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Comparisons of Electrical Capacitance Volume Tomography and Ultrasonography for Breast Cancer Detection

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Background: In our previous study, we have developed a novel sensor for Electrical Capacitance Volume Tomography (ECVT) to map the distribution of tumor inside the human breast. The ECVT provides a volumetric image of permittivity distribution of the breast tissue, showing high permittivity of abnormal tissue characterized with simple cyst, benign tumor or malignant cancer as compared with normal breast. In this study, we evaluate the ECVT to differentiate malignant cancers from benign tumors. The study showed statistically capacitance measurement value of breasts with malignant cancers identified independently with USG are relatively higher than breasts with benign tumors. Methods: Serial cases involving 120 patients diagnosed with simple cysts, benign tumors or malignant cancers with Ultrasonography (USG) are scanned using the ECVT, and the images from both of the modalities are compared. All the patients are informed with the purpose and procedure of the experiment. Results: It is found that the maximum capacitance measurement value obtained by the ECVT is correlated strongly with the malignancy of the tumor identified by the USG. 90% of all malignant data reside above the value of normalized capacitance of 0.3. Conclusions: This findings suggest the feasibility of the ECVT technique to differentiate the malignant cancer from benign tumor with relatively high selectivity. However, the study is limited by an uncertainty of USG technique in determining the malignancy of the breast tumors, as procedure of biopsy as a golden standard of malignancy evaluation is not performed.
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Keywords: BREAST CANCER DETECTION; ELECTRICAL CAPACITANCE VOLUME TOMOGRAPHY; ULTRASONOGRAPHY; VOLUMETRIC IMAGE

Document Type: Research Article

Publication date: August 1, 2014

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  • Advanced Science, Engineering and Medicine (ASEM) is a science, engineering, technical and medical journal focused on the publishing of peer-reviewed multi-disciplinary research articles dealing with all fundamental and applied research aspects in the areas of (1) Physical Sciences, (2) Engineering, (3) Biological Sciences/Health Sciences, (4) Medicine, (5) Computer and Information Sciences, (6) Mathematical Sciences, (7) Agriculture Science and Engineering, (8) Geosciences, and (9) Energy/Fuels/Environmental/Green Science and Engineering.
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