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.
Eur Radiol
; 28(1): 124-132, 2018 Jan.
Article
em En
| MEDLINE
| ID: mdl-28681074
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Árvores de Decisões
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Imageamento por Ressonância Magnética
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Aumento da Imagem
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Carcinoma de Células Renais
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Meios de Contraste
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Neoplasias Renais
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Female
/
Humans
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Male
/
Middle aged
Idioma:
En
Revista:
Eur Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Estados Unidos