MRI EVALUATION OF LIVER LESIONS WITH HISTOPATHOLOGICAL CORRELATION
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Keywords
Liver lesions, Magnetic Resonance Imaging, Histopathological correlation, Diagnostic accuracy, Diffusion-weighted imaging This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution‑ Non Commercial‑Share Alike 4.0 License, which allows others to remix, tweak, and build upon the work non‑commercially,, as long as appropriate credit is given and the new creations are licensed under the identical terms.
Abstract
Introduction: Accurate characterization of liver lesions is crucial for optimal patient management. Magnetic Resonance Imaging (MRI) has emerged as a powerful non-invasive tool for liver lesion evaluation. This study aimed to assess the diagnostic accuracy of MRI in characterizing liver lesions through correlation with histopathological findings.
Methods: A prospective, observational study was conducted over 6 months, involving 150 patients with liver lesions. All patients underwent 3T MRI examinations including T1-weighted, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced sequences. Two radiologists independently analyzed the MRI images, and their findings were correlated with histopathological results. Statistical analysis included diagnostic accuracy measures, inter-observer agreement, and multivariate analysis of predictive MRI features.
Results: MRI demonstrated high diagnostic performance with 92.3% sensitivity, 88.7% specificity, and 90.7% overall accuracy in characterizing liver lesions. Strong inter-observer agreement was observed for most MRI features (κ = 0.79-0.92). Significant differences in ADC values were found between benign (1.85 ± 0.42 × 10⁻³ mm²/s) and malignant (1.12 ± 0.31 × 10⁻³ mm²/s) lesions (p<0.001). Multivariate analysis identified delayed washout (OR = 5.1), diffusion restriction (OR = 4.2), and arterial enhancement (OR = 3.5) as the strongest predictors of malignancy. Conclusion: MRI demonstrates excellent diagnostic accuracy in characterizing liver lesions, with strong correlation to histopathological findings. The identified predictive imaging features and quantitative parameters provide a robust framework for non-invasive lesion assessment, supporting the central role of MRI in the diagnostic algorithm for liver lesions.
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Dr. Subhash R
Assistant Professor, Department of Radio- R.N.T. Medical College, Udaipur, Rajasthan, India