Tumor burden in metastatic colorectal cancer quantified using deep learning models: Prognostic value and maintenance treatment benefit in the CAIRO3 trial
This exploratory analysis showed that a higher tumor burden, quantified by number or volume, was independently associated with poor outcomes. Patients with lower tumor burden, especially lower TTV, appeared to derive the greatest benefit from maintenance therapy, supporting their potential as predictive biomarkers.
Authors
- Inez M. Verpalen (ORCID: https://orcid.org/0000-0002-3267-0637)
- M. N. G. Braat
- Koen Zwart (ORCID: https://orcid.org/0000-0002-0369-4509)
- J Lagendijk
- Pim de Jong
- Anne M. May (ORCID: https://orcid.org/0000-0003-4857-8189)
- Ruby Kemna (ORCID: https://orcid.org/0000-0003-0762-5778)
- Guus Martinus Bol (ORCID: https://orcid.org/0000-0002-4021-3823)
- Sophie A. Kurk (ORCID: https://orcid.org/0000-0001-8948-5937)
- Bertine L. Stehouwer
- Michiel J. Zeeuw
- Rutger-Jan Swijnenburg
- Dania Al-Toma
- Mahsoem Ali
- Cornelis J.A. Punt
- Geert Kazemier
- Miriam Koopman
Institutions
- Utrecht University (NL)
- University Medical Center Utrecht (NL)
- ZorgKeuzeLab (NL)
- Amsterdam University Medical Centers (NL)
- University of Amsterdam (NL)
- Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
Publication Details
- Journal
- European Journal of Cancer
- Published
- 2026-03-07
- DOI
- https://doi.org/10.1016/j.ejca.2026.116668
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00