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      Extending expressed RNA genomics from surgical decision making for cytologically indeterminate thyroid nodules to targeting therapies for metastatic thyroid cancer

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          Abstract

          The Afirma Genomic Sequencing Classifier (GSC) is a rule‐out test for malignancy/noninvasive follicular thyroid neoplasms with papillary‐like nuclear features among patients with Bethesda category III/IV nodules, whereas the complimentary Xpression Atlas provides genomic insights from a curated panel of 511 genes among GSC suspicious and Bethesda category V/VI nodules. Together, they facilitate personalized treatment decisions based on genomic insights derived from the transcriptome of the biopsied target and extend the diagnostic and therapeutic reach of cytopathologists and fine‐needle aspiration biopsy sample collection.

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          The Bethesda System for Reporting Thyroid Cytopathology: a meta-analysis.

          We aimed to investigate the validity of the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) through meta-analysis. All publications between January 1, 2008 and September 1, 2011 that studied TBSRTC and had available histological follow-up data were retrieved. To calculate the sensitivity, specificity and diagnostic accuracy, the cases diagnosed as follicular neoplasm, suspicious for malignancy and malignant which were histopathologically confirmed as malignant were defined as true-positive. True-negative included benign cases confirmed as benign on histopathology. The nondiagnostic category was excluded from the statistical calculation. The correlations between the 6 diagnostic categories were investigated. The publications review resulted in a case cohort of 25,445 thyroid fine-needle aspirations, 6,362 (25%) of which underwent surgical excision; this group constituted the basis of the study. The sensitivity, specificity and diagnostic accuracy were 97, 50.7 and 68.8%, respectively. The positive predictive value and negative predictive value were 55.9 and 96.3%, respectively. The rates of false negatives and false positives were low: 3 and 0.5%, respectively. The results of meta-analysis showed high overall accuracy, indicating that TBSRTC represents a reliable and valid reporting system for thyroid cytology. Copyright © 2012 S. Karger AG, Basel.
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            Cross-validation pitfalls when selecting and assessing regression and classification models

            Background We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. Methods We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. Results We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. Conclusions We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.
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              Genetic analysis of 779 advanced differentiated and anaplastic thyroid cancers.

              Purpose: To define the genetic landscape of advanced differentiated and anaplastic thyroid cancer (ATC) and identify genetic alterations of potential diagnostic, prognostic, and therapeutic significance.Experimental Design: The genetic profiles of 583 advanced differentiated and 196 ATCs generated with targeted next-generation sequencing cancer-associated gene panels MSK-IMPACT and FoundationOne were analyzed.Results: ATC had more genetic alterations per tumor, and pediatric papillary thyroid cancer had fewer genetic alterations per tumor when compared with other thyroid cancer types. DNA mismatch repair deficit and activity of APOBEC cytidine deaminases were identified as mechanisms associated with high mutational burden in a subset of differentiated thyroid cancers and ATCs. Copy number losses and mutations of CDKN2A and CDKN2B, amplification of CCNE1, amplification of receptor tyrosine kinase genes KDR, KIT, and PDGFRA, amplification of immune evasion genes CD274, PDCD1LG2, and JAK2, and activating point mutations in small GTPase RAC1 were associated with ATC. An association of KDR, KIT, and PDGFRA amplification with the sensitivity of thyroid cancer cells to lenvatinib was shown in vitro Three genetically distinct types of ATCs are proposed.Conclusions: This large-scale analysis describes genetic alterations in a cohort of thyroid cancers enriched in advanced cases. Many novel genetic events previously not seen in thyroid cancer were found. Genetic alterations associated with anaplastic transformation were identified. An updated schematic of thyroid cancer genetic evolution is proposed. Clin Cancer Res; 24(13); 3059-68. ©2018 AACR.
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                Author and article information

                Contributors
                sali@jhmi.edu
                Journal
                Cancer Cytopathol
                Cancer Cytopathol
                10.1002/(ISSN)1934-6638
                CNCY
                Cancer Cytopathology
                John Wiley and Sons Inc. (Hoboken )
                1934-662X
                1934-6638
                24 April 2019
                June 2019
                : 127
                : 6 ( doiID: 10.1002/cncy.v127.6 )
                : 362-369
                Affiliations
                [ 1 ] Department of Pathology The Johns Hopkins Hospital Baltimore Maryland
                [ 2 ] Department of Radiology The Johns Hopkins Hospital Baltimore Maryland
                [ 3 ] Department of Endocrine Surgery Cleveland Clinic Cleveland Ohio
                [ 4 ] Department of Pathology Massachusetts General Hospital, Harvard Medical School Boston Massachusetts
                [ 5 ] Department of Medicine Florida International University, Memorial Healthcare System Hollywood Florida
                [ 6 ] Department of Clinical Affairs Veracyte Inc South San Francisco California
                [ 7 ] Department of Medical Affairs Veracyte Inc South San Francisco California
                [ 8 ] Department of Research and Development Veracyte Inc South San Francisco California
                [ 9 ] Division of Endocrinology, Diabetes, and Metabolism Department of Medicine Johns Hopkins University School of Medicine Baltimore Maryland
                Author notes
                [*] [* ] Corresponding author: Syed Z. Ali, MD, FRCPath, FIAC, Department of Pathology, Rm 406, Pathology Building, The Johns Hopkins Hospital, 600 N. Wolfe St, Baltimore, MD 21287; sali@ 123456jhmi.edu

                Author information
                https://orcid.org/0000-0003-4183-7387
                Article
                CNCY22132
                10.1002/cncy.22132
                6618055
                31017745
                3ac34303-3aae-4a49-824d-9905f257eba2
                © 2019 The Authors. Cancer Cytopathology published by Wiley Periodicals, Inc. on behalf of American Cancer Society

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 27 February 2019
                : 12 March 2019
                : 14 March 2019
                Page count
                Figures: 2, Tables: 0, Pages: 8, Words: 16527
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                Custom metadata
                2.0
                cncy22132
                June 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.5 mode:remove_FC converted:10.07.2019

                clinical validation,indeterminate cytology,machine learning,molecular diagnostics,thyroid nodule

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