Novel imaging predictors in pediatric lymphoma: radiomics and artificial intelligence. A systematic review
AI and radiomics in PL are still in their early stages but show great promise in automatically extracting important diagnostic parameters, such as the tumor volume, and in delivering sharp diagnostic images with a significant dose reduction to the patient.
Authors
- Giorgio Treglia (ORCID: https://orcid.org/0000-0001-9808-780X)
- Gianluca Bottoni (ORCID: https://orcid.org/0000-0003-4150-0266)
- Lars Kurch (ORCID: https://orcid.org/0000-0002-3396-4880)
- Lise Borgwardt (ORCID: https://orcid.org/0000-0002-1000-3534)
- Teresa Battaglia (ORCID: https://orcid.org/0009-0001-8333-8810)
- Virginia Livellara
- Arnoldo Piccardo (ORCID: https://orcid.org/0000-0002-6779-1471)
- Alberto Garaventa (ORCID: https://orcid.org/0000-0002-5368-6363)
- Francesco Fiz (ORCID: https://orcid.org/0000-0003-3932-1330)
Institutions
- Istituto Giannina Gaslini (IT)
- Copenhagen University Hospital (DK)
- Ente Ospedaliero Ospedali Galliera (IT)
- Ente Ospedaliero Cantonale (CH)
- University Hospital Leipzig (DE)
- University Hospital in Halle (DE)
- Istituto Imaging della Svizzera Italiana
- Università della Svizzera italiana (CH)
- University of Lausanne (CH)
Publication Details
- Journal
- The Quarterly Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2025-11-11
- DOI
- https://doi.org/10.23736/s1824-4785.25.03648-9
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36