Using deep learning to help identify aggressive prostate cancer on MRI
Official title: Rad-pathomic deep learning models to assist radiologists in differentiating aggressive from indolent prostate cancer on MRI
Funded at Stanford University · NIH-11006283 · Data updated 2026-11-12
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.
Quick facts
| Grant type | R37 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | January 12, 2022 |
| Project end | December 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $599,578 |
| NIH project number | 5R37CA260346-04 |
| Project ID | NIH-11006283 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are American men who are at risk for prostate cancer and require MRI imaging for diagnosis.
✕Less likely to help
Patients with non-prostate cancers or those who do not require MRI imaging for prostate cancer diagnosis may not benefit from this research.
What this research studies
This research focuses on improving the accuracy of MRI scans in distinguishing between aggressive and non-aggressive prostate cancer. By developing advanced deep learning models that analyze MRI images alongside pathology data, the project aims to reduce the number of unnecessary biopsies and improve cancer detection rates. The methodology involves creating specific MRI biomarkers that correlate with pathology features, enhancing the ability of radiologists to make accurate diagnoses. This approach could significantly impact the management of prostate cancer by providing clearer guidance on treatment options.
Why it matters
Potential benefit: If successful, this research could lead to more accurate diagnoses of prostate cancer, reducing unnecessary procedures and improving patient outcomes.
How similar studies have gone: Previous studies have shown promise in using deep learning for cancer detection, indicating that this approach has potential for success.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Rusu, Mirabela — Stanford University
- Study coordinator: Rusu, Mirabela
Related research
Other NIH-funded projects on the same topics.
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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.
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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.
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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 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.
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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.