Computed Tomography Radiomics to Differentiate Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma |
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| Affiliation: | 1. Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany;2. Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany;3. Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany;4. University Cancer Center Frankfurt (UCT), University Hospital, Goethe University, Frankfurt am Main, Germany;5. Department of Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany;7. Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany;11. Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany;1. The Institute of Cancer Research, London, UK;2. Head and Neck Unit, The Royal Marsden, London, UK;3. Leeds Cancer Centre, St James’s Institute of Oncology, Leeds Teaching Hospital NHS Trust, Leeds, UK;4. Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK;5. Division of Cancer Sciences, The University of Manchester, Manchester, UK;1. Leeds Institute of Medical Research at St James''s, University of Leeds, Leeds, UK;2. Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK;3. Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK;4. Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK;5. The Christie NHS Foundation Trust, Manchester, UK;7. The Royal Marsden Hospital, Sutton, UK;11. Northern Centre for Cancer Care, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Cumberland Infirmary, Carlisle, UK;12. Institute of Cancer Sciences, University of Glasgow, Glasgow, UK;8. Beatson West of Scotland Cancer Centre, Glasgow, UK;9. Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, UK;71. The University of Liverpool, Liverpool, UK;1. Leonard M. Miller School of Medicine, University of Miami, Miami, Florida, USA;2. University of Illinois College of Medicine, Chicago, Illinois, USA;3. Musculoskeletal Oncology Division, Miami Cancer Institute, Baptist Health South Florida, Miami, Florida, USA;1. Editorial Department of Chinese Journal of Cancer Research, Peking University Cancer Hospital & Institute, Beijing, China;2. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing, China;1. Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK;2. Department of Oncology, Weston Park Cancer Centre, Sheffield, UK |
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| Abstract: | AimsIntrahepatic 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 methodsIn 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.ResultsThe 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.ConclusionsRadiomics-based imaging biomarkers can potentially help to non-invasively discriminate between iCCA and HCC. |
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| Keywords: | Artificial intelligence Biomarkers Hepatocellular carcinoma Intrahepatic cholangiocarcinoma Machine learning Predictive medicine |
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