Advanced tissue imaging and AI to improve prostate cancer outcome predictions
Multimodality spatial analysis in prostate cancer to improve prognostic estimation and cast light into the black box of pathology artificial intelligence algorithms
This project combines high-resolution protein and gene mapping of prostate tumors with artificial intelligence to help predict which prostate cancers are likely to be aggressive and which may be less risky.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project ID | NIH-11251992 on NIH RePORTER |
What this research studies
If you have prostate cancer, researchers will use tissue from tumors to map proteins and RNA at very small (micron) scales and run those maps alongside AI analysis of standard pathology images. The team will combine two cutting-edge spatial proteogenomic technologies with pathology AI to find biological patterns that drive the AI results. They will look for new markers tied to outcomes after treatment and test whether those markers explain or improve AI-based predictions. The work aims to reveal tumor heterogeneity that current genomic tests and AI tools do not fully capture.
Who could benefit from this research
Good fit: Ideal candidates are men diagnosed with prostate cancer who have tumor tissue from biopsy or surgery and are willing to let researchers use their samples and medical data.
Not a fit: Patients without available tumor tissue, those with cancers unrelated to the prostate, or those not willing to share tissue or data are unlikely to benefit directly from participating.
Why it matters
Potential benefit: If successful, this work could lead to more accurate, biology-informed predictions of cancer aggressiveness to guide more personalized treatment decisions.
How similar studies have performed: Existing RNA-based genomic tests and early pathology-AI tools have provided useful prognostic information, but combining spatial proteogenomics with AI to explain and extend AI findings is a novel approach.
Where this research is happening
San Francisco, United States
- University of California, San Francisco — San Francisco, United States (Active)
Researchers
- Principal investigator: Cooperberg, Matthew R — University of California, San Francisco
- Study coordinator: Cooperberg, Matthew R
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.