ReviewJournal of thoracic disease2024
The blind spots on chest computed tomography: what do we miss.
Review in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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