Computer-based automated estimation of breast vascularity and correlation with breast cancer in DCE-MRI images
Ημερομηνία
2017Γλώσσα
en
Λέξη-κλειδί
Επιτομή
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with gadolinium constitutes one of the most promising protocols for boosting up the sensitivity in breast cancer detection. The aim of this study was twofold: first to design an image processing methodology to estimate the vascularity of the breast region in DCE-MRI images and second to investigate whether the differences in the composition/texture and vascularity of normal, benign and malignant breasts may serve as potential indicators regarding the presence of the disease. Clinical material comprised thirty nine cases examined on a 3.0-T MRI system (SIGNA HDx; GE Healthcare). Vessel segmentation was performed using a custom made modification of the Seeded Region Growing algorithm that was designed in order to identify pixels belonging to the breast vascular network. Two families of features were extracted: first, morphological and textural features from segmented images in order to quantify the extent and the properties of the vascular network; second, textural features from the whole breast region in order to investigate whether the nature of the disease causes statistically important changes in the texture of affected breasts. Results have indicated that: (a) the texture of vessels presents statistically significant differences (p < 0.001) between normal, benign and malignant cases, (b) the texture of the whole breast region for malignant and non-malignant breasts, produced statistically significant differences (p < 0.001), (c) the relative ratios of the texture between the two breasts may be used for the discrimination of non-malignant from malignant patients, and (d) an area under the receiver operating characteristic curve of 0.908 (AUC) was found when features were combined in a logistic regression prediction rule according to ROC analysis. © 2016 Elsevier Inc.
Collections
Related items
Showing items related by title, author, creator and subject.
-
Reproducibility of apparent diffusion coefficient measurements evaluated with different workstations
Fanariotis M., Vassiou K., Tsougos I., Fezoulidis I. (2018)Aim To evaluate apparent diffusion coefficient (ADC) measurements of breast lesions on different computer platforms to address post-processing influences on ADC measurement reproducibility. Materials and methods One hundred ... -
Diagnostic performance of quantitative diffusion tensor imaging for the differentiation of breast lesions at 3 T MRI
Tsougos I., Bakosis M., Tsivaka D., Athanassiou E., Fezoulidis I., Arvanitis D., Vassiou K. (2019)Background: Conventional breast magnetic resonance imaging (MRI), including dynamic contrast-enhanced MR mammography, may lead to ambiguous diagnosis and unnecessary biopsies. Purpose: To investigate the contribution of ... -
Breast Cancer Classification on Multiparametric MRI – Increased Performance of Boosting Ensemble Methods
Vamvakas A., Tsivaka D., Logothetis A., Vassiou K., Tsougos I. (2022)Introduction: This study aims to assess the utility of Boosting ensemble classification methods for increasing the diagnostic performance of multiparametric Magnetic Resonance Imaging (mpMRI) radiomic models, in differentiating ...