New ultrafast view-sharing sequences have enabled breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to be performed at high spatial and temporal resolution. The aim of this study is to evaluate the diagnostic potential of textural features that quantify the spatiotemporal changes of the contrast-agent uptake in computer-aided diagnosis of malignant and benign breast lesions imaged with high spatial and temporal resolution DCE-MRI. METHOD: The proposed approach is based on the textural analysis quantifying the spatial variation of six dynamic features of the early-phase contrast-agent uptake of a lesion's largest cross-sectional area. The textural analysis is performed by means of the second-order gray-level co-occurrence matrix, gray-level run-length matrix and gray-level difference matrix. This yields 35 textural features to quantify the spatial variation of each of the six dynamic features, providing a feature set of 210 features in total. The proposed feature set is evaluated based on receiver operating characteristic (ROC) curve analysis in a cross-validation scheme for random forests (RF) and two support vector machine classifiers, with linear and radial basis function (RBF) kernel. Evaluation is done on a dataset with 154 breast lesions (83 malignant and 71 benign) and compared to a previous approach based on 3D morphological features and the average and standard deviation of the same dynamic features over the entire lesion volume as well as their average for the smaller region of the strongest uptake rate. RESULT: The area under the ROC curve (AUC) obtained by the proposed approach with the RF classifier was 0.8997, which was significantly higher (P = 0.0198) than the performance achieved by the previous approach (AUC = 0.8704) on the same dataset. Similarly, the proposed approach obtained a significantly higher result for both SVM classifiers with RBF (P = 0.0096) and linear kernel (P = 0.0417) obtaining AUC of 0.8876 and 0.8548, respectively, compared to AUC values of previous approach of 0.8562 and 0.8311, respectively. CONCLUSION: The proposed approach based on 2D textural features quantifying spatiotemporal changes of the contrast-agent uptake significantly outperforms the previous approach based on 3D morphology and dynamic analysis in differentiating the malignant and benign breast lesions, showing its potential to aid clinical decision making.
COBISS.SI-ID: 2742139
A new method of magnetic resonance imaging in which contrast depends on the tissue conductivity was developed. The method has an interesting property, that is it enables acquisition of two different conductivity-weighted images in a single scan. One of the images corresponds to tissue conductivity at low (DC) frequencies and the other to tissue conductivity at high (RF) frequencies. Images of tissue conductivity provide new tissue information and may therefore be used as a supplement to more established MRI methods, of which contrast depends primarily on nuclear relaxation times of the tissue, rate of diffusion in it or the presence of macromolecules ... As such, the proposed imaging method also applicable in characterization of different tumor types.
COBISS.SI-ID: 28001575
The paper deals with prediction of the treatment effects of irreversible electroporation (IRE). IRE is a method for nonthermal ablation of solid tumors. For its success it is extremely important that a coverage and an exposure time of the treated tumor with electric field is within the specified range. Measurement of electric field distribution during the electroporation treatment can be achieved using Magnetic Resonance Electrical Impedance Tomography (MREIT). In the study it is shown that MREIT can enable electroporation monitoring of IRE-treated tumors and also enables prediction of IRE ablated tumor areas during IRE of mouse tumors in vivo. This was achieved by coupling MREIT with the corresponding Peleg-Fermi mathematical model which provides cell death probability in the IRE-treated tumors. This technique can potentially be used in electroporation-based clinical applications, such as IRE tissue ablation and electrochemotherapy, to improve and assure the treatment outcome and also in deep brain stimulation for monitoring of electric field distribution.
COBISS.SI-ID: 11799380