Quantitative and qualitative liver CT: imaging feature association with histopathologically confirmed hepatic cirrhosis

Purpose To assess the diagnostic performance of quantitative and qualitative imaging features of hepatic cirrhosis on CT. Methods A single-center retrospective cohort study was performed on all patients who had undergone non-targeted liver biopsy < 3 months following abdominal CT imaging between 2007 and 2020. Histopathology was required as a reference standard for hepatic cirrhosis diagnosis. Two readers independently assessed all CT quantitative and qualitative features, blinded to the clinical history and the reference standard. The diagnostic performance of each imaging feature was assessed using multivariate regression and logistic regression in a recursive feature elimination framework. Results 98 consecutive patients met inclusion criteria including 26 with histopathologically confirmed hepatic cirrhosis, and 72 without cirrhosis. Liver surface nodularity (p < 0.0001), lobar redistribution (p < 0.0001), and expanded gallbladder fossa (p < 0.0016) were qualitative CT features associated with liver cirrhosis consistent between both reviewers. Liver surface nodularity demonstrated highest sensitivity (73-77%) and specificity (79-82%). Falciform space width was the only quantitative feature associated with cirrhosis, for a single reviewer (p < 0.04). Using a recursive feature elimination framework, liver surface nodularity and falciform space width were the strongest performing features for identifying cirrhosis. No feature combinations strengthened diagnostic performance. Conclusion Many quantitative and qualitative CT imaging signs of hepatic cirrhosis have either poor accuracy or poor interobserver agreement. Qualitative imaging features of hepatic cirrhosis on CT performed better than quantitative metrics, with liver surface nodularity the most optimal feature for diagnosing hepatic cirrhosis. [GRAPHICS] .

Automated summary

This study assessed the diagnostic performance of CT imaging features for hepatic cirrhosis, revealing that liver surface nodularity is the optimal feature for diagnosis.