificity. This work supports the use of metabolic imaging to guide biopsies to the most aggressive tumour regions and could potentially reduce sampling error. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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