A novel machine learning-driven immunogenic cell death signature for predicting ovarian cancer prognosis
The ICDRS presents a significant advancement for prognostication of patients with OC, facilitating refined predictions and the exploration of personalized treatment pathways. Prospective clinical trials are necessary to validate its clinical utility and expand the application of this model to other cancer types.
Authors
- Gabriel Levin (ORCID: https://orcid.org/0000-0003-1282-5379)
- Felipe Batalini (ORCID: https://orcid.org/0000-0003-3354-308X)
- Tingting Gao (ORCID: https://orcid.org/0000-0002-2657-9124)
- Yali Wang (ORCID: https://orcid.org/0000-0002-2948-3400)
- Hao Chen (ORCID: https://orcid.org/0000-0002-6282-0237)
- Xude Sun
- Hooman Soleymani Majd (ORCID: https://orcid.org/0000-0002-7603-9986)
Institutions
- Hadassah Medical Center (IL)
- Churchill Hospital (GB)
- Jewish General Hospital (CA)
- People 's Liberation Army 451 Hospital (CN)
- Xi'an Medical University (CN)
- Mayo Clinic in Arizona (US)
- Fuzhou Maternity and Child Health Care Hospital (CN)
- Mayo Clinic Hospital (US)
- Oxford University Hospitals NHS Trust (GB)
- First Hospital of Xi'an (CN)
- McGill University (CA)
Publication Details
- Journal
- Translational Cancer Research
- Published
- 2025-02-01
- DOI
- https://doi.org/10.21037/tcr-2025-118
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00