Predicting recurrence risk in early-stage oral cancer using advanced imaging techniques
A Quantitative Risk Model for Predicting Outcome and Identifying Structural Biomarkers of Treatment Targets in Oral Cancer on a Large Multi-Center Patient Cohort
['FUNDING_R01'] · STATE UNIVERSITY OF NEW YORK AT BUFFALO · NIH-10824384
This study is looking to create a smart tool that uses advanced technology to help predict the chances of oral cavity cancer coming back after surgery, so patients can get more personalized care based on their specific risks.
Quick facts
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | STATE UNIVERSITY OF NEW YORK AT BUFFALO (nih funded) |
| Locations | 1 site (AMHERST, UNITED STATES) |
| Trial ID | NIH-10824384 on ClinicalTrials.gov |
What this research studies
This research aims to develop a Quantitative Risk Model (QRM) that utilizes machine learning and artificial intelligence to predict the risk of recurrence in patients with Stage I/II oral cavity cancers after surgery. By analyzing pathology images, the study seeks to identify specific biomarkers that indicate tumor aggression, which can help in tailoring post-treatment care. The approach involves creating a robust analysis pipeline that enhances the accuracy of prognostic assessments compared to traditional methods. Patients will benefit from a more precise understanding of their recurrence risk, potentially leading to better-informed treatment decisions.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients diagnosed with Stage I or II oral cavity cancers who have undergone surgical treatment.
Not a fit: Patients with advanced-stage oral cancers or those who have not undergone surgery may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could provide patients with a more accurate prediction of their cancer recurrence risk, leading to personalized treatment plans.
How similar studies have performed: Previous research has shown promise in using machine learning and imaging techniques for cancer prognostication, indicating that this approach could be effective.
Where this research is happening
AMHERST, UNITED STATES
- STATE UNIVERSITY OF NEW YORK AT BUFFALO — AMHERST, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: DOYLE, SCOTT — STATE UNIVERSITY OF NEW YORK AT BUFFALO
- Study coordinator: DOYLE, SCOTT
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 below.
Conditions: Cancers