HMN 2026: How Light-based imaging offers hope in improving thyroid cancer diagnosis while reducing surgeries

A new Houston Methodist study highlights a promising noninvasive imaging technique that could help doctors more accurately diagnose papillary thyroid cancer, the most common type of thyroid cancer.

The approach, second harmonic generation (SHG) microscopy, uses light to examine changes in collagen, the protein that helps give structure to tissues. These subtle changes in the tissue may signal whether a thyroid nodule is cancerous.

Published in the Journal of Biomedical Optics, the study was co-led by Stephen Wong, Ph.D., the John S. Dunn Presidential Distinguished Chair in Biomedical Engineering at Houston Methodist, and Raksha Raghuanthan, Ph.D., assistant professor at the Houston Methodist Research Institute.

“This work is significant for the field because it could make thyroid cancer diagnosis faster, more accurate and more objective,” Wong said. “It offers a new way to improve diagnosis, complementing conventional cytology.”

Thyroid cancer is the most common cancer of the endocrine system and is the leading cancer among young adults ages 16–33, according to the National Institutes of Health.

“Unlike artificial intelligence black-box approaches, this study used interpretable statistical modeling to identify biologically meaningful collagen signatures associated with thyroid cancer,” Wong said.

By providing clearer, more consistent information, the researchers said this new and less invasive procedure could be a valuable addition to traditional lab testing and help doctors make more accurate diagnoses.

The researchers hope to establish this biopsy method and significantly reduce the need for invasive surgeries. Future steps include validating the method in larger patient cohorts and expanding the technology to distinguish among other thyroid cancer subtypes.

Other collaborators on the study are Lin Wang, Hong Zhao, Helmi Khadra and Elizabeth Jacobi from Houston Methodist; Wesley Poon, Orhun Davarci and Reid Master from Texas A&M University; and Jun Liu from Shanghai Jiao Tong University.

The content is provided for information purposes only.