Using AI to improve risk assessment for acute myeloid leukemia
Official title: AI-based AML risk stratification using next generation cytogenomics
Funded at Phase Genomics, INC. · NIH-10862769 · Data updated 2026-11-12
This study is looking at how certain hidden changes in chromosomes can help us better understand acute myeloid leukemia (AML) and improve predictions about how patients will do, especially for those who are considered at intermediate risk, so that doctors can tailor treatments more effectively.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Phase Genomics, INC. NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project start | July 1, 2023 |
| Project end | June 30, 2025 |
| Funding year | 2024 |
| Award (this year) | $1,000,000 |
| NIH project number | 5R44CA278140-02 |
| Project ID | NIH-10862769 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients diagnosed with acute myeloid leukemia, particularly those with intermediate risk profiles.
✕Less likely to help
Patients with other types of leukemia or those without chromosomal abnormalities may not benefit from this research.
What this research studies
This research focuses on enhancing the understanding of acute myeloid leukemia (AML) by analyzing chromosomal abnormalities that are often overlooked in traditional assessments. By employing advanced machine learning techniques, the project aims to develop a new risk-prediction metric that can provide clearer insights into patient prognosis, particularly for those classified as having intermediate risk. The approach involves creating a comprehensive dataset of cytogenomic information to identify patterns that correlate with AML outcomes, ultimately guiding more personalized treatment decisions for patients.
Why it matters
Potential benefit: If successful, this research could lead to more accurate risk assessments for AML patients, improving treatment strategies and outcomes.
How similar studies have gone: Previous studies have shown promise in using machine learning for cancer risk assessment, indicating that this approach could yield significant advancements in AML prognosis.
Where this research is happening
-
Phase Genomics, INC.
Seattle, United States Active
Researchers
- Principal investigator: Eacker, Stephen Matthew — Phase Genomics, INC.
- Study coordinator: Eacker, Stephen Matthew
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.