Using advanced imaging and AI to improve prostate cancer biopsy decisions
Official title: RSI-AI: Predicting clinically significant prostate cancer to guide biopsy decisions by combining advanced tissue microstructure imaging with deep learning
Funded at Cortechs Labs, INC. · NIH-10933573 · Data updated 2026-11-12
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.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Cortechs Labs, INC. NIH-funded |
| Lab location | 1 site (San Diego, UNITED STATES) |
| Project start | August 15, 2021 |
| Project end | July 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $860,506 |
| NIH project number | 5R44CA254738-03 |
| Project ID | NIH-10933573 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are men who are at risk for prostate cancer and may require a biopsy based on clinical indications.
✕Less likely to help
Patients who have already been diagnosed with prostate cancer or those who do not require a biopsy may not benefit from this research.
What this research studies
This research aims to enhance the accuracy of prostate cancer diagnoses by developing advanced tools that combine tissue imaging and artificial intelligence. The project focuses on creating two models, RSI-AI and RSI-AI+, which utilize deep learning algorithms to analyze diffusion magnetic resonance imaging (MRI) data. These models will help clinicians identify which patients should undergo biopsies and pinpoint the most relevant areas of the prostate to target. By integrating clinical data with imaging results, the research seeks to provide a more comprehensive assessment of prostate cancer risk.
Why it matters
Potential benefit: If successful, this research could lead to more accurate prostate cancer diagnoses, reducing unnecessary biopsies and improving treatment outcomes.
How similar studies have gone: Other research has shown promising results using advanced imaging techniques and AI for cancer detection, indicating that this approach has potential for success.
Where this research is happening
-
Cortechs Labs, INC.
San Diego, United States Active
Researchers
- Principal investigator: White, Nathan Scott — Cortechs Labs, INC.
- Study coordinator: White, Nathan Scott
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.
-
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.
-
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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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.