Texture Analysis on Routine Mri Sequences to Differentiate Betweefocal Nodular Hyperplasia and Hepatocellular Adenoma Publisher



Salahshour F^{1, 2} ; Khameneh AG¹ ; Amirkhiz GDH¹ ; Yazdi NA¹ ; Shafiekhani S³ Show Affiliations
Source: Polish Journal of Radiology Published:2023

Abstract

Purpose: We investigated the diagnostic power of texture analysis (TA) performed on MRI (T2-weighted, gadolinium-enhanced, and diffusion-weighted images) to differentiate between focal nodular hyperplasia (FNH) and hepato-cellular adenoma (HCA). Material and methods: This was a retrospective single-centre study. Patients referred for liver lesion characterization, who had a definitive pathological diagnosis, were included. MRI images were taken by a 3-Tesla scanner. The values of TA parameters were obtained using the ImageJ platform by an observer blinded to the clinical and pathology judgments. A non-parametric Mann-Whitney U test was applied to compare parameters between the 2 groups. With receiver operating characteristic (ROC) analysis, the area under the curve (AUC), sensitivity, and specificity were calculated. Finally, we performed a binary logistic regression analysis. A p-value <0.05 was reported as statistically significant. Results: A total of 62 patients with 106 lesions were enrolled. T2 hyperintensity, Atoll sign, and intralesional fat were encountered more in HCAs, and central scars were more frequent in FNHs. Multiple TA features showed statistically significant differences between FNHs and HCAs, including skewness on T2W and entropy on all sequences. Skewness on T2W revealed the most significant AUC (0.841, good, p < 0.0001). The resultant model from binary logistic regression was statistically significant (p < 0.0001) and correctly predicted 84.1% of lesions. The corresponding AUC was 0.942 (excellent, 95% CI: 0.892-0.992, p < 0.0001). Conclusion: Multiple first-order TA parameters significantly differ between these lesions and have almost fair to good diagnostic power. They have differentiation potential and can add diagnostic value to routine MRI evaluations. © Pol J Radiol 2023.