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Mahmoudi S, Bernatz S, Ackermann J, Koch V, Dos Santos DP, Grünewald LD, Yel I, Martin SS, Scholtz JE, Stehle A, Walter D, Zeuzem S, Wild PJ, Vogl TJ, Kinzler MN. Computed Tomography Radiomics to Differentiate Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma. Clin Oncol (R Coll Radiol) 2023; 35:e312-e318. [PMID: 36804153 DOI: 10.1016/j.clon.2023.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/05/2022] [Accepted: 01/25/2023] [Indexed: 02/04/2023]
Abstract
AIMS Intrahepatic cholangiocarcinoma (iCCA) and hepatocellular carcinoma (HCC) differ in prognosis and treatment. We aimed to non-invasively differentiate iCCA and HCC by means of radiomics extracted from contrast-enhanced standard-of-care computed tomography (CT). MATERIALS AND METHODS In total, 94 patients (male, n = 68, mean age 63.3 ± 12.4 years) with histologically confirmed iCCA (n = 47) or HCC (n = 47) who underwent contrast-enhanced abdominal CT between August 2014 and November 2021 were retrospectively included. The enhancing tumour border was manually segmented in a clinically feasible way by defining three three-dimensional volumes of interest per tumour. Radiomics features were extracted. Intraclass correlation analysis and Pearson metrics were used to stratify robust and non-redundant features with further feature reduction by LASSO (least absolute shrinkage and selection operator). Independent training and testing datasets were used to build four different machine learning models. Performance metrics and feature importance values were computed to increase the models' interpretability. RESULTS The patient population was split into 65 patients for training (iCCA, n = 32) and 29 patients for testing (iCCA, n = 15). A final combined feature set of three radiomics features and the clinical features age and sex revealed a top test model performance of receiver operating characteristic (ROC) area under the curve (AUC) = 0.82 (95% confidence interval =0.66-0.98; train ROC AUC = 0.82) using a logistic regression classifier. The model was well calibrated, and the Youden J Index suggested an optimal cut-off of 0.501 to discriminate between iCCA and HCC with a sensitivity of 0.733 and a specificity of 0.857. CONCLUSIONS Radiomics-based imaging biomarkers can potentially help to non-invasively discriminate between iCCA and HCC.
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Affiliation(s)
- S Mahmoudi
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany.
| | - S Bernatz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany; Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany; Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany; University Cancer Center Frankfurt (UCT), University Hospital, Goethe University, Frankfurt am Main, Germany
| | - J Ackermann
- Department of Molecular Bioinformatics, Institute of Computer Science, Goethe University, Frankfurt am Main, Germany
| | - V Koch
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - D P Dos Santos
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - L D Grünewald
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - I Yel
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - S S Martin
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - J-E Scholtz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - A Stehle
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - D Walter
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - S Zeuzem
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - P J Wild
- Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany; Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany; Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany
| | - T J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - M N Kinzler
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
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Mahmoudi S, Bernatz S, Althoff FC, Koch V, Grünewald LD, Scholtz JE, Walter D, Zeuzem S, Wild PJ, Vogl TJ, Kinzler MN. Dual-energy CT based material decomposition to differentiate intrahepatic cholangiocarcinoma from hepatocellular carcinoma. Eur J Radiol 2022; 156:110556. [PMID: 36270195 DOI: 10.1016/j.ejrad.2022.110556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/29/2022] [Accepted: 10/07/2022] [Indexed: 11/20/2022]
Abstract
PURPOSE To assess the potential of material decomposition in dual-energy CT (DECT) to differentiate intrahepatic cholangiocarcinoma (iCCA) from hepatocellular carcinoma (HCC). METHOD In this retrospective study, we included 94 patients (26 female (27.7 %), median age 64.5 (interquartile range 55.5-74.5) years) with either iCCA or HCC who underwent abdominal contrast-enhanced DECT in arterial phase. To test for differences between iCCA (n = 47) and HCC (n = 47), we evaluated mean attenuation and DECT material density values including iodine density (ID), normalized iodine uptake (NIU), fat fraction, and lesion-to-liver parenchyma ratio. Histopathology served as reference standard for all lesions. We used univariate logistic regression models for the outcome iCCA versus HCC. ROC curve analysis was applied to assess discriminative ability of the model. Model accuracy was evaluated by calculating the Brier score. Youden index was applied to establish thresholds to differentiate between iCCA and HCC. RESULTS Comparison of quantitative image parameters revealed significant differences between iCCA and HCC for ID (1.6 ± 0.5 mg/ml vs 2.8 ± 0.8 mg/ml, p < 0.001), NIU (14.5 ± 4.8 vs 24.8 ± 10.3, p < 0.001), attenuation (41.9 ± 10.1 HU vs 47.9 ± 8.9 HU, p = 0.003), and fat fraction (12.0 ± 7.8 % vs 9.0 ± 6.4 %, p = 0.045). ROC curve analysis revealed highest ability to differentiate iCCA from HCC for ID (AUC = 0.93, 95 % CI 0.89-0.98). For ID, an optimal threshold of 2.33 mg/dl was determined to discriminate between iCCA and HCC (sensitivity 89.4 %, specificity 76.6 %). CONCLUSIONS DECT-based iodine quantification can serve as a tool for the differentiation of iCCA and HCC in contrast-enhanced CT. ID yielded the highest diagnostic performance and may assist in clinical routine CT diagnostics.
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Affiliation(s)
- Scherwin Mahmoudi
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
| | - Simon Bernatz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
| | - Friederike C Althoff
- Department of Internal Medicine II, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
| | - Vitali Koch
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
| | - Leon D Grünewald
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
| | - Jan-Erik Scholtz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
| | - Dirk Walter
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
| | - Stefan Zeuzem
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
| | - Peter J Wild
- Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany; Wildlab, University Hospital Frankfurt MVZ GmbH, Frankfurt am Main, Germany.
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
| | - Maximilian N Kinzler
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany.
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