MRI imaging and machine learning based radiomics for detection of mixed HCC and CCA tumors: Still a need for liver biopsy?
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.
Authors
- Christel Weiß (ORCID: https://orcid.org/0000-0002-8692-5690)
- Juan Alberto Garay Mora (ORCID: https://orcid.org/0000-0002-3758-5087)
- Yuquan Qian (ORCID: https://orcid.org/0000-0003-2721-700X)
- Germán Alberto Castrillón (ORCID: https://orcid.org/0000-0002-4575-6245)
- Michael Haimerl
- A. Schreyer (ORCID: https://orcid.org/0000-0002-2539-059X)
- Xiangde Min (ORCID: https://orcid.org/0000-0002-1589-6861)
- Matthias F. Froelich (ORCID: https://orcid.org/0000-0001-8501-2147)
- I. Rodrı́guez (ORCID: https://orcid.org/0000-0002-9693-2042)
- Muzaffer Reha Ümütlü (ORCID: https://orcid.org/0000-0002-8168-1500)
- Madeleine Vacha
- Ying‐Shi Sun (ORCID: https://orcid.org/0000-0001-9424-1910)
- Jürgen Hesser (ORCID: https://orcid.org/0000-0002-4001-1164)
- Andreas Teufel (ORCID: https://orcid.org/0000-0001-7953-8927)
- Abhinay Vellala
- Carlos Romero Alaffita
- Qiao-Yuan Lu
- Matthias Ebert (ORCID: https://orcid.org/0000-0001-5713-5465)
- Zhiqiang Guo (ORCID: https://orcid.org/0000-0002-3690-287X)
- Stefan Oswald Schoenberg (ORCID: https://orcid.org/0000-0002-3115-8367)
Institutions
- Autonomous University of San Luis Potosí (MX)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- LMU Klinikum (DE)
- University Hospital Regensburg (DE)
- Universidad de Antioquia (CO)
- Peking University Cancer Hospital (CN)
- Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (MX)
- Shanxi Fenyang Hospital (CN)
- University Medical Centre Mannheim (DE)
- Universitätsklinikum Brandenburg an der Havel (DE)
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Journal of Clinical Oncology
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1200/jco.2025.43.4_suppl.531
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Chinesisch-Deutsche Zentrum für Wissenschaftsförderung