Molecular Classification of Thyroid Nodules Using High-Dimensionality Genomic Data
| Autor: | C. Charles Wang, C. Ted Rigl, Eric T. Wang, Moraima Pagan, Ed Tom, Richard B. Lanman, Juan Rosai, Virginia A. LiVolsi, Jessica D. Reynolds, Jonathan I. Wilde, Hui Wang, Giulia C. Kennedy, Martha A. Zeiger, Lyssa Friedman, Giovanni Fellegara, Electron Kebebew, Nusrat Rabbee, Darya Chudova, Camila M. Egidio |
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| Rok vydání: | 2010 |
| Předmět: |
Thyroid nodules
medicine.medical_specialty Pathology Transcription Genetic Endocrinology Diabetes and Metabolism Biopsy Fine-Needle Clinical Biochemistry Biochemistry Endocrinology Molecular classification Text mining Artificial Intelligence Internal medicine Biopsy medicine Humans RNA Messenger Thyroid Nodule Oligonucleotide Array Sequence Analysis medicine.diagnostic_test business.industry Biochemistry (medical) Thyroid Genetic Variation Reproducibility of Results Genomics medicine.disease medicine.anatomical_structure Gene Expression Regulation ROC Curve Cytopathology Test set business Indeterminate Algorithms |
| Zdroj: | The Journal of Clinical Endocrinology & Metabolism. 95:5296-5304 |
| ISSN: | 1945-7197 0021-972X |
| Popis: | We set out to develop a molecular test that distinguishes benign and malignant thyroid nodules using fine-needle aspirates (FNA).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.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.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. |
| Databáze: | OpenAIRE |
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