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|Title: ||Classification of breast lesions using quantitative dynamic contrast enhanced-MRI|
|Authors: ||Jayatilake, M.|
|Editors: ||Kulczycki, P.|
|Keywords: ||Magnetic Resonance Imaging|
|Issue Date: ||2018|
|Citation: ||Mohan Jayatilake, Teresa Gonçalves, and Luı́s Rato. Classification of breast lesions
using quantitative dynamic contrast enhanced-MRI. In P. Kulczycki R.P. Barneva, V.E. Brimkov and J.M.R.S. Tavares, editors, CompIMAGE2018 - Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications, volume (to appear) of LNCS, page (to appear). Springer, 2018.|
|Abstract: ||Imaging biomarkers are becoming important in both research and clinical studies. This study is focused on developing measures of tumour mean, fractal dimension, homogeneity, energy, skewness and kurtosis that reflect the values of the pharmacokinetic (PK) parameters within the breast tumours, evaluate those using clinical data and in-
vestigate their feasibility as a biomarker to discriminate malign from benign breast lesions. In total, 75 patients with breast cancer underwent Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). Axial bilateral images with fat-saturation and full breast coverage were performed at 3T Siemens with a 3D gradient echo-based TWIST sequence. The whole tumour mean, fractal dimension, homogeneity, energy, skewness and kurtosis of K trans and V e values were calculated.
Median of both the mean and fractal dimension of K trans and V e for benign and malignant show significant discrimination. Further, the median of skewness and kurtosis of V e between benign and malignant are also
significantly varying. In conclusion, mean and fractal dimension of both Ktrans and V e and skewness and kurtosis of Ve for typical breast cancer, computed from PK parametric maps, show potential as a biomarker for
breast tumour diagnosis either as a benign or malignant.|
|Appears in Collections:||INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica|
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