The new Afirma GSC uniquely combines RNA sequencing and machine learning to leverage more enriched, previously undetectable genomic information. The findings suggest that by maintaining the current Afirma test's high sensitivity and further improving its specificity, the Afirma GSC can identify 30 percent more benign thyroid nodules among those deemed indeterminate - not clearly benign or malignant - following cytopathology, thereby enabling nearly 70 percent of patients whose thyroid nodules are benign to avoid unnecessary diagnostic surgery.
"The Afirma GEC has already changed how physicians manage patients with indeterminate thyroid nodules, enabling them to monitor these patients rather than direct them to thyroid surgery, which can have lifelong implications," said
The new Afirma GSC was validated on a prospective, multicenter, blinded cohort of 191 indeterminate thyroid nodule fine needle aspiration samples - the same sample set previously used to validate the GEC test. Investigators found that the Afirma GSC maintained the current test's high sensitivity (91 percent vs. 90 percent) and significantly increased its specificity (68 percent vs. 52 percent). The Afirma GSC's negative predictive value was 96 percent, compared to 94 percent for the current test.
The Afirma GSC leverages RNA sequencing to derive clinically useful information from enriched genomic content, including gene expression, DNA variants, fusions, copy number variants and other features that may be predictive of thyroid cancer and can enhance the classifier's ability to distinguish benign from malignant nodules. The classifier uses machine learning that is based on ensemble methods in which multiple algorithms - each playing its own role - are used to obtain a better predictive performance than any single algorithm on its own. The algorithms evaluate the vast genomic information enabling the test to "recognize" benign nodules.
"We are employing the same machine learning methods that are being used in other fields such as social media and self-driving cars, but applying them to thyroid cancer diagnosis," said
The pivotal clinical validation data were unveiled in a product theater event during the AACE meeting. A poster on the Afirma GSC's development will be presented at the conference on
The Afirma Genomic Sequencing Classifier is the next-generation version of the Afirma Gene Expression Classifier, and is used to identify patients with benign thyroid nodules among those with indeterminate cytopathology results in order to preserve the thyroid. Each year in
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