Targeting minimal residual disease in AML with smart imaging and AI
Official title: Targeting minimal residual disease in AML by using single-cell morphological and biophysical analysis with deep learning
Funded at Massachusetts Institute of Technology · NIH-11404887 · Data updated 2026-11-12
A patient-centered project to find better ways to treat residual leukemia cells after remission using advanced imaging and AI together with precise measurement tools.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts Institute of Technology NIH-funded |
| Lab location | 1 site (Cambridge, United States) |
| Project start | September 9, 2024 |
| Project end | August 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $630,475 |
| NIH project number | 5U01CA282163-03 |
| Project ID | NIH-11404887 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with acute myeloid leukemia who have achieved remission but still have measurable residual disease.
✕Less likely to help
Patients without measurable residual disease at remission, or those unable to tolerate targeted therapies studied in this project.
What this research studies
This project aims to identify and target the small population of leukemia cells that remain after initial treatment, using a combination of high-content label-free imaging, AI-driven sorting, and precise biophysical measurements. By analyzing cells at the time of complete remission, researchers hope to predict how well a therapy will work and uncover hidden differences that could drive relapse. The team brings together experts from Deepcell, MIT, and Dana-Farber to create a real-time functional test of MRD vulnerabilities that could guide personalized therapy choices. If successful, this platform could help prevent relapse and spare patients from ineffective treatments.
Why it matters
Potential benefit: Could lead to more effective, personalized treatment plans that reduce relapse risk for AML patients.
How similar studies have gone: Prior work shows that analyzing residual disease can guide therapy, but this project aims to combine imaging, AI, and biophysical assays in real time for actionable insights.
Where this research is happening
-
Massachusetts Institute of Technology
Cambridge, United States Active
Researchers
- Principal investigator: Manalis, Scott R — Massachusetts Institute of Technology
- Study coordinator: Manalis, Scott R
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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.