Computed tomography (CT) and magnetic resonance imaging (MRI) of diffuse liver disease: a multiparametric predictive modelling algorithm can aid categorization of liver parenchyma

Ricardo Donners; Carmen Zaugg; Julian E Gehweiler; Tuyana Boldanova; Markus H Heim; Luigi M Terracciano; Daniel T Boll

doi:10.21037/qims-21-384

Computed tomography (CT) and magnetic resonance imaging (MRI) of diffuse liver disease: a multiparametric predictive modelling algorithm can aid categorization of liver parenchyma

Quant Imaging Med Surg. 2022 Feb;12(2):1186-1197. doi: 10.21037/qims-21-384.

Authors

Ricardo Donners¹, Carmen Zaugg¹, Julian E Gehweiler¹, Tuyana Boldanova^{2

3}, Markus H Heim^{2

3}, Luigi M Terracciano⁴, Daniel T Boll¹

Affiliations

¹ Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland.
² Division of Gastroenterology and Hepatology, University Hospital Basel, Basel, Switzerland.
³ Department of Biomedicine, University of Basel, Basel, Switzerland.
⁴ Institute of Pathology, University Hospital Basel, Basel, Switzerland.

Abstract

Background: Liver steatosis is common and tracking disease evolution to steatohepatitis and cirrhosis is essential for risk stratification and resultant patient management. Consequently, diagnostic tools allowing categorization of liver parenchyma based on routine imaging are desirable. The study objective was to compare established mono-factorial, dynamic single parameter and iterative multiparametric routine computed tomography (CT) and magnetic resonance imaging (MRI) analyses to distinguish between liver steatosis, steatohepatitis, cirrhosis and normal liver parenchyma.

Methods: A total of 285 multi-phase contrast enhanced CT and 122 MRI studies with histopathological correlation of underlying parenchymal condition were retrospectively included. Parenchymal conditions were characterized based on CT Hounsfield units (HU) or MRI signal intensity (SI) measurements and calculated HU or SI ratios between non-contrast and contrast enhanced imaging time points. First, the diagnostic accuracy of mono-factorial analyses using established, static non-contrast HU and in- to opposed phase SI change cut-offs to distinguish between parenchymal conditions was established. Second, single dynamic discriminator analyses, with optimized non-contrast and enhancement HU and SI ratio cut-off values derived from the data, employing receiver operating characteristic (ROC) curve areas under the curve (AUCs) and the Youden index for maximum accuracy, were used for disease diagnosis. Third, multifactorial analyses, employing multiple non-contrast and contrast enhanced HU and SI ratio cut-offs in a nested, predictive-modelling algorithm were performed to distinguish between normal parenchyma, liver steatosis, steatohepatitis and cirrhosis. CT and MRI analyses were performed separately.

Results: No single CT or MRI parameter showed significant difference between all four parenchymal conditions (each P>0.05). Mono-factorial static-CT-discriminator analyses identified liver steatosis with 75% accuracy. Mono-factorial MRI analyses identified steatosis with 89% accuracy. Single-dynamic CT parameter analyses identified normal parenchyma with 72% accuracy and cirrhosis with 75% accuracy. Single-dynamic MRI parameter analyses identified fatty parenchyma with 90% accuracy. Multifactorial CT analyzes identified normal parenchyma with 84%, liver steatosis with 95%, steatohepatitis with 95% and cirrhosis with 80% accuracy. Multifactorial predictive modelling of MRI parameters identified normal parenchyma with 79%, liver steatosis with 89%, steatohepatitis with 92% and cirrhosis with 89% accuracy.

Conclusions: Multiparametric analyses of quantitative measurements derived from routine CT and MRI, utilizing a predictive modelling algorithm, can help to distinguish between normal liver parenchyma, liver steatosis, steatohepatitis and cirrhosis.

Keywords: Liver; decision-support; diagnostic imaging; fatty liver; liver cirrhosis.