MRI imaging and machine learning based radiomics for detection of mixed HCC and CCA tumors: Still a need for liver biopsy?
Abstract
531 Background: 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. Methods: 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. Results: 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. Conclusions: Until improved diagnostic tools are available, biopsy of liver cancer remains critical to the detection, diagnosis, and effective treatment of these cancers.
Article Details
Journal Info
Journal of Clinical Oncology
Lippincott Williams & Wilkins
Authors (20)
Yuquan Qian
National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
Qiaoyuan Lu
Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
Isaac Rodriguez
Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
Michael Vácha
Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
Xiangde Min
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
Muzaffer Reha Ümütlü
Department of Radiology, University Hospital, LMU Munich, Munich, Germany
German A Castrillon
Department of Radiology and Gastrohepatology, University of Antioquia, Medellin, Colombia
Andreas Schreyer
Department of Radiology, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany, Brandenburg an Der Havel, Germany
Michael Haimerl
Stefan O. Schoenberg
Matthias Philip Ebert
Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
Abhinay Vellala
Carlos Romero Alaffita
Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico
Juan Alberto Garay Mora
Instituto Nacional de Ciencias Médicas & Nutrición Salvador Zubiran, Mexico City, Mexico
Zhiqiang Guo
Jürgen Hesser
Department of Data Analysis and Modeling in Medicine, Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
Christel Weiß
Matthias Froelich
Yingshi Sun
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
Andreas Teufel
Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany