AI tools to organize and predict immune-related side effects in cancer therapy
Official title: AI-Driven Curation and Analysis of Immune-Related Adverse Events in Cancer Immunotherapy Using Secondary Immune-Oncology Databases
Funded at Georgetown University · NIH-11454834 · Data updated 2026-11-12
A concrete plan to build AI-powered tools that automatically pull together information about immune-related side effects from cancer immunotherapy to help doctors manage treatment.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Georgetown University NIH-funded |
| Lab location | 1 site (Washington, United States) |
| Project start | July 8, 2026 |
| Project end | June 30, 2028 |
| Funding year | 2026 |
| Award (this year) | $412,167 |
| NIH project number | 1R21CA309407-01A1 |
| Project ID | NIH-11454834 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with cancer receiving immune checkpoint inhibitors whose adverse events are or could be recorded in the participating registries.
✕Less likely to help
Patients not treated with ICIs or not included in the registries may not directly benefit.
What this research studies
This project uses AI to extract and harmonize adverse event data from cancer patients treated with immune checkpoint inhibitors. It combines NLP and machine learning with existing registries to create a usable data resource and a predictive model to flag patients at risk earlier in therapy. The work aims to improve how irAEs are tracked and managed so patients can stay on effective treatments. Researchers will partner with Georgetown University’s IO registry and external data from Hackensack Meridian Health to develop and test the tools. The end goal is a practical data platform that clinicians can use to guide safer, more personalized immunotherapy.
Why it matters
Potential benefit: If successful, patients could experience fewer treatment disruptions due to adverse events and receive more tailored immunotherapy.
How similar studies have gone: Previous AI and NLP efforts have shown promise in automating clinical data extraction and risk prediction in oncology, but integration into real-world irAE management is still emerging.
Where this research is happening
-
Georgetown University
Washington, United States Active
Researchers
- Principal investigator: Zhang, Yili — Georgetown University
- Study coordinator: Zhang, Yili
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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.