European journal of radiology open · 2026
Classifying diagnostic pitfalls in juxtapleural CT lesions: A root cause.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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Lai Kunquan, Huang Xinhuan, Lai Huan, Chen Huixian, Huang Suiqiao, Xu Honggang, Wu Yongshun
Аннотация
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.
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.