MetaPredictomics
In this study, we highlighted the value of maximizing the use of medical imaging for NSCLC recurrence prognostication by incorporating data from various organs, rather than focusing solely on the tumor and its immediate surroundings. This multisource integration proved particularly beneficial in the meta models, where combining clinicopathologic data with tumor radiomics and organomics models significantly enhanced recurrence prediction.
Authors
- Mehdi Amini (ORCID: https://orcid.org/0000-0002-4370-680X)
- Yazdan Salimi (ORCID: https://orcid.org/0000-0002-1233-9576)
- Habib Zaidi (ORCID: https://orcid.org/0000-0001-7559-5297)
- Ghasem Hajianfar (ORCID: https://orcid.org/0000-0001-5359-2407)
- Zahra Mansouri (ORCID: https://orcid.org/0000-0003-2087-9721)
Institutions
- Obuda University (HU)
- University Medical Center Groningen (NL)
- University of Groningen (NL)
- University of Southern Denmark (DK)
- University Hospital of Geneva (CH)
Publication Details
- Journal
- Clinical Nuclear Medicine
- Published
- 2025-09-03
- DOI
- https://doi.org/10.1097/rlu.0000000000006086
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00