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European Radiology
Published in: European Radiology 1/2018

01-01-2018 | Magnetic Resonance

Statistical clustering of parametric maps from dynamic contrast enhanced MRI and an associated decision tree model for non-invasive tumour grading of T1b solid clear cell renal cell carcinoma

Authors: Yin Xi, Qing Yuan, Yue Zhang, Ananth J. Madhuranthakam, Michael Fulkerson, Vitaly Margulis, James Brugarolas, Payal Kapur, Jeffrey A. Cadeddu, Ivan Pedrosa

Published in: European Radiology | Issue 1/2018

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Abstract

Objectives

To apply a statistical clustering algorithm to combine information from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) into a single tumour map to distinguish high-grade from low-grade T1b clear cell renal cell carcinoma (ccRCC).

Methods

This prospective, Institutional Review Board -approved, Health Insurance Portability and Accountability Act -compliant study included 18 patients with solid T1b ccRCC who underwent pre-surgical DCE MRI. After statistical clustering of the parametric maps of the transfer constant between the intravascular and extravascular space (K trans ), rate constant (K ep ) and initial area under the concentration curve (iAUC) with a fuzzy c-means (FCM) algorithm, each tumour was segmented into three regions (low/medium/high active areas). Percentages of each region and tumour size were compared to tumour grade at histopathology. A decision-tree model was constructed to select the best parameter(s) to predict high-grade ccRCC.

Results

Seven high-grade and 11 low-grade T1b ccRCCs were included. High-grade histology was associated with higher percent high active areas (p = 0.0154) and this was the only feature selected by the decision tree model, which had a diagnostic performance of 78% accuracy, 86% sensitivity, 73% specificity, 67% positive predictive value and 89% negative predictive value.

Conclusions

The FCM integrates multiple DCE-derived parameter maps and identifies tumour regions with unique pharmacokinetic characteristics. Using this approach, a decision tree model using criteria beyond size to predict tumour grade in T1b ccRCCs is proposed.

Key Points

• Tumour size did not correlate with tumour grade in T1b ccRCC.
• Tumour heterogeneity can be analysed using statistical clustering via DCE-MRI parameters.
• High-grade ccRCC has a larger percentage of high active area than low-grade ccRCCs.
• A decision-tree model offers a simple way to differentiate high/low-grade ccRCCs.
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Metadata
Title
Statistical clustering of parametric maps from dynamic contrast enhanced MRI and an associated decision tree model for non-invasive tumour grading of T1b solid clear cell renal cell carcinoma
Authors
Yin Xi
Qing Yuan
Yue Zhang
Ananth J. Madhuranthakam
Michael Fulkerson
Vitaly Margulis
James Brugarolas
Payal Kapur
Jeffrey A. Cadeddu
Ivan Pedrosa
Publication date
01-01-2018
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 1/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-017-4925-6

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