Using advanced digital imaging and AI to improve prostate cancer diagnosis
Official title: Virtual Digital Histopathology with Explainable Deep Learning for Prostate Cancer Diagnosis
Funded at University of California-Irvine · NIH-10990125 · Data updated 2026-11-12
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.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California-Irvine NIH-funded |
| Lab location | 1 site (Irvine, United States) |
| Project start | September 1, 2024 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $453,000 |
| NIH project number | 1R21CA294195-01 |
| Project ID | NIH-10990125 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals undergoing prostate biopsies for suspected cancer.
✕Less likely to help
Patients who have already been diagnosed and treated for prostate cancer may not benefit from this research.
What this research studies
This research focuses on enhancing prostate cancer diagnosis through the use of digital histopathology and deep learning algorithms. By analyzing tissue biopsy images, the project aims to automate tumor segmentation and provide clearer explanations of the results to clinicians. The approach involves converting non-stained biopsy images into virtual stained versions, allowing for better evaluation of tumor characteristics and aiding in accurate diagnosis. The goal is to improve the interpretability of these advanced imaging techniques for better clinical adoption.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and reliable prostate cancer diagnoses, ultimately improving patient outcomes.
How similar studies have gone: Previous research has shown promise in using deep learning for cancer diagnosis, indicating potential success for this novel approach.
Where this research is happening
-
University of California-Irvine
Irvine, United States Active
Researchers
- Principal investigator: Shah, Pratik — University of California-Irvine
- Study coordinator: Shah, Pratik
Related research
Other NIH-funded projects on the same topics.
-
Ethical multimodal AI platform to improve prostate cancer detection with ultrasound
A patient-friendly AI system uses ultrasound and MRI information to help doctors better find prostate cancer, aiming to improve accuracy and access.
-
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.
-
Targeting PSMA for Prostate Cancer Diagnosis and Treatment
This project explores a new way to find and treat advanced prostate cancer by focusing on a specific protein called PSMA.
-
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.
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.