Improving detection of tumor spread in head and neck cancer using advanced AI techniques
Official title: Optimization and Validation of a Cost-effective Image-Guided Automated Extracapsular Extension Detection Framework through Interpretable Machine Learning in Head and Neck Cancer
Funded at Mississippi State University · NIH-10913498 · Data updated 2026-11-12
This study is working on a smart computer system that helps doctors spot a specific issue in head and neck cancers that can affect how well patients do, so they can make better treatment plans before surgery.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Mississippi State University NIH-funded |
| Lab location | 1 site (Mississippi State, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $147,937 |
| NIH project number | 5R03DE032766-02 |
| Project ID | NIH-10913498 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with squamous cell carcinoma of the head and neck who are undergoing preoperative evaluations.
✕Less likely to help
Patients with other types of cancers or those who are not undergoing surgical treatment for head and neck cancer may not benefit from this research.
What this research studies
This research focuses on developing an AI-based framework to detect extracapsular extension (ECE) in head and neck cancers, which is a critical factor affecting patient outcomes. By utilizing machine learning algorithms, the project aims to provide accurate predictions of ECE during preoperative evaluations, potentially allowing for better treatment planning. The approach emphasizes transparency and interpretability, ensuring that clinicians can understand how predictions are made. This could lead to more informed decisions regarding the need for chemoradiation before surgery, ultimately improving patient care.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate detection of tumor spread, allowing for tailored treatment plans that may improve survival rates and reduce treatment-related complications.
How similar studies have gone: Other research has shown promise in using AI and machine learning for cancer detection, indicating that this approach could be a significant advancement in the field.
Where this research is happening
-
Mississippi State University
Mississippi State, United States Active
Researchers
- Principal investigator: Wang, Haifeng — Mississippi State University
- Study coordinator: Wang, Haifeng
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AI-powered radiotherapy planning for cervical and head-and-neck cancer
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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.