Survival outcomes and prognostic factors in spindle cell variants of squamous cell carcinoma: a machine learning analysis of 1086 patients from the SEER database
The findings suggest that anatomical site and disease stage significantly influence survival, while current treatment modalities show limited impact on outcomes. The superior performance of the RSF model indicates potential value in using machine learning for risk stratification in clinical practice.
Authors
- Marwan Alaswad (ORCID: https://orcid.org/0009-0004-2992-8037)
- Tarek Ziad Arabi (ORCID: https://orcid.org/0000-0002-6292-7683)
- Nida Mariyam
- Akef S. Obeidat (ORCID: https://orcid.org/0000-0002-4666-5118)
- Belal Nedal Sabbah (ORCID: https://orcid.org/0000-0001-9131-7430)
- Mohammed Hady Albitar
Institutions
- Alfaisal University (SA)
Publication Details
- Journal
- Annals of Medicine and Surgery
- Published
- 2025-06-25
- DOI
- https://doi.org/10.1097/ms9.0000000000003482
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36