AI-guided personalized PSMA radiotherapy for prostate cancer
Deep Learning-Based Treatment Planning for PSMA RPT
This project uses artificial intelligence and routine PET/SPECT images to tailor PSMA-targeted radiopharmaceutical doses for men with advanced prostate cancer.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project ID | NIH-11252311 on NIH RePORTER |
What this research studies
If you have prostate cancer that concentrates PSMA, researchers will use your pre-treatment PSMA-PET scans and post-therapy SPECT/CT images to map where the drug travels in the body. A deep learning model will be trained on these imaging patterns and past dosimetry to predict how much radiation different organs and tumors receive. That prediction would be used to recommend a personalized treatment dose instead of the current one-size-fits-all regimen. The goal is to use noninvasive imaging you already get to make each treatment safer and more effective.
Who could benefit from this research
Good fit: Ideal candidates are men with PSMA-avid metastatic prostate cancer who are eligible for or receiving 177Lu-PSMA radiopharmaceutical therapy and can undergo PET and SPECT/CT imaging.
Not a fit: People without PSMA-avid tumors or those not receiving PSMA-targeted radiopharmaceutical therapy would not benefit from this approach.
Why it matters
Potential benefit: If successful, this could lead to more accurate dosing that increases tumor radiation while lowering risk to healthy organs.
How similar studies have performed: Previous work has shown that image-guided dosimetry and early AI tools can predict radiotracer distribution, but fully AI-driven personalized dosing for PSMA RPT is still a new and emerging approach.
Where this research is happening
New Haven, United States
- Yale University — New Haven, United States (Active)
Researchers
- Principal investigator: El Fakhri, Georges — Yale University
- Study coordinator: El Fakhri, Georges
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 below.