Deep Learning Radiomics to Better Track Cancer Therapy Response
Official title: Deep Learning Serial Radiomics for Improved Cancer Therapy Response Assessment.
Funded at Onc.ai, INC. · NIH-11384900 · Data updated 2026-11-12
A small grant will help create an automated deep-learning tool that uses CT scans to predict how well metastatic cancer patients will respond to treatment earlier in the trial.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Onc.ai, INC. NIH-funded |
| Lab location | 1 site (San Carlos, United States) |
| Project start | August 5, 2025 |
| Project end | July 31, 2027 |
| Funding year | 2026 |
| Award (this year) | $313,742 |
| NIH project number | 5R44CA302212-02 |
| Project ID | NIH-11384900 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with metastatic cancer who participate in CT-imaging-based therapy trials and have baseline and follow-up scans.
✕Less likely to help
Patients not enrolled in imaging-based trials or without follow-up CT data may not benefit.
What this research studies
This SBIR project aims to build and validate a deep learning radiomics biomarker that uses baseline and follow-up CT scans to produce a continuous score predicting overall survival, supplementing current RECIST criteria. The approach learns from entire CT images, including areas outside visible tumors, to capture subtle response signals. Researchers will train and test the model on existing imaging data and compare its predictions to standard RECIST-based assessments. If successful, the tool could give faster, more accurate signals about whether a therapy is working in metastatic cancer trials.
Why it matters
Potential benefit: If successful, patients could benefit from earlier and more accurate decisions about their treatment plans in cancer trials.
How similar studies have gone: Previous work suggests radiomics and deep learning can improve imaging-based biomarkers, but validation in prospective trials is ongoing.
Where this research is happening
-
Onc.ai, INC.
San Carlos, United States Active
Researchers
- Principal investigator: Schmidt, Taly Gilat — Onc.ai, INC.
- Study coordinator: Schmidt, Taly Gilat
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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.