Combined DCE-MRI- and FDG-PET enable histopathological grading prediction in a rat model of hepatocellular carcinoma

Georgios A. Kaissis, Fabian K. Lohöfer, Marie Hörl, Irina Heid, Katja Steiger, Kim Agnes Munoz-Alvarez, Markus Schwaiger, Ernst J. Rummeny, Wilko Weichert, Philipp Paprottka, Rickmer Braren

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Purpose: To test combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-FDG positron emission tomography (FDG-PET)-derived parameters for prediction of histopathological grading in a rat Diethyl Nitrosamine (DEN)-induced hepatocellular carcinoma (HCC) model. Methods: 15 male Wistar rats, aged 10 weeks were treated with oral DEN 0.01 % in drinking water and monitored until HCCs were detectable. DCE-MRI and PET were performed consecutively on small animal scanners. 38 tumors were identified and manually segmented based on HCC-specific contrast enhancement patterns. Grading (G2/3: 24 tumors, G1:14 tumors) alongside other histopathological parameters, tumor volume, contrast agent and 18F-FDG uptake metrics were noted. Class imbalance was addressed using SMOTE and collinearity was removed using hierarchical clustering and principal component analysis. A logistic regression model was fit separately to the individual parameter groups (DCE-MRI-derived, PET-derived, tumor volume) and the combined parameters. Results: The combined model using all imaging-derived parameters achieved a mean ± STD sensitivity of 0.88 ± 0.16, specificity of 0.70 ± 0.20 and AUC of 0.90 ± 0.03. No correlation was found between tumor grading and tumor volume, morphology, necrosis, extracellular matrix, immune cell infiltration or underlying liver fibrosis. Conclusion: A combination of DCE-MRI- and 18F-FDG-PET-derived parameters provides high accuracy for histopathological grading of hepatocellular carcinoma in a relevant translational model system.

Original languageEnglish
Article number108848
JournalEuropean Journal of Radiology
StatePublished - Mar 2020
Externally publishedYes


  • Hepatocellular carcinoma
  • Preclinical models
  • Tumour grading


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