Predefined and data-driven CT radiomics predict recurrence-free and overall survival in patients with pulmonary metastases treated with stereotactic body radiotherapy
In conclusion, concise predictive models including CT density-radiomics of metastases, age, clinical indication, and lung primary cancer origin can help identify those patients with probable earlier recurrence or death prior to SBRT treatment so that more aggressive treatment can be applied.
Authors
- Pascal Salazar (ORCID: https://orcid.org/0000-0002-7779-2603)
- Anastasia Oikonomou (ORCID: https://orcid.org/0000-0001-6996-237X)
- Balaji Ganeshan (ORCID: https://orcid.org/0000-0002-1386-0531)
- Patrick Cheung
Institutions
- Sunnybrook Health Science Centre (CA)
- University of Toronto (CA)
- Health Sciences Centre (CA)
- Canon (United States) (US)
- Medica (United States) (US)
- University College London (GB)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-12-31
- DOI
- https://doi.org/10.1371/journal.pone.0311910
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81