Literature DB >> 31952456

Inter- and Intraobserver Agreement in the Assessment of Thyroid Nodule Ultrasound Features and Classification Systems: A Blinded Multicenter Study.

Agnese Persichetti1, Enrico Di Stasio2, Carmela Coccaro1, Filomena Graziano1, Antonio Bianchini3, Vincenzo Di Donna4, Salvatore Corsello4, Dario Valle3, Giancarlo Bizzarri3, Andrea Frasoldati5, Alfredo Pontecorvi4, Enrico Papini1, Rinaldo Guglielmi1.   

Abstract

Background: Single-center trials demonstrated moderate-substantial level of interobserver agreement in the evaluation of ultrasound (US) features of thyroid nodules. Multicenter studies on US agreement, however, are scanty, and data on intraobserver agreement are poor. Aim of the study was to assess inter- and intraobserver agreement between different thyroid centers and different specialists.
Methods: A blinded analysis of 100 electronically recorded thyroid nodule US images was conducted in three large-volume thyroid centers by seven radiologists and endocrinologists. The evaluation was repeated after randomization 4 months later. The following US characteristics were evaluated: composition, echogenicity, margins, intranodular echogenic spots, vascularity, and shape. Thyroid nodules were also classified according to AACE/ACE/AME, EU-TIRADS, ATA, and ACR-TIRADS US classifications. Intra- and interobserver agreement was calculated using cross-tabulation expressed as mean Cohen's Kappa.
Results: Interobserver agreement for US features: K-coefficient was 0.53 for composition, 0.47 for echogenicity, 0.46 for intranodular vascularity, and 0.33 for margins of the nodules. For echogenic foci, the K-coefficient was 0.47 for microcalcifications, 0.38 for macrocalcifications, 0.11 for the subcategory comet-tail artifacts, and 0.42 for shape. Operators resulted uncertain on hyperechoic foci definition in 16% of cases and described them as "hyperechoic foci of uncertain significance." Interobserver Cohen-K for US classification systems was 0.44 for AACE, 0.42 for ACR-TIRADS, 0.39 EU-TIRADS, and 0.34 for ATA. Intraobserver agreement: the K-coefficient for nodule US features was 0.62 for intranodular vascularity, 0.58 for composition, 0.60 for echogenicity, 0.54 for macrocalcifications, 0.55 for microcalcifications, 0.47 for comet tails, 0.39 for margins, and 0.35 for shape. Intraobserver Cohen-K for US classification systems was 0.54 for AACE, 0.49 for ACR-TIRADS, 0.38 for ATA, and 0.33 for EU-TIRADS. Conclusions: Intraobserver reproducibility for thyroid nodule US reporting and US classification systems appears fairly adequate, while the interobserver agreement between different centers is lower than that assessed in single-center trials. Reporting and rating ability of thyroid US examiners still appear not consistent. An unified lexicon of thyroid US features, a simplified method of classification, and a dedicated training in the description of thyroid US findings may increase the observers' agreement and the predictive value of US classification systems in real world practice.

Entities:  

Keywords:  interobserver agreement; intraobserver agreement; thyroid nodule; thyroid ultrasound report; ultrasound classification systems; ultrasound features

Mesh:

Year:  2020        PMID: 31952456     DOI: 10.1089/thy.2019.0360

Source DB:  PubMed          Journal:  Thyroid        ISSN: 1050-7256            Impact factor:   6.568


  14 in total

1.  Software-Based Analysis of the Taller-Than-Wide Feature of High-Risk Thyroid Nodules.

Authors:  Ming-Hsun Wu; Kuen-Yuan Chen; Argon Chen; Chiung-Nien Chen
Journal:  Ann Surg Oncol       Date:  2021-01-03       Impact factor: 5.344

2.  Ultrasound-Based Indications for Thyroid Fine-Needle Aspiration: Outcome of a TIRADS-Based Approach versus Operators' Expertise.

