![]() The genomic sequencing classifier had a sensitivity of 91% (95% CI, 79-98) and a specificity of 68% (95% CI, 60-76). 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 secondary end point was measurement of classifier performance in biopsies from Bethesda II, V, and VI nodules. 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. Algorithm development and independent validation occurred between August 2016 and May 2017.Įxposures Thyroid nodule surgical histopathology diagnosis by an expert panel blinded to all genomic data. Of these, 191 samples (91.0%) had adequate residual RNA for validation of the genomic sequencing classifier. 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. All patients underwent surgery without genomic information and were assigned a histopathology diagnosis by an expert panel blinded to all genomic information. Objective To measure the performance of a genomic sequencing classifier for cytologically indeterminate thyroid nodules.ĭesign, 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. 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. ![]() Shared Decision Making and Communication.Scientific Discovery and the Future of Medicine.Health Care Economics, Insurance, Payment.Clinical Implications of Basic Neuroscience.Challenges in Clinical Electrocardiography.Standards for Reporting of Diagnostic Accuracy Studies diagram of sample flow through the study. Afirma gene expression classifier system.ĮFigure 2. Performance comparison between the genomic sequence classifier and gene expression classifier.ĮFigure 1. Prevalence of malignancy between validation cohorts.ĮTable 7. Histology subtype comparison between validation cohorts.ĮTable 6. List of 1115 core genes deriving the ensemble model prediction.ĮTable 5. Feature sets used in each classifier within the final ensemble model.ĮTable 4. Composition of the core ensemble model training set.ĮTable 3. Blinding of the independent test set.ĮTable 2. Genomic sequence classifier to gene expression classifier comparison on a per-samples basis.ĮTable 1. RNA sequencing pipeline, feature extraction, and quality control.ĮAppendix.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |