Improving diagnosis and prognosis of prostate cancer using advanced machine learning techniques
Official title: Robust and Interpretable Multimodal Machine Learning Models for Diagnosis and Prognosis of Prostate Cancer
Funded at Weill Medical Coll of Cornell Univ · NIH-11045739 · Data updated 2026-11-12
This study is working on using smart computer technology to improve how doctors diagnose and predict prostate cancer, so patients can get more accurate results and better treatment options.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Weill Medical Coll of Cornell Univ NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | March 1, 2024 |
| Project end | February 28, 2029 |
| Funding year | 2025 |
| Award (this year) | $153,721 |
| NIH project number | 5K25CA283145-02 |
| Project ID | NIH-11045739 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 or have been diagnosed with the disease.
✕Less likely to help
Patients with prostate cancer who are not eligible for imaging studies or those with advanced metastatic disease may not benefit from this research.
What this research studies
This research focuses on enhancing the accuracy of prostate cancer diagnosis and prognosis through the use of advanced machine learning models. By integrating large imaging datasets with clinical scores, the project aims to develop robust frameworks that can better identify clinically significant prostate cancer. Patients will benefit from improved diagnostic tools that utilize deep learning architectures to analyze MRI images and other clinical data, potentially leading to more personalized treatment plans. The research will also address the challenges of over- and underdiagnosis that currently exist in standard diagnostic methods.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and timely diagnoses of prostate cancer, improving treatment outcomes for patients.
How similar studies have gone: Other research has shown promising results using machine learning approaches for cancer diagnosis, indicating that this project builds on a foundation of successful methodologies.
Where this research is happening
-
Weill Medical Coll of Cornell Univ
New York, United States Active
Researchers
- Principal investigator: Kim, Heejong — Weill Medical Coll of Cornell Univ
- Study coordinator: Kim, Heejong
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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.