PHILADELPHIA - Thyroid nodules are small lumps that form within the thyroid gland and are quite common in the general population, with a prevalence as high as 67%. The great majority of thyroid nodules are not cancerous and cause no symptoms. However, there are currently limited guidelines on what to do with a nodule when the risk of cancer is uncertain. A new study from The Sidney Kimmel Cancer Center - Jefferson Health investigates whether a non-invasive method of ultrasound imaging, combined with a Google-platform machine-learning algorithm, could be used as a rapid and inexpensive first screen for thyroid cancer.
"Currently, ultrasounds can tell us if a nodule looks suspicious, and then the decision is made whether to do a needle biopsy or not," says Elizabeth Cottril, MD, an otolaryngologist at Thomas Jefferson University, and clinical leader of the study. "But fine-needle biopsies only act as a peephole, they don't tell us the whole picture. As a result, some biopsies return inconclusive results for whether or not the nodule may be malignant, or cancerous, in other words."
If examining the cells of a needle biopsy proves inconclusive, the sample can be further tested via molecular diagnostics to determine risk of malignancy. These tests look for the presence of certain mutations or molecular markers that are associated with malignant thyroid cancers. When nodules test positive for high risk markers or mutations, the thyroid may be surgically removed. However, the standards for when to use molecular testing are still in development, and the test is not yet offered in all practice settings, especially at smaller community hospitals.
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In order to improve the predictive power of the first-line diagnostic, the ultrasound, Jefferson researchers looked into machine learning or artificial intelligence models developed by Google. These applications are being used in other spaces: retail giants like Urban Outfitters use machine learning to help classify their many products, making it easier for the consumer to find an item they're interested in. Disney uses it to annotate their products based on specific characters or movies. In this case, the researchers applied a machine-learning algorithm to ultrasound images of patients' thyroid nodules to see if it could pick out distinguishing patterns. The study was published in JAMA-Oto on October 24th.
"The goal of our study was to see whether automated machine learning could use image-processing technology to predict the genetic risk of thyroid nodules, compared to molecular testing," says Kelly Daniels, a fourth year medical student at Jefferson and first author of the study.