Dataset Information

Qualifiers of atypia in the cytologic diagnosis of thyroid nodules are associated with different Afirma gene expression classifier results and clinical outcomes.

ABSTRACT:

Background

Thyroid nodules with atypia of undetermined significance (AUS) on fine-needle aspiration (FNA) have a low risk of malignancy that appears to vary based on specific features described in the AUS diagnosis. The Afirma gene expression classifier (GEC) is a molecular test designed to improve preoperative risk stratification of thyroid nodules, but its performance for different patterns of AUS has not been defined. The objective of this study was to assess GEC results and clinical outcomes in AUS nodules with architectural atypia (AUS-A), cytologic atypia (AUS-C) or both (AUS-C/A).

Methods

This was a retrospective review of all thyroid nodules with AUS cytopathology that underwent GEC testing at the authors' institution over a period of >4 years.

Results

In 227 nodules that had AUS cytology results and Afirma GEC testing, the rate of benign GEC results was higher in AUS-A nodules (70 of 107; 65%) than in AUS-C/A nodules (25 of 65; 38%; P?=?.0008), and AUS-C nodules exhibited an intermediate rate of benign results (27 of 55 nodules; 59%). The risk of cancer among patients who had GEC-suspicious nodules, 86% of whom underwent resection, was 19% (6 of 25) for AUS-A nodules compared with 57% (21 of 37) for AUS-C/A nodules (P?=?.003) and 45% (10 of 22) for AUS-C nodules (P?=?.07). In nodules that had an indeterminate repeat cytology result, no difference was observed in the rate of benign GEC results or in the malignancy rate compared with nodules that had a single cytology result.

Conclusions

The performance characteristics of Afirma GEC testing vary, depending on qualifiers of cytologic atypia. Recognition of these differences may enable clinicians to provide improved counseling and treatment recommendations to patients. Cancer Cytopathol 2017;125:313-322. © 2017 American Cancer Society.

SUBMITTER: Baca SC

PROVIDER: S-EPMC5484344 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Qualifiers of atypia in the cytologic diagnosis of thyroid nodules are associated with different Afirma gene expression classifier results and clinical outcomes.

Baca Sylvan C SC Wong Kristine S KS Strickland Kyle C KC Heller Howard T HT Kim Matthew I MI Barletta Justine A JA Cibas Edmund S ES Krane Jeffrey F JF Marqusee Ellen E Angell Trevor E TE

Cancer cytopathology 20170202 5

<h4>Background</h4>Thyroid nodules with atypia of undetermined significance (AUS) on fine-needle aspiration (FNA) have a low risk of malignancy that appears to vary based on specific features described in the AUS diagnosis. The Afirma gene expression classifier (GEC) is a molecular test designed to improve preoperative risk stratification of thyroid nodules, but its performance for different patterns of AUS has not been defined. The objective of this study was to assess GEC results and clinical ...[more]

PMID: 28152275

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Project description:ImportanceUse of next-generation sequencing of RNA and machine learning algorithms can classify the risk of malignancy in cytologically indeterminate thyroid nodules to limit unnecessary diagnostic surgery.ObjectiveTo measure the performance of a genomic sequencing classifier for cytologically indeterminate thyroid nodules.Design, setting, and participantsA blinded validation study was conducted on a set of cytologically indeterminate thyroid nodules collected by fine-needle aspiration biopsy between June 2009 and December 2010 from 49 academic and community centers in the United States. All patients underwent surgery without genomic information and were assigned a histopathology diagnosis by an expert panel blinded to all genomic information. There were 210 potentially eligible thyroid biopsy samples with Bethesda III or IV indeterminate cytopathology that constituted a cohort previously used to validate the gene expression classifier. Of these, 191 samples (91.0%) had adequate residual RNA for validation of the genomic sequencing classifier. Algorithm development and independent validation occurred between August 2016 and May 2017.ExposuresThyroid nodule surgical histopathology diagnosis by an expert panel blinded to all genomic data.Main outcomes and measuresThe primary end point was measurement of genomic sequencing classifier sensitivity, specificity, and negative and positive predictive values in biopsies from Bethesda III and IV nodules. The secondary end point was measurement of classifier performance in biopsies from Bethesda II, V, and VI nodules.ResultsOf the 183 included patients, 142 (77.6%) were women, and the mean (range) age was 51.7 (22.0-85.0) years. The genomic sequencing classifier had a sensitivity of 91% (95% CI, 79-98) and a specificity of 68% (95% CI, 60-76). At 24% cancer prevalence, the negative predictive value was 96% (95% CI, 90-99) and the positive predictive value was 47% (95% CI, 36-58).Conclusions and relevanceThe genomic sequencing classifier demonstrates high sensitivity and accuracy for identifying benign nodules. Its 36% increase in specificity compared with the gene expression classifier potentially increases the number of patients with benign nodules who can safely avoid unnecessary diagnostic surgery.

Project description:BackgroundIndeterminate categories of thyroid cytopathology (categories B-III and B-IV of the Bethesda system) are integrated by a heterogeneous spectrum of cytological scenarios that are generally clustered for analysis and management recommendations. It has been suggested that aspirates exhibiting nuclear atypia have a higher risk of malignancy. This study aimed to assess whether cytologically indeterminate thyroid nodules with nuclear atypia have a significantly higher cancer risk than those without nuclear atypia.MethodsOn June 30, 2016, PubMed and EMBASE were searched for articles in English or Spanish using a search strategy developed by an endocrinologist and a librarian. Case reports were excluded, and no date limits were used. The references of all included studies were also screened for relevant missing studies. Studies were included if the prevalences of malignancy of cytologically indeterminate thyroid nodules with histological confirmation with and without nuclear atypia were reported. Studies were excluded if they had: (i) nodules suspicious for malignancy; (ii) nodules with non-indeterminate (B-III or B-IV) cytology on repeated biopsy, if performed; (iii) nodules not consecutively evaluated; or (iv) cohorts overlapping with another larger series. Two investigators independently assessed the eligibility and risk of bias of the studies. PRISMA and MOOSE guidelines were followed. Summary data were extracted from published reports by one investigator and independently reviewed by another. Data were pooled using a random-effects model. Heterogeneity was explored using subgroup analysis and mixed-effect model meta-regression. The odds ratio for malignancy of cytologically indeterminate thyroid nodules with nuclear atypia over cytologically indeterminate thyroid nodules without nuclear atypia was calculated.ResultsOf 2571 retrieved studies, 20 were eligible. The meta-analysis was conducted on summary data of 3532 cytologically indeterminate thyroid nodules: 1162 with and 2370 without nuclear atypia. The odds ratio for malignancy in cytologically indeterminate thyroid nodules with nuclear atypia was 3.63 [confidence interval 3.06-4.35]. There was no evidence of publication bias, and heterogeneity was insignificant (I2 < 0.01%, p = 0.40).ConclusionsNuclear atypia is a significant indicator of malignancy in cytologically indeterminate thyroid nodules and needs to be standardized and implemented into clinical practice.

Dataset Information

Qualifiers of atypia in the cytologic diagnosis of thyroid nodules are associated with different Afirma gene expression classifier results and clinical outcomes.

Background

Methods

Results

Conclusions

Publications

Qualifiers of atypia in the cytologic diagnosis of thyroid nodules are associated with different Afirma gene expression classifier results and clinical outcomes.

Similar Datasets

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