Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm
Sybil's predictive performance for future lung cancer risk is robust across different reconstruction filters and axial slice thicknesses, demonstrating its versatility in various imaging settings. Combining favorable reconstruction parameters can significantly enhance predictive ability at years 2-4. The absence of significant differences between Siemens and Toshiba scanners further supports Sybil's versatility.
Authors
- Alexander Graur (ORCID: https://orcid.org/0000-0001-8149-461X)
- Florian J. Fintelmann (ORCID: https://orcid.org/0000-0002-0119-3903)
- Lecia V. Sequist (ORCID: https://orcid.org/0000-0002-8965-6991)
- Judit Simon (ORCID: https://orcid.org/0000-0001-9279-8627)
- Peter G. Mikhael (ORCID: https://orcid.org/0000-0002-6030-1636)
- Allison E.B. Chang (ORCID: https://orcid.org/0009-0009-3169-8337)
- Raymond U. Osarogiagbon
- Steven J. Skates
Institutions
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Investigative Radiology
- Published
- 2024-10-22
- DOI
- https://doi.org/10.1097/rli.0000000000001131
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Eli Lilly and Company
- Pfizer
- AstraZeneca
- Gilead Sciences
- Boston Scientific Corporation
- Genentech
- National Cancer Institute