Abstract

Thyroid fine-needle aspiration biopsy results are cytologically indeterminate in 15-30% of cases. When these nodules undergo diagnostic surgery, approximately three-quarters are histologically benign. These unnecessary surgeries diminish quality of life, generate complications, and increase healthcare costs. The Afirma gene expression classifier (GEC) is validated to pre-operatively identify cytologically indeterminate nodules likely to be truly benign so that surgery can be avoided. Its performance is supported by robust multicenter prospective and blinded clinical validation studies, and supported by extensive independent clinical utility publications which show a marked reduction in surgery among patients with benign Afirma GEC results. To rule-out cancer and avoid unnecessary diagnostic surgery, Afirma’s quality and depth of validation stand alone. The accuracy of a benign result is the negative predictive value (NPV). Afirma achieves an NPV ≥94% among cytologically indeterminate nodules (Bethesda III or IV). Thirteen clinical utility studies describing 1468 GEC benign patients demonstrate that few Afirma GEC benign nodules undergo surgery, including after 3 years of follow-up. With a specificity of 52%, over half of the truly benign nodules with indeterminate cytology receive a benign GEC result. High test sensitivity is critical to safely rule out cancer. The Afirma GEC’s 90% sensitivity means that regardless of the pre-test risk of malignancy, 90% of all malignant nodules are GEC suspicious. The Afirma GEC has transformed patient care. Where the majority of cytologically indeterminate patients were once operated to determine if the nodule was benign or malignant, now nearly half of these surgeries can be avoided.

Highlights

  • Prior to the adoption of thyroid nodule fine-needle aspiration biopsy (FNAB), thyroid nodules were regularly referred for diagnostic surgery because of their 5-15% risk of malignancy (ROM) [1]

  • While an Afirma gene expression classifier (GEC) suspicious result raises the risk of cancer from 24-25% to 37-38%, it should be clear that the strength of the test is that it identifies just over one-half of all benign nodules with Bethesda III or IV cytology as genomically benign, and 90% of all cancers as genomically suspicious regardless of the cancer prevalence (Figure 1)

  • In a series of 165 Bethesda III/IV nodules operated without Afirma GEC testing, we reported that the use of total thyroidectomy was as low as 39% for Bethesda III nodules in academic centers to as high as 60% in Bethesda IV nodules in community practice settings [(67) supplemental data]

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Summary

Introduction

Prior to the adoption of thyroid nodule fine-needle aspiration biopsy (FNAB), thyroid nodules were regularly referred for diagnostic surgery because of their 5-15% risk of malignancy (ROM) [1]. When cytologically indeterminate thyroid nodules undergo diagnostic surgery, approximately three-quarters prove to be benign on surgical histopathology (Figure 1) [4,5,6] The care of such patients is being dramatically altered by a new diagnostic strategy that pre-operatively identifies many of these benign nodules with indeterminate cytopathology [Bethesda categories III and IV [7]] as having a low risk of cancer so that diagnostic surgery can be avoided, along with its costs, complications, and inconveniences. While an Afirma GEC suspicious result raises the risk of cancer from 24-25% to 37-38%, it should be clear that the strength of the test is that it identifies just over one-half of all benign nodules with Bethesda III or IV cytology as genomically benign, and 90% of all cancers as genomically suspicious regardless of the cancer prevalence (Figure 1). Failing to trigger one of these cassettes, the GEC evaluates the expression of 142 genes that are used in a proprietary mathematical algorithm to classify indeterminate thyroid nodule samples as either GEC benign or GEC suspicious

Rationale for the Measurement of Messenger Ribonucleic Acid Expression
Clinical Validation
Clinical Practice Experiences and Clinical Utility
Implementation in Routine Clinical Practice
Clinical Decision Making
Malignancy Classifiers
Findings
Conclusion
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