Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free‐breathing 4D‐CT
Combined tumor respiratory motion and radiomics features from planning CT showed promising predictive value for 2-year tumor control and survival, indicating the potential need for improving motion management strategies in future studies using machine learning-based prognosis models.
Authors
- Samuel Kadoury (ORCID: https://orcid.org/0000-0002-3048-4291)
- Marion Tonneau (ORCID: https://orcid.org/0000-0003-4776-115X)
- Houda Bahig (ORCID: https://orcid.org/0000-0001-9508-0602)
- Édith Filion
- Emilie Ouraou
- William Le (ORCID: https://orcid.org/0000-0002-6106-7386)
- Marie‐Pierre Campeau
- Toni Vu
- Robert Doucet
Institutions
- Centre Hospitalier de l’Université de Montréal (CA)
- Polytechnique Montréal (CA)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1002/mp.17586
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- Varian Medical Systems