1 Scopus citations

Abstract

Introduction: Interpretation of high-resolution CT images plays an important role in the diagnosis and management of interstitial lung diseases. However, interreader variation may exist due to varying levels of training and expertise. This study aims to evaluate interreader variation and the role of thoracic radiology training in classifying interstitial lung disease (ILD). Methods: This is a retrospective study where seven physicians (radiologists, thoracic radiologists, and a pulmonologist) classified the subtypes of ILD of 128 patients from a tertiary referral center, all selected from the Interstitial Lung Disease Registry which consists of patients from November 2014 to January 2021. Each patient was diagnosed with a subtype of interstitial lung disease by a consensus diagnosis from pathology, radiology, and pulmonology. Each reader was provided with only clinical history, only CT images, or both. Reader sensitivity and specificity and interreader agreements using Cohen's κ were calculated. Results: Interreader agreement based only on clinical history, only on radiologic information, or combination of both was most consistent amongst readers with thoracic radiology training, ranging from fair (Cohen's κ: 0.2–0.46), moderate to almost perfect (Cohen's κ: 0.55–0.92), and moderate to almost perfect (Cohen's κ: 0.53–0.91) respectively. Radiologists with any thoracic training showed both increased sensitivity and specificity for NSIP as compared to other radiologists and the pulmonologist when using only clinical history, only CT information, or combination of both (p < 0.05). Conclusions: Readers with thoracic radiology training showed the least interreader variation and were more sensitive and specific at classifying certain subtypes of ILD. Summary sentence: Thoracic radiology training may improve sensitivity and specificity in classifying ILD based on HRCT images and clinical history.

Original languageEnglish
Pages (from-to)14-21
Number of pages8
JournalClinical Imaging
Volume97
DOIs
StatePublished - May 2023

Keywords

  • Artificial intelligence
  • Cardiothoracic
  • Interreader agreement
  • Interstitial lung disease
  • Pulmonary fibrosis

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