MRI Imaging and Machine Learning Based Radiomics for Detection of Mixed HCC and CCA Tumors.
Authors
Affiliations (17)
Affiliations (17)
- Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China.
- Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
- Department of Radiology and Gastrohepatology, University of Antioquia, Medellin, Colombia.
- Department of Radiology, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany.
- Department of Radiology, University Hospital Regensburg, Regensburg, Germany.
- Department of Radiology and Nuclear Medicine, Hospital Braunschweig, Braunschweig, Germany.
- Department of Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health (CPDBW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Universidad Autónoma de San Luis Potosí, San Luis Potosi, Mexico.
- Instituto Nacional de Ciencias Médicas & Nutrición Salvador Zubiran, Mexico City, Mexico.
- Department of Oncology, Shanxi Province Fenyang Hospital, Fenyang, China.
- Department of Data Analysis and Modeling in Medicine, Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Department of Biomedical Informatics, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health (CPDBW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. Electronic address: [email protected].
Abstract
Primary liver cancer (PLC), comprising hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), is a leading cause of cancer mortality globally. The combined hepatocellular-cholangiocarcinoma (cHCC-CC) subtype may be less common but is relevant to treatment efficacy. We therefore evaluated the diagnostic accuracy of various approaches in distinguishing these liver cancers. Patients diagnosed with HCC, CCA, and cHCC-CC at Beijing University Cancer Hospital and Institute, China were included. Radiologists of varying expertise independently assessed MRI scans, and we measured their diagnostic consistency. Radiomic features were extracted from MRI scans, and machine learning was applied to differentiate the cancer types. Standard imaging was insufficient to reliably characterize cHCC-CC. Abdominal imaging experts (AIEs) had a higher mean sensitivity for HCC and CCA, 88% and 84% respectively, while non-experts (NIEs) had a lower sensitivity of 50% for HCC and 38% for CCA (HCC: p=0.03, CCA: p=0.008). Radiomic analysis found 'Sphericity' and 'ClusterShade' as the most relevant features. However, radiomics algorithms were also not sufficient to distinguish cHCC-CC from either HCC or CCA. Regarding sensitivity, the radiomic-based model was not better than radiologists for any of the three classes (p=0.065 for HCC, p=0.426 for CCA, and p=1.0 for cHCC-CC). The random forest algorithm yielded an accuracy of 76% in the test set, since it correctly classified most HCC and CCA, while only one quarter of cHCC-CC tumors. Histopathological analysis, complemented by imaging as indicated, remains essential for accurate detection, diagnosis, and treatment of liver cancers.