Deep learning to improve MRI-based prostate cancer detection
Official title: Advancing Clinically Translatable Deep Learning for Prostate Cancer Detection on MRI
Funded at New York University School of Medicine · NIH-11450734 · Data updated 2026-11-12
A new AI tool helps doctors interpret prostate MRI to more accurately find clinically significant cancer in American men.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | New York University School of Medicine NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | July 16, 2026 |
| Project end | June 30, 2031 |
| Funding year | 2026 |
| Award (this year) | $700,171 |
| NIH project number | 1R01CA307281-01A1 |
| Project ID | NIH-11450734 on NIH RePORTER |
Who could benefit from this research
✓Could help
American men undergoing prostate MRI for cancer evaluation may be eligible.
✕Less likely to help
Individuals outside the target condition or those not undergoing MRI-based evaluation for prostate cancer may not benefit.
What this research studies
This grant aims to develop a robust deep learning model that can read prostate MRI scans from diverse patients and scanners, with the goal of identifying cancers that really matter while reducing unnecessary procedures. The approach includes training the model on varied imaging data and testing its performance across different clinical settings to ensure reliable results. The project also focuses on providing clear confidence measures so clinicians can trust and act on the AI recommendations in real time.
Why it matters
Potential benefit: If successful, patients could experience more accurate diagnoses, fewer unnecessary biopsies, and faster, more confident treatment decisions.
How similar studies have gone: Previous AI models show promise but have limited generalizability and actionable confidence; this project aims to address those gaps.
Where this research is happening
-
New York University School of Medicine
New York, United States Active
Researchers
- Principal investigator: Johnson, Patricia — New York University School of Medicine
- Study coordinator: Johnson, Patricia
Related research
Other NIH-funded projects on the same topics.
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Deep learning MRI biomarkers to predict prostate cancer outcomes
A Yale-led project uses advanced MRI images and artificial intelligence to better predict how localized prostate cancer will behave in patients.
-
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.
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Using deep learning to help identify aggressive prostate cancer on MRI
This study is working on making MRI scans better at telling the difference between aggressive and non-aggressive prostate cancer, which could help reduce unnecessary biopsies and improve how doctors detect cancer, ultimately leading to better treatment choices for patients.
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Using advanced digital imaging and AI to improve prostate cancer diagnosis
This study is working on using advanced computer technology to help doctors better understand prostate cancer by analyzing tissue samples, making it easier to spot tumors and explain the results clearly.
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Using advanced imaging and AI to improve prostate cancer biopsy decisions
This study is working on new tools that use advanced imaging and artificial intelligence to help doctors better diagnose prostate cancer, making it easier to decide who needs a biopsy and where to focus on in the prostate.
-
Cardiovascular risk planning and reduction for men with prostate cancer
A program to better predict and reduce heart disease risk in men treated for prostate cancer, especially older patients.
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.