Eco-Evolutionary Guided Pathomic Analysis Detects Biomarkers to Predict Ductal Carcinoma <i>In Situ</i> Upstaging
Evolutionary dynamics of the various niches composing the tumor ecosystem can be harnessed for predicting cancer progression, demonstrating how eco-evolutionary-designed approaches can guide biomarkers discovery studies in the era of digital pathology. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.
Authors
- Mehdi Damaghi (ORCID: https://orcid.org/0000-0002-7744-6161)
- Andrew Chen
- Prateek Prasanna (ORCID: https://orcid.org/0000-0002-3068-3573)
- Ji Dong K. Bai (ORCID: https://orcid.org/0000-0001-5543-7427)
- Joseph Johnson (ORCID: https://orcid.org/0000-0001-8574-6909)
- Manal Elmasry (ORCID: https://orcid.org/0000-0001-8125-5790)
- Chao Chen (ORCID: https://orcid.org/0000-0003-1703-6483)
- Yuzhu Chen
- Yujie Xiao (ORCID: https://orcid.org/0000-0003-3179-4387)
- Brooke Jackson
Institutions
- Mansoura University (EG)
- Moffitt Cancer Center (US)
- Stony Brook Medicine (US)
- Case Western Reserve University (US)
- Stony Brook University (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1158/0008-5472.can-24-2070
- Citations
- 4
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 3.75
Funders
- Moffitt Cancer Center
- National Institutes of Health
- National Cancer Institute
- National Institute of General Medical Sciences