ArticleEuropean journal of radiology open2026
Classifying diagnostic pitfalls in juxtapleural CT lesions: A root cause.
Article in European journal of radiology open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: To develop and assess the feasibility of a root-cause analysis (RCA) framework for classifying diagnostic pitfalls in the computed tomography (CT) interpretation of juxtapleural lesions. Methods: This single-center, retrospective, exploratory taxonomy-development study was approved by our Institutional Review Board with a waiver of informed consent. To focus on the underlying mechanisms of diagnostic failure rather than overall institutional performance, we analyzed a cohort deliberately enriched for diagnostic difficulty. We analyzed 20 patients (mean age, 55.1 ± 16.9 years; 11 women) from a county-level hospital whose examinations were performed between December 2021 and March 2024. All patients had a definitive diagnosis established by histopathology/microbiology (n = 18) or conclusive clinical diagnosis (n = 2). Non-concordant cases (n = 18) were subjected to a structured RCA. Two radiologists independently categorized errors into three pitfall types. Results: In this difficulty-enriched, intentionally selected cohort-which is not representative of general diagnostic performance-major or moderate diagnostic discordance was identified in 18 of 20 cases (90.0%; 95% CI: 68.3%, 98.8%). These errors converged into three distinct pitfalls: (1) Interpretive Errors due to Misleading Features, representing the most common type (12/18, 66.7%); (2) Perceptual Errors (3/18, 16.7%); and (3) Errors of Integration (3/18, 16.7%). Inter-rater reliability for this categorization showed substantial agreement (Cohen's κ = 0.68; 95% CI: 0.41, 0.95), though the wide confidence interval reflects the limited sample size. Conclusion: In diagnostically challenging scenarios, errors in the CT interpretation of juxtapleural lesions exhibit systematic and classifiable patterns. Our novel three-pitfall taxonomy provides a preliminary yet promising framework for analyzing these errors, demonstrating feasibility and initial inter-rater reliability. External validation in larger, multicenter, prospective studies is required before this framework can be recommended for clinical implementation, educational deployment, or assessment of impact on diagnostic performance.
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