Reducing Bias in the Evaluation of Robotic Surgery for Lung Cancer Through Machine Learning
The application of machine learning to adjust for selection bias allowed for better control of confounding factors in the analysis of the effect of RAS on 90-day mortality. Our results suggest a potential benefit of robotic surgery compared to thoracotomy, although further studies are needed to confirm these findings.
Authors
- Jonathan Cottenet (ORCID: https://orcid.org/0009-0002-8501-3312)
- Pascale Tubert‐Bitter (ORCID: https://orcid.org/0000-0003-4942-7753)
- Alain Bernard (ORCID: https://orcid.org/0000-0002-5367-0097)
- Catherine Quantin (ORCID: https://orcid.org/0000-0001-5134-9411)
Institutions
- Inserm (FR)
- Université de Versailles Saint-Quentin-en-Yvelines (FR)
- Université Paris-Saclay (FR)
- Centre de recherche en Epidémiologie et Santé des Populations (FR)
- Maison des Sciences sociales et des Humanités de Dijon (FR)
- Institut National de Santé Publique (TN)
Publication Details
- Journal
- Cancers
- Published
- 2025-10-17
- DOI
- https://doi.org/10.3390/cancers17203347
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36