Diagnostic Accuracy of Diffusion Weighted Magnetic Resonance Imaging in Differentiating Malignant from Benign Liver Lesions Taking Histopathology as Gold Standard
DOI:
https://doi.org/10.59058/jaimc.2026.24.1.111Keywords:
KEY WORDS: Diagnostic Accuracy, Histopathology, Liver Lesions, Sensitivity, Specificity, PPV, NPVAbstract
Background & Objective: Liver lesions are common, and distinguishing between malignant and benign types is crucial.Histopathology, the gold standard, is invasive and costly. DW MRI, a promising non-invasive alternative, requires further
research to confirm its diagnostic accuracy in liverlesion differentiation.Objective was to determine the diagnostic accuracy
of diffusion weighted MRI in differentiating malignant from benign liver lesions taking histopathology as gold standard.
Methodology: A total of 150 patients meeting inclusion criteria were enrolled from the Department of Diagnostic
Radiology, Sir Ganga Ram Hospital, Lahore. Written consent and medical history were obtained, and all underwent
diffusion-weighted MRI on a 1.5-T scanner. MRI findings were compared with histopathology for assessing diagnostic
accuracy of DW MRI in differentiating liver lesions from benign lesions. Data was analyzed using SPSS 26.0. Sensitivity,
specificity, and predictive values were calculated, and results were stratified by age and gender.
Results: The patients had a mean age of 46.77±14.63 years and males were dominant with n=86 (57.3%). Participants
aged 18–40 years were 45.3%, while 54.7% were older than 40 years. DW MRI classified 61.3% of lesions as malignant,
confirmed as 64.7% by histopathology. Sensitivity, specificity, PPV, NPV, and accuracy were 90.72%, 92.45%, 95.66%,
84.46%, and 98.70%, respectively.
Conclusion: Diffusion-weighted MRI demonstrated higher diagnostic accuracy in differentiating malignant from
benign liver lesions while taking histopathology as the gold standard, showing high sensitivity, specificity, and
consistent performance across age and gender, supporting its clinical utility
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Copyright (c) 2026 Kiran Sarfraz, Saman Chaudhry, Verda Ashraf, Zunair Maqsood, Hira Ashfaq Butt, Shahzaib Nawaz (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.