The blind spots on chest computed tomography: what do we miss
- PMID: 39831206
- PMCID: PMC11740042
- DOI: 10.21037/jtd-24-1125
The blind spots on chest computed tomography: what do we miss
Abstract
Chest computed tomography (CT) is the most frequently performed imaging examination worldwide. Compared with chest radiography, chest CT greatly improves the detection rate and diagnostic accuracy of chest lesions because of the absence of overlapping structures and is the best imaging technique for the observation of chest lesions. However, there are still frequently missed diagnoses during the interpretation process, especially in certain areas or "blind spots", which may possibly be overlooked by radiologists. Awareness of these blind spots is of great significance to avoid false negative results and potential adverse consequences for patients. In this review, we summarize the common blind spots identified in actual clinical practice, encompassing the central areas within the pulmonary parenchyma (including the perihilar regions, paramediastinal regions, and operative area after surgery), trachea and bronchus, pleura, heart, vascular structure, external mediastinal lymph nodes, thyroid, osseous structures, breast, and upper abdomen. In addition to careful review, clinicians can employ several techniques to mitigate or minimize errors arising from these blind spots in film interpretation and reporting. In this review, we also propose technical methods to reduce missed diagnoses, including advanced imaging post-processing techniques such as multiplanar reconstruction (MPR), maximum intensity projection (MIP), artificial intelligence (AI) and structured reporting which can significantly enhance the detection of lesions and improve diagnostic accuracy.
Keywords: Diagnostic imaging; blind spots; chest computed tomography (chest CT).
2024 AME Publishing Company. All rights reserved.
Conflict of interest statement
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1125/coif). The authors have no conflicts of interest to declare.
Figures










Similar articles
-
Blind spots in brain imaging: a pictorial essay.Quant Imaging Med Surg. 2025 Jan 2;15(1):1023-1039. doi: 10.21037/qims-24-1270. Epub 2024 Dec 30. Quant Imaging Med Surg. 2025. PMID: 39839019 Free PMC article. Review.
-
Cause determination of missed lung nodules and impact of reader training and education: Simulation study with nodule insertion software.J Cancer Res Ther. 2020 Jul-Sep;16(4):780-787. doi: 10.4103/jcrt.JCRT_312_17. J Cancer Res Ther. 2020. PMID: 32930118
-
Blind spots on CT imaging of the head: Insights from 5 years of report addenda at a single institution.Clin Imaging. 2021 Aug;76:189-194. doi: 10.1016/j.clinimag.2021.04.026. Epub 2021 Apr 29. Clin Imaging. 2021. PMID: 33957385
-
Did I miss that: subtle and commonly missed findings on chest radiographs.Curr Probl Diagn Radiol. 2015 May-Jun;44(3):277-89. doi: 10.1067/j.cpradiol.2014.09.003. Epub 2014 Oct 30. Curr Probl Diagn Radiol. 2015. PMID: 25445879 Review.
-
Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation.Eur Radiol. 2024 Sep;34(9):5876-5885. doi: 10.1007/s00330-024-10676-w. Epub 2024 Mar 11. Eur Radiol. 2024. PMID: 38466390 Free PMC article.
References
Publication types
LinkOut - more resources
Full Text Sources