AI Improves Agreement and Reduces Time for Quantifying Metabolic Tumour Burden in Hodgkin Lymphoma
An automated AI-based tool can significantly increase agreement among specialists quantifying tMTV in HL patients staged with [18F]FDG PET/CT, without markedly changing the measurements.
Authors
- Babak Saboury (ORCID: https://orcid.org/0000-0002-8632-6292)
- Elin Trägårdh (ORCID: https://orcid.org/0000-0002-7116-303X)
- Olof Enqvist (ORCID: https://orcid.org/0000-0002-7456-1009)
- Johannes Ulén (ORCID: https://orcid.org/0000-0002-1542-7774)
- Jesus Lopez Urdaneta
- May Sadik (ORCID: https://orcid.org/0000-0001-9781-2264)
- Sally F. Barrington (ORCID: https://orcid.org/0000-0002-2516-5288)
- José Luis Loaiza Góngora
- Martijn van Essen (ORCID: https://orcid.org/0000-0002-2257-1651)
- Anne Lerberg Nielsen (ORCID: https://orcid.org/0000-0002-6130-9736)
- Lars Edenbrandt (ORCID: https://orcid.org/0000-0002-0263-8820)
- Annika Loft
- Rajender Kumar (ORCID: https://orcid.org/0000-0002-5817-2046)
Institutions
- University of Copenhagen (DK)
- King's College London (GB)
- Akershus University Hospital (NO)
- Sahlgrenska University Hospital (SE)
- Odense University Hospital (DK)
- Copenhagen University Hospital (DK)
- Rigshospitalet (DK)
- Skåne University Hospital (SE)
- National Institutes of Health Clinical Center (US)
- Post Graduate Institute of Medical Education and Research (IN)
- Chalmers University of Technology (SE)
Publication Details
- Journal
- Hematology Reports
- Published
- 2025-11-07
- DOI
- https://doi.org/10.3390/hematolrep17060060
- Primary Topic
- Lymphoma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00