Dataset Information

Developing a lung nodule management protocol specifically for cardiac CT: Methodology in the DISCHARGE trial.

ABSTRACT:

Purpose

In this methodology paper we describe the development of a lung nodule management algorithm specifically for patients undergoing cardiac CT.

Methods

We modified the Lung-RADS algorithm specifically to manage lung nodules incidentally detected on cardiac CT (Lung-RADS for cardiac CT). We will evaluate the modified algorithm as part of the DISCHARGE trial (www.dischargetrial.eu) in which patients with suspected coronary artery disease are randomly assigned to cardiac CT or invasive coronary angiography across Europe at 16 sites involving 3546 patients. Patients will be followed for up to four years.

Results

The major adjustments to Lung-RADS specifically for cardiac CT relate to 1) incomplete coverage of the lungs by cardiac CT compared with chest CT, and when to order a completion chest CT versus a follow up chest CT, 2) cardiac CT findings will not trigger annual lung-cancer screening, and 3) a lower threshold of at least 10 mm for classifying new ground glass nodules as probably benign (category 3).

Conclusions

The DISCHARGE trial will assess a lung nodule management algorithm designed specifically for cardiac CT in patients with stable chest pain across Europe.

SUBMITTER: Haase R

PROVIDER: S-EPMC7327416 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Developing a lung nodule management protocol specifically for cardiac CT: Methodology in the DISCHARGE trial.

Haase Robert R Dodd Jonathan D JD Kauczor Hans-Ulrich HU Kazerooni Ella A EA Dewey Marc M

European journal of radiology open 20200625

<h4>Purpose</h4>In this methodology paper we describe the development of a lung nodule management algorithm specifically for patients undergoing cardiac CT.<h4>Methods</h4>We modified the Lung-RADS algorithm specifically to manage lung nodules incidentally detected on cardiac CT (Lung-RADS for cardiac CT). We will evaluate the modified algorithm as part of the DISCHARGE trial (www.dischargetrial.eu) in which patients with suspected coronary artery disease are randomly assigned to cardiac CT or i ...[more]

PMID: 32637465

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Project description:The purpose of this study was to explore the effects of CT slice thickness, reconstruction algorithm, and radiation dose on quantification of CT features to characterize lung nodules using a chest phantom. Spherical lung nodule phantoms of known densities (-630 and + 100 HU) were inserted into an anthropomorphic thorax phantom. CT scan was performed ten times with relocations. CT data were reconstructed using 12 different imaging settings; three different slice thicknesses of 1.25, 2.5, and 5.0 mm, two reconstruction kernels of sharp and standard, and two radiation dose of 30 mAs and 12 mAs. Lesions were segmented using a semiautomated method. Twenty representative CT quantitative features representing CT density and texture were compared using multiple regression analysis. In 100 HU nodule phantoms, 18 and 19 among 20 computer features showed significant difference between different mAs and reconstruction algorithms, respectively (p ≤ 0.05). 20, 19, and 19 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p ≤ 0.05). In -630 HU nodule phantoms, 18 and 19 showed significant difference between different mAs and reconstruction algorithms, respectively (p ≤ 0.05). 18, 11, and 17 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p ≤ 0.05). When comparing the absolute value of regression coefficient, the effect of slice thickness in 100 HU nodule and reconstruction algorithm in -630 HU nodule was greater than the effect of remaining scan parameters. The slice thickness, mAs, and reconstruction algorithm had a significant impact on the quantitative image features. In clinical studies involving deep learning or radiomics, it should be noted that differences in values can occur when using computer features obtained from different CT scan parameters in combination. Therefore, when interpreting the statistical analysis results, it is necessary to reflect the difference in the computer features depending on the scan parameters.

Dataset Information

Developing a lung nodule management protocol specifically for cardiac CT: Methodology in the DISCHARGE trial.

Purpose

Methods

Results

Conclusions

Publications

Developing a lung nodule management protocol specifically for cardiac CT: Methodology in the DISCHARGE trial.

Similar Datasets

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