Using deep learning to analyze cancer tissue images and genetic data
Official title: Deep-learning Integration of Histopathology and Proteogenomics at a Pan-cancer Level - Resubmission
Funded at New York University School of Medicine · NIH-11020985 · Data updated 2026-11-12
This study is working on using advanced computer technology to look at cancer tissue images and other related data to help doctors better understand and treat cancer, making it easier to use existing samples for improving patient care.
Quick facts
| Grant type | Fellowship 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 | March 1, 2023 |
| Project end | June 30, 2025 |
| Funding year | 2025 |
| Award (this year) | $20,297 |
| NIH project number | 5F30CA271622-03 |
| Project ID | NIH-11020985 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients diagnosed with various types of cancer who have undergone tissue biopsies.
✕Less likely to help
Patients who do not have cancer or those whose cancer has not been biopsied may not benefit from this research.
What this research studies
This research aims to develop advanced deep learning models that can analyze histopathology images of cancer tissues to identify important clinical and molecular features. By integrating these images with proteogenomic data, the project seeks to improve cancer prognosis and treatment strategies. The approach involves training convolutional neural networks on data from various cancer types to predict clinical outcomes and mutations, making it more accessible for clinical use. This could lead to better understanding and treatment of cancer by utilizing existing tissue samples more effectively.
Why it matters
Potential benefit: If successful, this research could enhance the accuracy of cancer prognosis and treatment by providing clinicians with powerful tools to analyze tissue morphology alongside genetic information.
How similar studies have gone: Other research has shown promise in using deep learning for medical image analysis, indicating that this approach could be effective in cancer diagnostics as well.
Where this research is happening
-
New York University School of Medicine
New York, United States Active
Researchers
- Principal investigator: Wang, Joshua — New York University School of Medicine
- Study coordinator: Wang, Joshua
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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.