Ontology highlight
ABSTRACT: Importance
Use 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.Objective
To measure the performance of a genomic sequencing classifier for cytologically indeterminate thyroid nodules.Design, setting, and participants
A 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.Exposures
Thyroid nodule surgical histopathology diagnosis by an expert panel blinded to all genomic data.Main outcomes and measures
The 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.Results
Of 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 relevance
The 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.
SUBMITTER: Patel KN
PROVIDER: S-EPMC6583881 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature

Patel Kepal N KN Angell Trevor E TE Babiarz Joshua J Barth Neil M NM Blevins Thomas T Duh Quan-Yang QY Ghossein Ronald A RA Harrell R Mack RM Huang Jing J Kennedy Giulia C GC Kim Su Yeon SY Kloos Richard T RT LiVolsi Virginia A VA Randolph Gregory W GW Sadow Peter M PM Shanik Michael H MH Sosa Julie A JA Traweek S Thomas ST Walsh P Sean PS Whitney Duncan D Yeh Michael W MW Ladenson Paul W PW
JAMA surgery 20180901 9
<h4>Importance</h4>Use 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.<h4>Objective</h4>To measure the performance of a genomic sequencing classifier for cytologically indeterminate thyroid nodules.<h4>Design, setting, and participants</h4>A blinded validation study was conducted on a set of cytologically indeterminate thyroid nodules collected by fine- ...[more]