Radiomic approach to support multidisciplinary tumor board decision-making in locally advanced non-small cell lung cancer
ML models combining clinicopathological and radiomic features can reproduce MTB treatment recommendations for LA-NSCLC with good accuracy. This approach may provide decision in settings with limited MTB expertise and promote more consistent treatment allocation.
Authors
- Harel Kotler
- Marco Schiavon (ORCID: https://orcid.org/0000-0001-8780-2011)
- Francesca Caumo (ORCID: https://orcid.org/0000-0002-9457-5176)
- Valentina Guarneri (ORCID: https://orcid.org/0000-0002-2375-8397)
- Fabio Aiolli (ORCID: https://orcid.org/0000-0002-5823-7540)
- Luca Bergamin (ORCID: https://orcid.org/0000-0002-0662-7862)
- M. Nuzzo (ORCID: https://orcid.org/0000-0003-2477-5196)
- Angela Grassi (ORCID: https://orcid.org/0000-0002-8313-8384)
- Alessandra Ferro (ORCID: https://orcid.org/0000-0002-1163-3765)
- Gisella Gennaro (ORCID: https://orcid.org/0000-0003-2444-1778)
- Marco Krengli (ORCID: https://orcid.org/0000-0002-2064-8379)
- Elena Scagliori
- Matteo Sepulcri (ORCID: https://orcid.org/0000-0002-9654-5587)
- Giulia Pasello (ORCID: https://orcid.org/0000-0002-8741-6038)
Institutions
- University of Padua (IT)
- Istituto Oncologico Veneto (IT)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-12-19
- DOI
- https://doi.org/10.3389/fonc.2025.1713847
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00