Using advanced imaging and AI to target leftover cancer cells in acute myeloid leukemia
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-10878458 · Data updated 2026-11-12
This study is looking to help people with acute myeloid leukemia (AML) by finding and targeting any leftover cancer cells after their first round of chemotherapy, using advanced imaging and smart technology to create personalized treatment plans that could stop the cancer from coming back.
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 | 2024 |
| Award (this year) | $647,458 |
| NIH project number | 1U01CA282163-01A1 |
| Project ID | NIH-10878458 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients who have achieved complete remission from AML but still have detectable minimal residual disease.
✕Less likely to help
Patients who are in active relapse or those without detectable minimal residual disease may not benefit from this research.
What this research studies
This research focuses on improving treatment for patients with acute myeloid leukemia (AML) by identifying and targeting minimal residual disease (MRD) after initial chemotherapy. The approach combines advanced imaging techniques and artificial intelligence to analyze the characteristics of remaining cancer cells, allowing for a more tailored treatment strategy before relapse occurs. By understanding the unique features of MRD, the research aims to select the most effective therapies for patients, potentially preventing relapse and improving outcomes. The study will leverage resources from leading institutions to develop a novel platform for real-time assessment of therapeutic vulnerabilities in AML.
Why it matters
Potential benefit: If successful, this research could lead to more effective treatments for AML patients, reducing the risk of relapse and improving long-term survival.
How similar studies have gone: Other research has shown promise in using advanced imaging and AI techniques for cancer treatment, indicating that this approach could be a significant advancement in AML management.
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
Related research
Other NIH-funded projects on the same topics.
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Targeting minimal residual disease in AML with smart imaging and AI
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.
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Spotting and targeting leftover AML cells with AI and single-cell tools
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Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
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Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
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Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
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.