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Objectives <p>To investigate whether a content-based image retrieval (CBIR) of similar chest CT images can help usual interstitial pneumonia (UIP) CT pattern classifications among readers with varying levels of experience.</p> Materials and methods <p>This retrospective study included patients who underwent high-resolution chest CT between 2013 and 2015 for the initial workup for fibrosing interstitial lung disease. UIP classifications were assigned to CT images by three thoracic radiologists, which served as the ground truth. One hundred patients were selected as queries. The CBIR retrieved the top three similar CT images with UIP classifications using a deep learning algorithm. The diagnostic accuracies and inter-reader agreement of nine readers before and after CBIR were evaluated.</p> Results <p>Of 587 patients (mean age, 63 years; 356 men), 100 query cases (26 UIP patterns, 26 probable UIP patterns, 5 indeterminate for UIP, and 43 alternative diagnoses) were selected. After CBIR, the mean accuracy (61.3% to 67.1%; <i>p</i> = 0.011) and inter-reader agreement (Fleiss Kappa, 0.400 to 0.476; <i>p</i> = 0.003) were slightly improved. The accuracies of the radiologist group for all CT patterns except indeterminate for UIP increased after CBIR; however, they did not reach statistical significance. The resident and pulmonologist groups demonstrated mixed results: accuracy decreased for UIP pattern, increased for alternative diagnosis, and varied for others.</p> Conclusion <p>CBIR slightly improved diagnostic accuracy and inter-reader agreement in UIP pattern classifications. However, its impact varied depending on the readers’ level of experience, suggesting that the current CBIR system may be beneficial when used to complement the interpretations of experienced readers.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>CT pattern classification is important for the standardized assessment and management of idiopathic pulmonary fibrosis, but requires radiologic expertise and shows inter-reader variability</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>CBIR slightly improved diagnostic accuracy and inter-reader agreement for UIP CT pattern classifications overall</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>The proposed CBIR system may guide consistent work-up and treatment strategies by enhancing accuracy and inter-reader agreement in UIP CT pattern classifications by experienced readers whose expertise and experience can effectively interact with CBIR results</i>.</p> Graphical Abstract <p></p>

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Influence of content-based image retrieval on the accuracy and inter-reader agreement of usual interstitial pneumonia CT pattern classification

  • 期刊论文

  • 发表日期：

    2025年5月22日

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  • Sohee Park,
  • Hye Jeon Hwang,
  • Jihye Yun,
  • Eun Jin Chae,
  • Jooae Choe,
  • Sang Min Lee,
  • Han Na Lee,
  • So Youn Shin,
  • Heejun Park,
  • Hana Jeong,
  • Min Jee Kim,
  • Jang Ho Lee,
  • Kyung-Wook Jo,
  • Seunghee Baek,
  • Joon Beom Seo

摘要

Objectives

To investigate whether a content-based image retrieval (CBIR) of similar chest CT images can help usual interstitial pneumonia (UIP) CT pattern classifications among readers with varying levels of experience.

Materials and methods

This retrospective study included patients who underwent high-resolution chest CT between 2013 and 2015 for the initial workup for fibrosing interstitial lung disease. UIP classifications were assigned to CT images by three thoracic radiologists, which served as the ground truth. One hundred patients were selected as queries. The CBIR retrieved the top three similar CT images with UIP classifications using a deep learning algorithm. The diagnostic accuracies and inter-reader agreement of nine readers before and after CBIR were evaluated.

Results

Of 587 patients (mean age, 63 years; 356 men), 100 query cases (26 UIP patterns, 26 probable UIP patterns, 5 indeterminate for UIP, and 43 alternative diagnoses) were selected. After CBIR, the mean accuracy (61.3% to 67.1%; p = 0.011) and inter-reader agreement (Fleiss Kappa, 0.400 to 0.476; p = 0.003) were slightly improved. The accuracies of the radiologist group for all CT patterns except indeterminate for UIP increased after CBIR; however, they did not reach statistical significance. The resident and pulmonologist groups demonstrated mixed results: accuracy decreased for UIP pattern, increased for alternative diagnosis, and varied for others.

Conclusion

CBIR slightly improved diagnostic accuracy and inter-reader agreement in UIP pattern classifications. However, its impact varied depending on the readers’ level of experience, suggesting that the current CBIR system may be beneficial when used to complement the interpretations of experienced readers.

Key Points

Question CT pattern classification is important for the standardized assessment and management of idiopathic pulmonary fibrosis, but requires radiologic expertise and shows inter-reader variability.

Findings CBIR slightly improved diagnostic accuracy and inter-reader agreement for UIP CT pattern classifications overall.

Clinical relevance The proposed CBIR system may guide consistent work-up and treatment strategies by enhancing accuracy and inter-reader agreement in UIP CT pattern classifications by experienced readers whose expertise and experience can effectively interact with CBIR results.

Graphical Abstract