Protein-based MRI agent detects tiny lung cancer metastases

Jenny J. Yang, Georgia State University Regents' Professor Emerita of Biochemistry and Biophysical ChemistryLung cancer is often diagnosed only after it has spread, when treatment options become more limited. Our technology can detect tiny tumors and hidden metastases without exposing patients to radiation. It also helps reveal how aggressive a cancer may be and how likely it is to spread, potentially reducing the need for invasive biopsies."

Researchers found the approach was particularly effective in aggressive lung adenocarcinomas associated with certain mutations, which are frequently resistant to current therapies and prone to spreading to multiple organs.

"This breakthrough was made possible through a close collaboration between Georgia State University and Emory University, bringing together complementary expertise in advanced imaging and cancer biology," Zhou said.

By combining innovative imaging technology with sophisticated models of cancer metastasis, the researchers were able to visualize invasive lung cancer and its spread to other organs at much earlier stages than previously possible.

Beyond diagnosis, the findings may have important implications for personalized cancer treatment. By enabling physicians to visualize collagen remodeling throughout primary and metastatic tumors, the technology may eventually help identify high-risk patients, guide treatment selection and monitor response to therapy over time.

"This is a game changer for cancer diagnosis and precision medicine," Yang said. "For the first time, we've shown that precision MRI can detect tiny tumors and whole body metastases without using harmful radiation while also providing important clues about how aggressively a cancer may behave."

Source:

Georgia State University

Journal reference:

Li, D., et al. (2026). Early detection of invasive lung cancer and multiorgan metastasis by a collagen-targeted protein MRI contrast agent. Science Advances. DOI: 10.1126/sciadv.adz6815. https://www.science.org/doi/10.1126/sciadv.adz6815