Deep learning MRI biomarkers to predict prostate cancer outcomes
Official title: Studying the Utility of Deep Learning Derived MRI Imaging Biomarkers for Localized Prostate Cancer
Funded at Yale University · NIH-11449993 · Data updated 2026-11-12
A Yale-led project uses advanced MRI images and artificial intelligence to better predict how localized prostate cancer will behave in patients.
Quick facts
| Grant type | R37 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | June 15, 2026 |
| Project end | May 31, 2031 |
| Funding year | 2026 |
| Award (this year) | $519,592 |
| NIH project number | 1R37CA307500-01A1 |
| Project ID | NIH-11449993 on NIH RePORTER |
Who could benefit from this research
✓Could help
Men with localized prostate cancer whose MRI scans and clinical data can contribute to biomarker development and validation.
✕Less likely to help
Patients not undergoing MRI-based assessment or not contributing data may not benefit directly.
What this research studies
This project will develop and test deep learning–based MRI biomarkers using multiparametric prostate MRI to identify men at highest risk of recurrence after localized prostate cancer treatment. The team will validate the AI-derived biomarker across diverse patient data to ensure it works in real clinics. If successful, the approach could provide a low-cost, noninvasive way to tailor treatment decisions and follow-up plans for individual patients.
Why it matters
Potential benefit: If successful, patients could receive more personalized treatment plans and closer monitoring for those at higher risk.
How similar studies have gone: Previous MRI biomarkers showed promise but lacked robust external validation, making this project an important next step.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Aneja, Sanjay — Yale University
- Study coordinator: Aneja, Sanjay
Related research
Other NIH-funded projects on the same topics.
-
Deep learning to improve MRI-based prostate cancer detection
A new AI tool helps doctors interpret prostate MRI to more accurately find clinically significant cancer in American men.
-
Exercise therapy to slow progression in localized prostate cancer on active surveillance
A study will test whether a structured home-based exercise program helps men with localized prostate cancer who are on active surveillance avoid moving to treatment or worsening, compared with usual care.
-
Biomarkers to identify aggressive prostate cancer early
A patient-friendly program to find and validate biomarkers in urine and tumor tissue that can flag aggressive prostate cancer early so men can get the right treatment sooner.
-
Home walking program for men on active surveillance for low-risk prostate cancer
This project sees if a year-long home treadmill walking program can change cancer-related markers and help men with low-grade prostate cancer on active surveillance avoid early treatment.
-
Precision pre-surgery therapy for high-risk PTEN-loss prostate cancer
This grant plans a targeted, pre-surgical treatment for men with high-risk localized prostate cancer who have PTEN loss, using intensified hormone therapy plus an AKT inhibitor before surgery.
-
AI to help spot aggressive versus slow prostate cancer on MRI
This project uses artificial intelligence that links MRI images and pathology to help doctors tell aggressive prostate cancer from slow-growing or benign tissue for men facing biopsy decisions.
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.