Quantitative and Morphology-Based Deep Convolutional Neural Network Approaches for Osteosarcoma Survival Prediction in the Neoadjuvant and Metastatic Settings
Neural networks can be used to automatically estimate the necrosis to tumor ratio, a quantitative metric predictive of survival. Furthermore, we identified alternate histomorphologic biomarkers specific to the necrotic and tumor regions, which could serve as predictors.
Authors
- Ke Yuan (ORCID: https://orcid.org/0000-0002-2318-1460)
- Aristotelis Tsirigos (ORCID: https://orcid.org/0000-0002-7512-8477)
- Nicolas Coudray (ORCID: https://orcid.org/0000-0002-6050-2219)
- Michael A. Occidental (ORCID: https://orcid.org/0000-0002-0314-9439)
- George Jour (ORCID: https://orcid.org/0000-0001-8916-8966)
- José G. Mantilla (ORCID: https://orcid.org/0000-0003-4752-6459)
- Ján Balko (ORCID: https://orcid.org/0000-0003-0451-2786)
- Adalberto Claudio Quiros (ORCID: https://orcid.org/0000-0003-4804-0741)
Institutions
- Charles University (CZ)
- NYU Langone Health (US)
- University Hospital in Motol (CZ)
- Precision for Medicine (United States) (US)
- Foster Care Associates Scotland (GB)
- New York University (US)
- University of Glasgow (GB)
Publication Details
- Journal
- Clinical Cancer Research
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1158/1078-0432.ccr-24-2599
- Citations
- 2
- Primary Topic
- Sarcoma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.84