40P Prognostic value of total liver tumor volume versus sum of diameters: AI-Driven analysis in 362 GEP-NET patients with liver metastases
Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are rare malignancies characterized by rather slow growth and heterogeneous treatment responses. Traditional methods like RECIST face limitations, including measurement variability, inconsistent lesion selection, and failure to account for total tumor burden, complicating accurate treatment outcome assessment. AI-driven volumetry provides a promising solution for new response criteria that can achieve greater accuracy and reliability in determining treatment efficacy.
Authors
- Margot Tesselaar (ORCID: https://orcid.org/0000-0002-8885-4638)
- J. Engel (ORCID: https://orcid.org/0000-0002-7384-2052)
- Ivaïlo M. Mladenov (ORCID: https://orcid.org/0000-0001-5577-5741)
- Paul Mugisha
- Kalina Chupetlovska (ORCID: https://orcid.org/0000-0002-1183-7426)
- G. Grassi
- Geerard L. Beets (ORCID: https://orcid.org/0000-0002-1671-9912)
- Marissa Maas (ORCID: https://orcid.org/0000-0001-9677-9917)
- Stefano Trebeschi (ORCID: https://orcid.org/0000-0002-5714-289X)
Institutions
- The Netherlands Cancer Institute (NL)
Publication Details
- Journal
- ESMO Open
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1016/j.esmoop.2025.104351
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00