Imaging to personalize stomach cancer treatment
Computational imaging approaches to personalized gastric cancer treatment
Using advanced computer analysis of scans and pathology to help doctors choose the best treatment for people with stomach (gastric) cancer.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project ID | NIH-11294307 on NIH RePORTER |
What this research studies
You or other patients' CT and pathology images will be analyzed with radiomics and deep learning to find imaging patterns linked to outcomes. The researchers will add pathology knowledge into AI models and develop new methods to read scans taken over time to predict response to pre-surgery (neoadjuvant) therapy. The goal is to reduce unnecessary chemotherapy for low-risk patients and identify those whose tumors need stronger treatment. Work will use clinical imaging and tissue data from Stanford and partner sites to build models that could guide future care.
Who could benefit from this research
Good fit: People with stomach (gastric) cancer who have clinical imaging and pathology data, especially those undergoing neoadjuvant therapy or surgery, are the ideal candidates for this line of work.
Not a fit: People without gastric cancer or without serial imaging/pathology data are unlikely to be included or directly benefit from this project.
Why it matters
Potential benefit: If successful, this could spare some patients from harmful but unnecessary chemotherapy and direct others to more effective treatments sooner.
How similar studies have performed: AI and radiomics methods have shown promise in predicting outcomes in several cancers but remain an emerging and not yet standard approach for gastric cancer.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Li, Ruijiang — Stanford University
- Study coordinator: Li, Ruijiang
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.