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JAMA Surg. 2018 Sep 1;153(9):817-824. doi: 10.1001/jamasurg.2018.1153.

Performance of a Genomic Sequencing Classifier for the Preoperative Diagnosis of Cytologically Indeterminate Thyroid Nodules.

Author information

1
Division of Endocrine Surgery, Department of Surgery, New York University Langone Medical Center, New York.
2
Division of Endocrinology, Diabetes, and Hypertension, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
3
Department of Research and Development, Veracyte Inc, San Francisco, California.
4
Department of Medical Affairs, Veracyte Inc, San Francisco, California.
5
Department of Clinical Affairs, Veracyte Inc, San Francisco, California.
6
Texas Diabetes and Endocrinology, Austin.
7
Section of Endocrine Surgery, Department of Surgery, University of California, San Francisco.
8
Division of Head and Neck Pathology, Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York.
9
The Memorial Center for Integrative Endocrine Surgery, Hollywood, Florida.
10
The Memorial Center for Integrative Endocrine Surgery, Weston, Florida.
11
The Memorial Center for Integrative Endocrine Surgery, Boca Raton, Florida.
12
Anatomic Pathology Division, Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia.
13
Division of Thyroid and Parathyroid Endocrine Surgery, Department of Otolaryngology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston.
14
Head and Neck Pathology Subspecialty, Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston.
15
Endocrine Associates of Long Island, Smithtown, New York.
16
Section of Endocrine Surgery, Department of Surgery, Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina.
17
Thyroid Cytopathology Partners, Austin, Texas.
18
Department of Surgery, Endocrine Surgery Program, David Geffen School of Medicine at UCLA, University of California, Los Angeles.
19
Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.

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.

PMID:
29799911
PMCID:
PMC6583881
DOI:
10.1001/jamasurg.2018.1153
[Indexed for MEDLINE]
Free PMC Article

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