A Diagnostic Accuracy of Multidetector Computed Tomography in Distinguishing Benign and Malignant Focal Liver Lesions at the National Referral Hospital, Bhutan: A Cross-sectional Study.
DOI:
https://doi.org/10.47811/217Keywords:
Diagnostic accuracy; Histopathology; Liver lesions; Multidetector CTAbstract
Introduction: Focal liver lesions (FLLs) are common findings in clinical practice, requiring accurate characterization to guide patient management. Multidetector computed tomography (MDCT) is widely available and routinely used in Bhutan. Therefore, this study aimed to determine the diagnostic accuracy of multidetector computed tomography (MDCT) in detecting focal liver lesions compared with histopathological findings.
Methods: This cross-sectional study was conducted at the Radiology Department of the Jigme Dorji Wangchuck National Referral Hospital from January 2024 to December 2025. All patients who underwent MDCT were included in the study. Data were collected by reviewing medical documents and recorded using a standard pro forma, and data were analyzed using SPSS (version 26).
Results: The majority were female (57.1%), with a mean age of 57.49 years. Histopathological examination revealed 79.4% malignant and 20.6% benign lesions. MDCT correctly characterized 49 malignant lesions (72%) and 9 benign lesions (8%), yielding an overall diagnostic accuracy of 92.1%. The sensitivity and specificity of MDCT for differentiating malignant from benign lesions were 98.0% and 69.2%, respectively, with a PPV of 92.5% and NPV of 90.0%. Cohen's kappa coefficient showed substantial agreement between MDCT and histopathology (kappa = 0.74). The association between MDCT diagnosis and histopathology was statistically significant (chi-square = 34.923, p < 0.001).
Conclusion: MDCT demonstrates high diagnostic accuracy in characterizing focal liver lesions, with excellent sensitivity for detecting malignant lesions. These findings support the continued use of MDCT as the primary imaging modality in Bhutan, where access to advanced imaging modalities is limited.
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Copyright (c) 2026 Tashi Gyeltshen, Gyem Dorji, Kanokwan Wetasin, Birendra Pradhan

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