Diagnosing ovarian masses by using nuclear magnetic resonance
Keywords:
Nuclear magnetic resonance, Ovarian tumour, Clinical value, Diagnostic value
Abstract
In the research, nuclear magnetic resonance (NMR) was used in order to study and diagnose ovarian tumour patients. A total of 80 cases of ovarian tumour patients who admitted to hospital in February 2014 to May 2016 were selected and randomly divided into 2 groups, with 40 cases in each group, namely; the experimental- and contrast group. The experimental group used NMR to diagnose whereas the contrast group utilized B-mode ultrasound in order to compare the character of masses and ascertain accuracy, sensitivity, and specificity of the clinical diagnostic afterwards. The accuracy, sensitivity, specificity, and characteristic of tumour masses in the experimental group was 95.84%, 94.75%, 90.92%, and 100%, respectively, which is apparently higher than the contrast group with 64.28%, 77.78%, 75.08%, and 70.83%, respectively. Both groups were found to be statistically significant different (p < 0.05). In comparison to NMR and ultrasound, former diagnostic method was better in terms of accuracy, sensitivity, and specificity which lead to significant detection and better diagnostic.References
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2. Liang YZ, Zhang HW, Cui Q. NMR diagnosis of ovarian cancer. J Chin Pharm Sci 2014; 4(21): 114–116.
3. Wan ZH. Magnetic resonance spectroscopy applications in brain tumor diagnosis. J Vasc Card Cereb 2013; 11: 83–84.
4. Wang X, Chen Z. Combined use of magnetic resonance imaging and ultrasonography in the diagnosis of ovarian cancer. Hebei Med 2015;21(3): 487–489.
5. Xiong K, Yu DF. Diagnostic value and significance of PET/CT and MRI for gynecologic malignant tumors. Chin J Woman Child Health Res 2014; 5: 885–887.
6. Gao C, Pu J, Sui C. Characteristics and clinical application of NMR imaging ovarian tumors. Chin Matern Child Health 2016; 3: 628–631.
7. Mao F. Nuclear magnetic resonance imaging in diagnosing ovarian cancer. Med Forum 2011; 15(31): 1040–1041.
Published
2017-01-19
Issue
Section
Original Research Article
Copyright (c) 2016 Zhang Erning
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