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      Performance of a Genomic Sequencing Classifier for the Preoperative Diagnosis of Cytologically Indeterminate Thyroid Nodules

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          Abstract

          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.

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          THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL

          C. J. CLOPPER, E. PEARSON (1934)
          Article 0 comments Cited 771 times – based on 0 reviews     Review now
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            The Bethesda System for Reporting Thyroid Cytopathology: a meta-analysis.

            Massimo Bongiovanni, Alessandra Spitale, William Faquin … (2012)
            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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              Is Open Access

              Cross-validation pitfalls when selecting and assessing regression and classification models

              Damjan Krstajic, Ljubomir J Buturovic, David Leahy … (2014)
              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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                Author and article information

                Journal
                Title: JAMA Surgery
                Abbreviated Title: JAMA Surg
                Publisher: American Medical Association (AMA)
                ISSN (Print): 2168-6254
                Publication date Created: September 01 2018
                Publication date (Print): September 01 2018
                Volume: 153
                Issue: 9
                Page: 817
                Affiliations
                [1 ]Division of Endocrine Surgery, Department of Surgery, New York University Langone Medical Center, New York
                [2 ]Division of Endocrinology, Diabetes, and Hypertension, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
                [3 ]Department of Research and Development, Veracyte Inc, San Francisco, California
                [4 ]Department of Medical Affairs, Veracyte Inc, San Francisco, California
                [5 ]Department of Clinical Affairs, Veracyte Inc, San Francisco, California
                [6 ]Texas Diabetes and Endocrinology, Austin
                [7 ]Section of Endocrine Surgery, Department of Surgery, University of California, San Francisco
                [8 ]Division of Head and Neck Pathology, Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York
                [9 ]The Memorial Center for Integrative Endocrine Surgery, Hollywood, Florida
                [10 ]The Memorial Center for Integrative Endocrine Surgery, Weston, Florida
                [11 ]The Memorial Center for Integrative Endocrine Surgery, Boca Raton, Florida
                [12 ]Anatomic Pathology Division, Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia
                [13 ]Division of Thyroid and Parathyroid Endocrine Surgery, Department of Otolaryngology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston
                [14 ]Head and Neck Pathology Subspecialty, Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston
                [15 ]Endocrine Associates of Long Island, Smithtown, New York
                [16 ]Section of Endocrine Surgery, Department of Surgery, Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina
                [17 ]Thyroid Cytopathology Partners, Austin, Texas
                [18 ]Department of Surgery, Endocrine Surgery Program, David Geffen School of Medicine at UCLA, University of California, Los Angeles
                [19 ]Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
                Article
                DOI: 10.1001/jamasurg.2018.1153
                PMC ID: 6583881
                PubMed ID: 29799911
                SO-VID: 2680fb46-eddb-4b59-8e75-5c6b3c0e5dd4
                Copyright © © 2018
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