Abstract
Purpose: The aim of this thesis was to detennine the diagnostic accuracy as well as the sensitivity of CT scan and MRI in detecting hepatocellular carcinoma HCC). Methodology: This is a retrospective study to identify the sensitivity and specificity of CT scan and MRI in detecting HCC. The study was done at Diagnostic and Radiology Department, Selayang Hospital. The time frame for the sample data collection was taken from January 2013 until March 2015. 74 samples were selected based on the inclusion criteria of the study. The patient data selections were strictly the patients who underwent for both CT scan and MRl for detection of HCC. The reference of standard that was used to confinn the presence of the HCC included other interventional findings such as percutaneous biopsy, surgical resection and liver transplant. Result: The results were used in Bayers' theorem to calculate the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of CT scan and MRI in detecting HCC. Based on the Bayers' theorem, the sensitivity of CT scan and MRI is 81% and 94% respectively. The specificity of CT scan and MRI is 40% and 90% respectively. The positive predictive value of CT scan and MRI is 90% and 98% respectively. The negative predictive value of CT scan and MRl is 25% and 69% respectively. Diagnostic accuracy of CT scan and MRI in detecting HCC is 76% and 93% respectively. From the result, it showed that CT scan and MRI have higher sensitivity in detecting HCC. However, result was found MRI is more sensitive in detecting small HCC lesion less than 2 cm. Conclusion: CT scan and MRI show similar diagnostic perfonnance for the detection of HCC. However, the advancement in MRI technologies and use of gadoxetix acidenhanced MRI improve the sensitivity in the detection of HCC lesion less than 2 cm in diameter.
Metadata
Item Type: | Thesis (Degree) |
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Creators: | Creators Email / ID Num. Osman, Nurain UNSPECIFIED |
Subjects: | R Medicine > R Medicine (General) > Biomedical engineering R Medicine > R Medicine (General) > Computer applications to medicine. Medical informatics R Medicine > R Medicine (General) > Neural Networks (Computer). Artificial intelligence |
Divisions: | Universiti Teknologi MARA, Shah Alam > Faculty of Health Sciences |
Programme: | Bachelor of Medical Imaging |
Keywords: | Computed Tomography, Magnetic Resonance Imaging, Hepatocellular Carcinoma |
Date: | 2015 |
URI: | https://ir.uitm.edu.my/id/eprint/28091 |
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