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.
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...
| Publicado en: | European Radiology Vol. 28; no. 1; pp. 124 - 133 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126562811&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126562811 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jan2018 vid: 28 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 126562811 126562811 143939567 NLM28681074 126562811 10.1007/s00330-017-4925-6 NLM28681074 126562811 ppf: 124 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 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. aug: au: Xi, Yin Yuan, Qing Zhang, Yue Madhuranthakam, Ananth J. Fulkerson, Michael Margulis, Vitaly Brugarolas, James Kapur, Payal Cadeddu, Jeffrey A. Pedrosa, Ivan affil: Department of Radiology, UT Southwestern Medical Center, 2201 Inwood Road, 75235-9085, Dallas, TX, USA sug: subj: Carcinoma, Renal Cell Pathology Contrast Media Image Enhancement Methods Decision Trees Kidney Neoplasms Pathology Magnetic Resonance Imaging Methods Middle Age Pharmacokinetics Algorithms Kidney Neoplasms Carcinoma, Renal Cell Female Prospective Studies Sensitivity and Specificity Neoplasm Grading Male Magnetic Resonance Imaging Statistics and Numerical Data Funding Source Human Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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