Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach
The combination of radiomics features from pre-treatment MRI with clinical parameters and AFP showed fair performance for predicting HCC response to radiation segmentectomy, better than that of AFP. These results need further validation.
Authors
- Octavia Bane (ORCID: https://orcid.org/0000-0003-1315-5848)
- Brett Marinelli (ORCID: https://orcid.org/0000-0003-3220-1360)
- Daniel Stocker (ORCID: https://orcid.org/0000-0002-1230-092X)
- Miriam Hulkower
- Guillermo Carbonell (ORCID: https://orcid.org/0000-0001-6780-6883)
- Sara Lewis (ORCID: https://orcid.org/0000-0002-4713-6453)
- Stefanie J. Hectors (ORCID: https://orcid.org/0000-0001-9457-6606)
- Bachir A. Taouli (ORCID: https://orcid.org/0000-0001-6409-1333)
- Pei Wang (ORCID: https://orcid.org/0000-0002-8149-756X)
- Edward Kim (ORCID: https://orcid.org/0000-0001-8050-2366)
- Weiping Ma (ORCID: https://orcid.org/0000-0003-3786-7460)
- Paul Kennedy (ORCID: https://orcid.org/0000-0003-4295-3670)
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- University Hospital of Zurich (CH)
- Hospital Universitario Virgen de la Arrixaca (ES)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Abdominal Radiology
- Published
- 2024-10-26
- DOI
- https://doi.org/10.1007/s00261-024-04606-z
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- Universität Zürich