Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2015 Mar;30(2):130-8.
doi: 10.1097/RTI.0000000000000140.

Lung nodule and cancer detection in computed tomography screening

Geoffrey D Rubin  1
Affiliations
Review

Lung nodule and cancer detection in computed tomography screening

Geoffrey D Rubin. J Thorac Imaging. 2015 Mar.

Abstract

Fundamental to the diagnosis of lung cancer in computed tomography (CT) scans is the detection and interpretation of lung nodules. As the capabilities of CT scanners have advanced, higher levels of spatial resolution reveal tinier lung abnormalities. Not all detected lung nodules should be reported; however, radiologists strive to detect all nodules that might have relevance to cancer diagnosis. Although medium to large lung nodules are detected consistently, interreader agreement and reader sensitivity for lung nodule detection diminish substantially as the nodule size falls below 8 to 10 mm. The difficulty in establishing an absolute reference standard presents a challenge to the reliability of studies performed to evaluate lung nodule detection. In the interest of improving detection performance, investigators are using eye tracking to analyze the effectiveness with which radiologists search CT scans relative to their ability to recognize nodules within their search path in order to determine whether strategies might exist to improve performance across readers. Beyond the viewing of transverse CT reconstructions, image processing techniques such as thin-slab maximum-intensity projections are used to substantially improve reader performance. Finally, the development of computer-aided detection has continued to evolve with the expectation that one day it will serve routinely as a tireless partner to the radiologist to enhance detection performance without significant prolongation of the interpretive process. This review provides an introduction to the current understanding of these varied issues as we enter the era of widespread lung cancer screening.

PubMed Disclaimer

Figures

Figure 1
Figure 1
This 1.25-mm thick CT section demonstrates an approximately 2-mm lung nodule (arrow). Nodules such as this or smaller can be found in virtually every CT scan obtained beyond young adulthood. Because nodules of this size are overwhelmingly benign there is no relevance to their identification unless disseminated or presenting as a new finding in a patient with documented malignancy. There is essentially no role for reporting this nodule within the context of lung cancer screening.
Figure 2
Figure 2
Two examples of 12 mm lung nodules from two different patients. The nodule on the left is not associated with normal lung structures in its vicinity and thus is easy to detect. The nodule on the right (arrow) is closely associated with 5–8 mm pulmonary blood vessels. Although it is substantially larger than adjacent blood vessels, it appears less conspicuous than the nodule at left.
Figure 3
Figure 3
Five mm lung nodules (red circles). The number in the upper left corner corresponds to the number of radiologists out of 13 who detected the nodule. Differences in the regional lung complexity and the cross-sectional area of the lungs on these cross-sections may be a key determinate in their detection.
Figure 4
Figure 4
Plots of gaze paths from two readers (top and bottom rows) evaluating the same stack of 1.25-mm thick CT sections for lung nodules. The duration of the search is divided into five segments and colored red, orange, green, aqua, and blue from beginning to end of the search. Plots on the left show the section number displayed from beginning to end of search. Plots on the right show the gaze point over time projected through all transverse sections. The first reader (top row) is classified as a scanner. He starts at the top of the lungs scanning the anterior portion of both lungs before moving inferiorly. When reaching the bottom of the lungs, the reader reverses direction scanning laterally in the posterior aspect of the lungs. The second reader (bottom row) completes nine passes through the lungs, searching the right lung over four passes before moving to the left lung for three passes before returning to the right lung for two more passes. This pattern has been classified as a “drilling” search pattern. The scan contained 4 5-mm nodules in the right lung and one in the left lung. These search patterns were consistent for the two readers across 40 datasets. Nodule detection sensitivity across 40 scans for the two readers was 46% and 73%, respectively.
Figure 5
Figure 5
A 1.25-mm thick transverse CT section revels two 4-mm rounded opacities appearing as lung nodules (arrows in top image). A 7-mm thick TS-MIP centered on the transverse section above reveals that only the posterior of the two opacities is a lung nodule (arrow in bottom image) and the anterior opacity corresponds to a part of a normal blood vessel (bottom).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Aberle DR, DeMello S, Berg CD, et al. Results of the two incidence screenings in the National Lung Screening Trial. The New England journal of medicine. 2013;369(10):920–31. - PMC - PubMed
    1. Rubin GD, Roos JE, Tall M, et al. Characterizing Search, Recognition, and Decision in the Detection of Lung Nodules on CT Scans: Elucidation with Eye Tracking. Radiology. 2014:132918. - PubMed
    1. Fischbach F, Knollmann F, Griesshaber V, Freund T, Akkol E, Felix R. Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness. European radiology. 2003;13(10):2378–83. - PubMed
    1. Sinsuat M, Saita S, Kawata Y, et al. Influence of slice thickness on diagnoses of pulmonary nodules using low-dose CT: potential dependence of detection and diagnostic agreement on features and location of nodule. Academic radiology. 2011;18(5):594–604. - PubMed
    1. Kazerooni EA, Austin JHM, Black WC, et al. ACR-STR practice parameter for the performance and reporting of lung cancer screening thoracic computed tomography (CT): 2014 (Resolution 4) Journal of thoracic imaging. 2014;29(5):310–6. - PubMed

MeSH terms