Authors:  Tamas Solymosi; Laszlo Hegedüs; Steen Joop Bonnema; Andrea Frasoldati; Laszlo Jambor; Gabor Laszlo Kovacs; Enrico Papini; Karoly Rucz; Gilles Russ; Zsolt Karanyi; Endre V Nagy
Journal:  Eur Thyroid J       Date:  2020-11-25

3.  Automated liver tumor detection in abdominal ultrasonography with a modified faster region-based convolutional neural networks (Faster R-CNN) architecture.

Authors:  Kenji Karako; Yuichiro Mihara; Junichi Arita; Akihiko Ichida; Sung Kwan Bae; Yoshikuni Kawaguchi; Takeaki Ishizawa; Nobuhisa Akamatsu; Junichi Kaneko; Kiyoshi Hasegawa; Yu Chen
Journal:  Hepatobiliary Surg Nutr       Date:  2022-10       Impact factor: 8.265

4.  Clinician Agreement on the Classification of Thyroid Nodules Ultrasound Features: A Survey of 2 Endocrine Societies.

Authors:  Nydia Burgos; Jing Zhao; Juan P Brito; Jenny K Hoang; Fabian Pitoia; Spyridoula Maraka; M Regina Castro; Ji-Hyun Lee; Naykky Singh Ospina
Journal:  J Clin Endocrinol Metab       Date:  2022-07-14       Impact factor: 6.134

Review 5.  Contemporary Thyroid Nodule Evaluation and Management.

Authors:  Giorgio Grani; Marialuisa Sponziello; Valeria Pecce; Valeria Ramundo; Cosimo Durante
Journal:  J Clin Endocrinol Metab       Date:  2020-09-01       Impact factor: 5.958

Review 6.  Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives.

Authors:  Ling-Rui Li; Bo Du; Han-Qing Liu; Chuang Chen
Journal:  Front Oncol       Date:  2021-02-09       Impact factor: 6.244

Review 7.  2021 Korean Thyroid Imaging Reporting and Data System and Imaging-Based Management of Thyroid Nodules: Korean Society of Thyroid Radiology Consensus Statement and Recommendations.

Authors:  Eun Ju Ha; Sae Rom Chung; Dong Gyu Na; Hye Shin Ahn; Jin Chung; Ji Ye Lee; Jeong Seon Park; Roh-Eul Yoo; Jung Hwan Baek; Sun Mi Baek; Seong Whi Cho; Yoon Jung Choi; Soo Yeon Hahn; So Lyung Jung; Ji-Hoon Kim; Seul Kee Kim; Soo Jin Kim; Chang Yoon Lee; Ho Kyu Lee; Jeong Hyun Lee; Young Hen Lee; Hyun Kyung Lim; Jung Hee Shin; Jung Suk Sim; Jin Young Sung; Jung Hyun Yoon; Miyoung Choi
Journal:  Korean J Radiol       Date:  2021-10-26       Impact factor: 3.500

8.  Explore the Diagnostic Efficiency of Chinese Thyroid Imaging Reporting and Data Systems by Comparing With the Other Four Systems (ACR TI-RADS, Kwak-TIRADS, KSThR-TIRADS, and EU-TIRADS): A Single-Center Study.

Authors:  Qi Qi; Aiyun Zhou; Suping Guo; Xingzhi Huang; Songli Chen; Yaohui Li; Pan Xu
Journal:  Front Endocrinol (Lausanne)       Date:  2021-10-27       Impact factor: 5.555

9.  Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes.

Authors:  M Han; E J Ha; J H Park
Journal:  AJNR Am J Neuroradiol       Date:  2020-12-24       Impact factor: 3.825

10.  The Added Value of Operator's Judgement in Thyroid Nodule Ultrasound Classification Arising From Histologically Based Comparison of Different Risk Stratification Systems.

Authors:  Bruno Madeo; Giulia Brigante; Anna Ansaloni; Erica Taliani; Shaniko Kaleci; Maria Laura Monzani; Manuela Simoni; Vincenzo Rochira
Journal:  Front Endocrinol (Lausanne)       Date:  2020-07-07       Impact factor: 5.555

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