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03 Feb 2020 - 06 Feb 2020
01 Mar 2020 - 05 Mar 2020

Metabolic Classification of Thyroid Nodules Uses MS Imaging

By Labmedica International staff writers
Posted on 26 Oct 2019
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Image: A microscope image of thyroid cancer cells, specifically papillary thyroid carcinoma, or PTC (Photo courtesy of Wendong Yu/Baylor College of Medicine).
Image: A microscope image of thyroid cancer cells, specifically papillary thyroid carcinoma, or PTC (Photo courtesy of Wendong Yu/Baylor College of Medicine).
Fine-needle aspiration (FNA) biopsy is a well-established technique for diagnosis of suspicious thyroid lesions. However, histologic discrimination between malignant and benign thyroid nodules from FNA can be challenging.

Each year, thanks to inconclusive tests for thyroid cancer, thousands of people undergo unnecessary surgeries to remove part or all of their thyroids. A new test based on the unique chemical fingerprints of thyroid cancer might change that and it is faster and about two-thirds more accurate than the diagnostic tests doctors use today.

Biochemists at the University of Texas at Austin (Austin, TX, USA) and their colleagues used a technology called mass spectrometry imaging. The new metabolic thyroid test identifies metabolites produced by cancerous cells that act as a kind of diagnostic fingerprint. The team worked on identifying these diagnostic metabolic fingerprints for over two years using 178 patient tissues before starting a pilot clinical study. During the clinical study, 68 new patients were tested, nearly a third of who had received inconclusive FNA results. The new metabolic thyroid test returned a false positive only about 1 time in 10 and could have prevented 17 patients in the study from undergoing unnecessary surgeries.

The scientists employed desorption electrospray ionization mass spectrometry (DESI-MS) imaging to diagnose thyroid lesions based on the molecular profiles obtained from FNA biopsy samples. Based on the molecular profiles obtained from malignant thyroid carcinomas and benign thyroid tissues, classification models were generated and used to predict on DESI-MSI data from FNA material with high performance. Their results demonstrate the potential for DESI-MSI to reduce the number of unnecessary diagnostic thyroid surgeries.

James W. Suliburk, MD, FACS, a co-principal investigator and head of endocrine surgery at Baylor College of Medicine (Houston, TX, USA) said, “With this next generation test, we can provide thyroid cancer diagnoses faster and with more precision than current techniques, this will be the new state-of-the-art. We are able to do this analysis directly on the FNA sample and much more rapidly than the current process, which could take between three and 30 days.” The study was published on October 7, 2019, in the journal Proceedings of the National Academy of Sciences.

Related Links:
University of Texas at Austin
Baylor College of Medicine

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