05.07.2017 | 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
verfasst von:
Yin Xi, Qing Yuan, Yue Zhang, Ananth J. Madhuranthakam, Michael Fulkerson, Vitaly Margulis, James Brugarolas, Payal Kapur, Jeffrey A. Cadeddu, Ivan Pedrosa
Erschienen in:
European Radiology
|
Ausgabe 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.