K-means clustering of hyperpolarised13C-MRI identifies intratumoural perfusion/metabolism mismatch in renal cell carcinoma as best predictor of highest grade

Abstract

AbstractPurposeConventional renal mass biopsy approaches are inaccurate, potentially leading to undergrading. This study explored using hyperpolarised [1-13C]pyruvate MRI (HP13C-MRI) to identify the most aggressive areas within the tumour of patients with clear cell renal cell carcinoma (ccRCC).Experimental designSix patients with ccRCC underwent presurgical HP13C-MRI and conventional contrast-enhanced MRI. Three k-means clusters were computed by combining thekPLas a marker of metabolic activity, and the13C-pyruvate signal-to-noise ratio (SNRPyr) as a perfusion surrogate. Combined clusters were compared to those derived from individual parameters and to those derived from percentage enhancement on 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. Tissues were further subject to MCT1 staining, RNA and whole-exome sequencing.ResultsForty-four samples were collected in total. The clustering approach combining SNRPyrandkPLdemonstrated the best performance for predicting 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 HP13C-MRI signal. The perfusion/metabolism mismatch cluster showed increased expression of metabolic genes and markers of aggressiveness, which may be due to genetic divergence.ConclusionsThis study demonstrates the potential of using HP13C-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, which could potentially reduce sampling error.