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Interobserver agreement of various thyroid imaging reporting and data systems.

Giorgio Grani1, Livia Lamartina2, Vito Cantisani3

  • 1Department of Internal Medicine and Medical Specialties'Sapienza' University of Rome, Rome, Italy giorgio.grani@uniroma1.it.

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|December 3, 2017
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Summary

Ultrasonography is key for thyroid nodules. Classification systems improve interobserver agreement for malignancy risk and fine-needle aspiration biopsy recommendations, especially after training.

Keywords:
TIRADSagreementinterobserver variabilityreliabilitythyroid nodule

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Area of Science:

  • Radiology
  • Endocrinology
  • Oncology

Background:

  • Ultrasonography is the primary tool for evaluating thyroid nodules.
  • Significant interobserver variability exists in interpreting ultrasound features.
  • Standardized classification systems aim to improve diagnostic consistency.

Purpose of the Study:

  • To assess interobserver variability among five thyroid nodule classification systems: AACE/ACE/AME, ACR, ATA, EU-TIRADS, and K-TIRADS.
  • To evaluate interobserver agreement in recommending fine-needle aspiration (FNA) biopsy.
  • To determine the impact of consensus training on agreement levels.

Main Methods:

  • Two independent raters evaluated 1055 thyroid nodule ultrasound images from 265 patients.
  • Nodules were assessed using AACE/ACE/AME, ACR, ATA, EU-TIRADS, and K-TIRADS systems.
  • Interobserver agreement was measured using Krippendorff's alpha and Cohen's kappa, with a consensus session after the first set.

Main Results:

  • Initial interobserver agreement (Krippendorff's alpha) varied from 0.47 to 0.61 across systems.
  • Agreement for FNA biopsy recommendations (Cohen's kappa) ranged from substantial (0.61) to near-perfect (0.82).
  • Agreement improved for all systems after consensus training and for a second set of nodules.

Conclusions:

  • While individual ultrasonographic feature descriptions show variability, classification systems enhance interobserver agreement.
  • Specific training and consensus significantly improve the reliability of these systems.
  • The evaluated classification systems demonstrate substantial to near-perfect agreement for guiding FNA biopsy decisions, which is their primary clinical utility.