Noninvasive Tumor Profiling: Quantitative Contrast-enhanced MRI Markers Help Predict PD-L1 and CTNNB1 Status in Hepatocellular Carcinoma
Quantitative multiparametric contrast-enhanced MRI parameters helped predict molecular profiles of hepatocellular carcinoma via machine learning algorithms.
Authors
- Edward A. Meister
- Xuchen Zhang (ORCID: https://orcid.org/0000-0002-1484-4672)
- Khaled Bousabarah (ORCID: https://orcid.org/0009-0004-7699-9890)
- Julius Chapiro (ORCID: https://orcid.org/0000-0002-1984-2246)
- David C. Madoff (ORCID: https://orcid.org/0000-0003-3461-437X)
- Mario Strazzabosco (ORCID: https://orcid.org/0000-0002-1995-6331)
- Ramesh Batra (ORCID: https://orcid.org/0000-0003-2425-9028)
- MingDe Lin (ORCID: https://orcid.org/0000-0001-7641-2595)
- James S. Duncan (ORCID: https://orcid.org/0000-0002-5167-9856)
- Bernhard Gebauer (ORCID: https://orcid.org/0000-0002-9948-7220)
- Tal Zeevi (ORCID: https://orcid.org/0000-0001-8917-7061)
- Lynn Jeanette Savic (ORCID: https://orcid.org/0000-0003-2115-6728)
- Jonathan Tefera (ORCID: https://orcid.org/0000-0001-9038-8579)
- Margarita V. Revzin (ORCID: https://orcid.org/0000-0002-4118-2475)
- Moritz Gross (ORCID: https://orcid.org/0000-0002-6831-5004)
- Annabella Shewarega (ORCID: https://orcid.org/0000-0003-0663-3047)
- Victor Kasolowsky
- Rabea Sobirey (ORCID: https://orcid.org/0000-0003-3418-8921)
- Nickolai J. Matuschewski
- Tabea Kao (ORCID: https://orcid.org/0009-0005-0358-2127)
Institutions
- New Haven Public Schools (US)
- Yale University (US)
- Humboldt-Universität zu Berlin (DE)
- University of New Haven (US)
- Sage Technologies (United States) (US)
Publication Details
- Journal
- Radiology
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1148/radiol.242750
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08