LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning
Deep learning-based HCC risk assessment according to LI-RADS can be implemented as automatically generated tumor risk maps using out-of-the-box image segmentation tools with high detection performance for LR-5 lesions. Before translation into clinical practice, further improvements in automatic LI-RADS classification, for example through large multi-center studies, would be desirable.
Authors
- Róbert Stollmayer (ORCID: https://orcid.org/0000-0003-4673-7588)
- Hans‐Ulrich Kauczor (ORCID: https://orcid.org/0000-0002-6730-9462)
- Pál Novák Kaposi (ORCID: https://orcid.org/0000-0002-7150-3495)
- Kai Schlamp
- Selda Güven (ORCID: https://orcid.org/0000-0002-6552-9947)
- Philipp Mayer (ORCID: https://orcid.org/0000-0003-1957-1594)
- Christian M. Heidt (ORCID: https://orcid.org/0009-0007-5597-7555)
- Miriam Klauß
- Oyunbileg von Stackelberg
Institutions
- Semmelweis University (HU)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Sağlık Bilimleri Üniversitesi (TR)
- Dışkapı Yıldırım Beyazıt Eğitim ve Araştırma Hastanesi (TR)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1186/s40644-025-00844-6
- Citations
- 5
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 6.61