Search

Objective: Abbreviated Breast Magnetic Resonance Imaging (MRI) is a fast and a selected scan, used for screening women at high risk of breast cancer. The objective of this study is to assess the diagnostic accuracy of a new shortened Abbreviated Protocol (AP) relative to Full Diagnostic Protocol (FDP). Methods: 206 breast MRIs were evaluated, respectively. AP was derived from the FDP and re-recorded. The new report was compared with the report of the previous FDP. The interpretation time of the shortened protocol was recorded. The results of the two protocols in terms of finding the lesion were compared using sensitivity, specificity, NPV, and PPV according to the histopathology results. Results: 124 of 206 MRIs were malignant and 82 of 206 MRIs were benign. The average interpretation time was 58±35s with AP. The MIP sequence evaluation time was only 17±12s. The PPV, NPV, sensitivity and specificity values for AP MRI were 93.0%, 94.8%, 96.77%, 96.8% and 89.0% respectively. The PPV, NPV, sensitivity and specificity values for FDP MRI were 94.5%, 96.2%, 97.6% and 91.5% respectively. There was no significant difference in sensitivity and specificity for both protocols (p< 0.05). Conclusion: AP is a new and shorter version of a Breast MRI. The diagnostic accuracy of abbreviated breast MRI for the detection of breast lesions shows a high level of sensitivity and specificity, with the advantages of shortening both the exam time and the interpretation time. [ABSTRACT FROM AUTHOR]

Aim: To reduce operator dependency and achieve greater accuracy, the computer-aided diagnosis (CAD) systems are becoming a useful tool for detecting noninvasively and determining tissue characterization in medical images. We aimed to suggest a CAD system in discriminating between benign and malignant breast masses. Methods: The dataset was composed of 105 randomly breast magnetic resonance imaging (MRI) including biopsy-proven breast lesions (53 malignant, 52 benign). The expectation-maximization (EM) algorithm was used for image segmentation. 2D-discrete wavelet transform was applied to each region of interests (ROIs). After that, intensity-based statistical and texture matrix-based features were extracted from each of the 105 ROIs. Random Forest algorithm was used for feature selection. The final set of features, by random selection base, splatted into two sets as 80% training set (84 MRI) and 20% test set (21 MRI). Three classification algorithms are such that decision tree (DT, C4.5), naive bayes (NB), and linear discriminant analysis (LDA) were used. The accuracy rates of algorithms were compared. Results: C4.5 algorithm classified 20 patients correctly with a success rate of 95.24%. Only one patient was misclassified. The NB classified 19 patients correctly with a success rate of 90.48%. The LDA Algorithm classified 18 patients correctly with a success rate of 85.71%. Conclusion: The CAD equipped with the EM segmentation and C4.5 DT classification was successfully distinguished as benign and malignant breast tumor on MRI. [ABSTRACT FROM AUTHOR]