Improving ovarian cancer treatment predictions using advanced imaging techniques
Official title: Early Evaluation of Ovarian Cancer Prognosis by Fusing Radiographic and Histopathologic Imaging Information
Funded at University of Oklahoma · NIH-11005785 · Data updated 2026-11-12
This study is looking to improve how doctors predict which ovarian cancer patients will respond well to chemotherapy by using advanced computer techniques to analyze CT scans and tissue images, helping to identify the best treatment options for each patient.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Oklahoma NIH-funded |
| Lab location | 1 site (Norman, United States) |
| Project start | February 15, 2022 |
| Project end | December 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $205,961 |
| NIH project number | 5P20GM135009-04 |
| Project ID | NIH-11005785 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are ovarian cancer patients undergoing chemotherapy who have diverse tumor characteristics.
✕Less likely to help
Patients with non-ovarian cancers or those not receiving chemotherapy may not benefit from this research.
What this research studies
This research aims to enhance the prediction of how ovarian cancer patients will respond to chemotherapy by combining information from radiographic and histopathologic images. Using advanced machine learning techniques, the study will analyze quantitative features from CT scans and digital pathology images to create a new imaging marker. This marker is expected to provide valuable insights into which patients are likely to benefit from specific chemotherapy treatments, addressing a significant gap in current clinical practice. The research will involve both retrospective and prospective patient data to validate the effectiveness of this approach.
Why it matters
Potential benefit: If successful, this research could lead to more personalized and effective chemotherapy treatments for ovarian cancer patients.
How similar studies have gone: Other research has shown promising results using similar imaging fusion techniques, indicating potential for success in this novel approach.
Where this research is happening
-
University of Oklahoma
Norman, United States Active
Researchers
- Principal investigator: Qiu, Yuchen — University of Oklahoma
- Study coordinator: Qiu, Yuchen
Related research
Other NIH-funded projects on the same topics.
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Combining CT scans and pathology slides to predict ovarian cancer treatment response
This project is testing a computer-made image marker that combines CT scans and pathology slides to tell which ovarian cancer patients are likely to benefit from chemotherapy.
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Understanding the experience of ovarian cancer after diagnosis to improve survivorship
This NIH grant funds a mixed-methods study to better understand physical and emotional challenges faced by ovarian cancer survivors and how supportive care can help them live better after diagnosis.
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Targeting quiescent ovarian cancer cells to overcome chemotherapy resistance
A study focusing on why some ovarian cancer cells pause dividing and resist chemotherapy, with the goal of finding new treatment targets for relapsed disease.
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Photodynamic lipids to boost chemotherapy for metastatic ovarian cancer
A patient-friendly approach tests a light-activated method to make ovarian cancer metastases more sensitive to chemotherapy.
-
Colorectal cancer prognosis through a broad, collaborative study
A large, multi-center study follows people newly diagnosed with colorectal cancer to find biomarkers and patterns that predict outcomes and guide future care.
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Integrated framework to validate and quantify proteoforms in patient-derived ovarian cancer cells
A collaboration will combine advanced top-down proteomics with a new computational method to precisely identify and measure intact proteoforms in tiny samples from ovarian cancer cells.
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.