Diagnostic accuracy of texture analysis applied to T1- and T2-Relaxation maps for liver fibrosis classification via machine-learning algorithms with liver histology as reference standard
TA-T<sub>1</sub> performed comparably to MRE in liver fibrosis classification to nmF and sF groups, and even though not statistically significant, combining those with MRE increased the performance, suggesting their complementary nature. Given the broad availability, robustness and short scanning times of T<sub>1</sub> mapping, we would advocate for the inclusion of T<sub>1</sub> mapping in every clinical and research liver examination.
Authors
- Siegfried Trattnig (ORCID: https://orcid.org/0000-0003-1623-3303)
- Martin Krššák (ORCID: https://orcid.org/0000-0001-9717-803X)
- Nina Bastati (ORCID: https://orcid.org/0000-0002-3994-6552)
- Sarah Poetter‐Lang (ORCID: https://orcid.org/0000-0002-8843-0958)
- Ahmed Ba‐Ssalamah (ORCID: https://orcid.org/0000-0002-3527-404X)
- Diana Sitarcikova (ORCID: https://orcid.org/0000-0002-8464-801X)
- Ulrike Attenberger
- Sami A Ba-Ssalamah
Institutions
- Medical University of Vienna (AT)
Publication Details
- Journal
- European Journal of Radiology
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1016/j.ejrad.2024.111887
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- Austrian Science Fund
- Christian Doppler Forschungsgesellschaft