The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules
Open accessAbstract
BACKGROUND: Molecular tests have clinical utility for thyroid nodules with indeterminate fine‐needle aspiration (FNA) cytology, although their performance requires further improvement. This study evaluated the analytical performance of the newly created ThyroSeq v3 test. METHODS: ThyroSeq v3 is a DNA‐ and RNA‐based next‐generation sequencing assay that analyzes 112 genes for a variety of genetic alterations, including point mutations, insertions/deletions, gene fusions, copy number alterations, and abnormal gene expression, and it uses a genomic classifier (GC) to separate malignant lesions from benign lesions. It was validated in 238 tissue samples and 175 FNA samples with known surgical follow‐up. Analytical performance studies were conducted. RESULTS: In the training tissue set of samples, ThyroSeq GC detected more than 100 genetic alterations, including BRAF, RAS, TERT, and DICER1 mutations, NTRK1/3, BRAF, and RET fusions, 22q loss, and gene expression alterations. GC cutoffs were established to distinguish cancer from benign nodules with 93.9% sensitivity, 89.4% specificity, and 92.1% accuracy. This correctly classified most papillary, follicular, and Hurthle cell lesions, medullary thyroid carcinomas, and parathyroid lesions. In the FNA validation set, the GC sensitivity was 98.0%, the specificity was 81.8%, and the accuracy was 90.9%. Analytical accuracy studies demonstrated a minimal required nucleic acid input of 2.5 ng, a 12% minimal acceptable tumor content, and reproducible test results under variable stress conditions. CONCLUSIONS: The ThyroSeq v3 GC analyzes 5 different classes of molecular alterations and provides high accuracy for detecting all common types of thyroid cancer and parathyroid lesions. The analytical sensitivity, specificity, and robustness of the test have been successfully validated and indicate its suitability for clinical use. Cancer 2018;124:1682‐90. © 2018 American Cancer Society
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Self-citationApproximate is Better than “Exact” for Interval Estimation of Binomial Proportions
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Integrated Genomic Characterization of Papillary Thyroid Carcinoma
Agrawal, N., Akbani, R., Aksoy, B. A., Ally, A., Arachchi, H., Asa, S. L., Auman, J. T., Balasundaram, M., Balu, S., Baylin, S. B., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.050 · Cited by 3,062
Self-citationThe Bethesda System for Reporting Thyroid Cytopathology
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Self-citationGenomic and transcriptomic hallmarks of poorly differentiated and anaplastic thyroid cancers
Landa, I., Ibrahimpasic, T., Boucai, L., Sinha, R., Knauf, J. A., Shah, R. H., Dogan, S., Ricarte-Filho, J. C., Krishnamoorthy, G. P., Xu, B., et al. (2016) · Journal of Clinical Investigation · doi:10.1172/jci85271 · Cited by 1,284
Korea's Thyroid-Cancer “Epidemic” — Screening and Overdiagnosis
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Preoperative Diagnosis of Benign Thyroid Nodules with Indeterminate Cytology
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Worldwide Thyroid-Cancer Epidemic? The Increasing Impact of Overdiagnosis
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Cited by
Steward, D. L., Carty, S. E., Sippel, R. S., Yang, S. P., Sosa, J. A., Sipos, J. A., Figge, J. J., Mandel, S., Haugen, B. R., Burman, K. D., et al. (2019) · JAMA Oncology · doi:10.1001/jamaoncol.2018.4616 · Cited by 486
Self-citationHa, E. J., Chung, S. R., Na, D. G., Ahn, H. S., Chung, J., Lee, J. Y., Park, J. S., Yoo, R.-E., Baek, J. H., Baek, S. M., et al. (2021) · Korean Journal of Radiology · doi:10.3348/kjr.2021.0713 · Cited by 298
Shonka, D. C., Ho, A., Chintakuntlawar, A. V., Geiger, J. L., Park, J. C., Seetharamu, N., Jasim, S., Abdelhamid Ahmed, A. H., Bible, K. C., Brose, M. S., et al. (2022) · Head & Neck · doi:10.1002/hed.27025 · Cited by 117
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Metadata
- Authors
- Nikiforova, M. N., Mercurio, S., Wald, A. I., Barbi de Moura, M., Callenberg, K., Santana‐Santos, L., Gooding, W. E., Yip, L., Ferris, R. L., & Nikiforov, Y. E.
- Venue
- Cancer
- Publisher
- Wiley
- Published
- 2018-01-18
- Volume
- 124
- Issue
- 8
- Pages
- 1682-1690
- ISSN
- 0008-543X (ISSN-L)
- 1097-0142
- OpenAlex
- W2792088630
- Cited by
- 396
- References
- 22
- FWCI
- 31.72
- Type
- journal article
- Language
- en
- Open access
- bronze
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