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Cited by 274

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Molecular Classification of Thyroid Nodules Using High-Dimensionality Genomic Data

Chudova, Darya, Wilde, Jonathan I., Wang, Eric T., Wang, Hui, Rabbee, Nusrat, Egidio, Camila M., Reynolds, Jessica, Tom, Ed, Pagan, Moraima, Rigl, C. Ted, Friedman, Lyssa, Wang, C. Charles, Lanman, Richard B., Zeiger, Martha, Kebebew, Electron, Rosai, Juan, Fellegara, Giovanni, LiVolsi, Virginia A., Kennedy, Giulia C.·2010·The Journal of Clinical Endocrinology & Metabolism
DOI 10.1210/jc.2010-1087
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Abstract

OBJECTIVE: We set out to develop a molecular test that distinguishes benign and malignant thyroid nodules using fine-needle aspirates (FNA). DESIGN: We used mRNA expression analysis to measure more than 247,186 transcripts in 315 thyroid nodules, comprising multiple subtypes. The data set consisted of 178 retrospective surgical tissues and 137 prospectively collected FNA samples. Two classifiers were trained separately on surgical tissues and FNAs. The performance was evaluated using an independent set of 48 prospective FNA samples, which included 50% with indeterminate cytopathology. RESULTS: Performance of the tissue-trained classifier was markedly lower in FNAs than in tissue. Exploratory analysis pointed to differences in cellular heterogeneity between tissues and FNAs as the likely cause. The classifier trained on FNA samples resulted in increased performance, estimated using both 30-fold cross-validation and an independent test set. On the test set, negative predictive value and specificity were estimated to be 96 and 84%, respectively, suggesting clinical utility in the management of patients considering surgery. Using in silico and in vitro mixing experiments, we demonstrated that even in the presence of 80% dilution with benign background, the classifier can correctly recognize malignancy in the majority of FNA samples. CONCLUSIONS: The FNA-trained classifier was able to classify an independent set of FNAs in which substantial RNA degradation had occurred and in the presence of blood. High tolerance to dilution makes the classifier useful in routine clinical settings where sampling error may be a concern. An ongoing multicenter clinical trial will allow us to validate molecular test performance on a larger independent test set of prospectively collected thyroid FNAs.

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Metadata

Authors
Chudova, D., Wilde, J. I., Wang, E. T., Wang, H., Rabbee, N., Egidio, C. M., Reynolds, J., Tom, E., Pagan, M., Rigl, C. T., Friedman, L., Wang, C. C., Lanman, R. B., Zeiger, M., Kebebew, E., Rosai, J., Fellegara, G., LiVolsi, V. A., & Kennedy, G. C.
Venue
The Journal of Clinical Endocrinology & Metabolism
Publisher
The Endocrine Society
Published
2010-12
Volume
95
Issue
12
Pages
5296-5304
ISSN
  • 0021-972X (ISSN-L)
  • 1945-7197
DOI
10.1210/jc.2010-1087
OpenAlex
W2037050084
Cited by
274
References
18
FWCI
18.05
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Molecular Classification of Thyroid Nodules Using High-Dimensionality Genomic Data. https://app.cochat.ai/papers/10.1210/jc.2010-1087
The paper
Chudova, D., Wilde, J. I., Wang, E. T., Wang, H., Rabbee, N., Egidio, C. M., Reynolds, J., Tom, E., Pagan, M., Rigl, C. T., Friedman, L., Wang, C. C., Lanman, R. B., Zeiger, M., Kebebew, E., Rosai, J., Fellegara, G., LiVolsi, V. A., & Kennedy, G. C. (2010). Molecular Classification of Thyroid Nodules Using High-Dimensionality Genomic Data. The Journal of Clinical Endocrinology & Metabolism. https://doi.org/10.1210/jc.2010-1087

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.