A novel computational pathology approach for identifying gene signatures prognostic of disease-free survival for papillary thyroid carcinomas
There was a 17.8% improvement in the C-index (from 0.605 to 0.783) for 123 cPTCs and 15% (from 0.576 to 0.726) for 38 fvPTCs compared to the standalone gene-expression signature. Hazard ratios also improved for cPTCs from 0.89 (0.67,0.99) to 4.43 (3.65,6.68) and fvPTC from 0.98 (0.76,1.32) to 2.28 (1.87,3.64). We validated the image-based risk model on an independent cohort of 32 cPTCs with hazard ratio 1.8 (1.534,2.167).
Authors
- Anant Madabhushi (ORCID: https://orcid.org/0000-0002-5741-0399)
- Qiuying Shi (ORCID: https://orcid.org/0000-0002-0224-3167)
- Sirvan Khalighi (ORCID: https://orcid.org/0000-0002-5563-1446)
- Shayan Monabbati (ORCID: https://orcid.org/0000-0003-0719-4007)
- Pingfu Fu (ORCID: https://orcid.org/0000-0002-2334-5218)
- L. Sylvia (ORCID: https://orcid.org/0000-0001-8418-5054)
Institutions
- University Hospitals of Cleveland (US)
- Emory University Hospital Midtown (US)
- The Wallace H. Coulter Department of Biomedical Engineering (US)
- Case Western Reserve University (US)
Publication Details
- Journal
- European Journal of Cancer
- Published
- 2024-09-17
- DOI
- https://doi.org/10.1016/j.ejca.2024.114326
- Citations
- 2
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.47
Funders
- National Heart, Lung, and Blood Institute
- National Cancer Institute