The Danish Randomized Lung Cancer CT Screening Trial—Overall Design and Results of the Prevalence Round

Pedersen, Jesper Holst; Ashraf, Haseem; Dirksen, Asger; Bach, Karen S.; Hansen, Hanne H.; Toennesen, P.; Thorsen, Hanne; Brodersen, John; Skov, Birgit Guldhammer; Døssing, Martin; Mortensen, Jann; Richter, Klaus; Clementsen, Paul Frost; Seersholm, Niels

doi:10.1097/jto.0b013e3181a0d98f

Cited by 380 publications

(294 citation statements)

References 29 publications

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“…In addition, many screening programs have adopted a 5 mm diameter nodule size as the cutoff between positive and negative nodules on CT 1 , 2 , 3 , 4 , 5 , 6 , 7 . Therefore, we used 6 mm HGGOs and analyzed the relationship between the visibility, SNR M 2 and CTDI vol .…”

Section: Methodsmentioning

confidence: 99%

“…Low‐dose computed tomography (CT) for lung cancer screening has been well established as a more effective method of detecting nodules and lung cancers, including early‐stage cancers, than plain chest radiography 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 . In low‐dose CT for lung cancer screening (LDCT), the detection task is typically the observation of focal lung opacities or nodules.…”

Section: Introductionmentioning

confidence: 99%

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A phantom study investigating the relationship between ground‐glass opacity visibility and physical detectability index in low‐dose chest computed tomography

Ichikawa

Kobayashi

Sagawa

et al. 2015

J Applied Clin Med Phys

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In this study, the relationship between ground‐glass opacity (GGO) visibility and physical detectability index in low‐dose computed tomography (LDCT) for lung cancer screening was investigated. An anthropomorphic chest phantom that included synthetic GGOs with CT numbers of ‐630 Hounsfield units (HU; high attenuation GGO: HGGO) and ‐800 HU (low attenuation GGO: LGGO), and three phantoms for physical measurements were employed. The phantoms were scanned using 12 CT systems located in 11 screening centers in Japan. The slice thicknesses and CT dose indices (CTDIvol) varied over 1.0–5.0 mm and 0.85–3.30 mGy, respectively, and several reconstruction kernels were used. Physical detectability index values were calculated from measurements of resolution, noise, and slice thickness properties for all image sets. Five radiologists and one thoracic surgeon, blind to one another's observations, evaluated GGO visibility using a five‐point scoring system. The physical detectability index correlated reasonably well with the GGO visibility (R2=0.709,p<0.01 for 6 mm HGGO and R2=0.646,p<0.01 for 10 mm LGGO), and was nearly proportional to the CTDIvol. Consequently, the CTDIvol also correlated reasonably well with the GGO visibility (R2=0.701,p<0.01 for 6 mm HGGO and R2=0.680,p<0.01 for 10 mm LGGO). As a result, the CTDIvol was nearly dominant in the GGO visibility for image sets with different reconstruction kernels and slice thicknesses, used in this study.PACS numbers: 81.70.Tx, 87.57.Q‐

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A phantom study investigating the relationship between ground‐glass opacity visibility and physical detectability index in low‐dose chest computed tomography

Ichikawa

Kobayashi

Sagawa

et al. 2015

J Applied Clin Med Phys

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“…The data consists of 296 low-dose volumetric CT images from 296 participants of the Danish Lung Cancer Screening Trial [19] with the following scan parameters: tube voltage 120 kV, exposure 40 mAs, slice thickness 1 mm, and in-plane resolution ranging from 0.72 to 0.78 mm. 144 images are from subjects without COPD and 152 images are from subjects diagnosed with mild to very severe COPD according to spirometry [14].…”

Section: Datamentioning

confidence: 99%

Dissimilarity-Based Classification of Anatomical Tree Structures

Sørensen

Dirksen

et al. 2011

Lecture Notes in Computer Science

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Abstract. A novel method for classification of abnormality in anatomical tree structures is presented. A tree is classified based on direct comparisons with other trees in a dissimilarity-based classification scheme. The pair-wise dissimilarity measure between two trees is based on a linear assignment between the branch feature vectors representing those trees. Hereby, localized information in the branches is collectively used in classification and variations in feature values across the tree are taken into account. An approximate anatomical correspondence between matched branches can be achieved by including anatomical features in the branch feature vectors. The proposed approach is applied to classify airway trees in computed tomography images of subjects with and without chronic obstructive pulmonary disease (COPD). Using the wall area percentage (WA%), a common measure of airway abnormality in COPD, as well as anatomical features to characterize each branch, an area under the receiver operating characteristic curve of 0.912 is achieved. This is significantly better than computing the average WA%.

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“…Experiments are conducted using 296 low-dose volumetric CT images (tube voltage 140 kV, exposure 40 mAs, slice thickness 1 mm, and in-plane resolution ranging from 0.72 to 0.78 mm) from 296 di erent (ex-)smokers enrolled in the Danish Lung Cancer Screening Trial [12].…”

Section: Datamentioning

confidence: 99%

Learning COPD Sensitive Filters in Pulmonary CT

Sørensen

Ashraf

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. The standard approaches to analyzing emphysema in computed tomography (CT) images are visual inspection and the relative area of voxels below a threshold (RA). The former approach is subjective and impractical in a large data set and the latter relies on a single threshold and independent voxel information, ignoring any spatial correlation in intensities. In recent years, supervised learning on texture features has been investigated as an alternative to these approaches, showing good results. However, supervised learning requires labeled samples, and these samples are often obtained via subjective and time consuming visual scoring done by human experts. In this work, we investigate the possibility of applying supervised learning using texture measures on random CT samples where the labels are based on external, non-CT measures. We are not targeting emphysema directly, instead we focus on learning textural di erences that discriminate subjects with chronic obstructive pulmonary disease (COPD) from healthy smokers, and it is expected that emphysema plays a major part in this. The proposed texture based approach achieves an 69% classication accuracy which is signi cantly better than RA's 55% accuracy.

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The Danish Randomized Lung Cancer CT Screening Trial—Overall Design and Results of the Prevalence Round

Abstract: Screening may facilitate minimal invasive treatment and can be performed with a relatively low rate of false-positive screen results compared with previous studies on lung cancer screening.

Cited by 380 publications

References 29 publications

A phantom study investigating the relationship between ground‐glass opacity visibility and physical detectability index in low‐dose chest computed tomography

A phantom study investigating the relationship between ground‐glass opacity visibility and physical detectability index in low‐dose chest computed tomography

Dissimilarity-Based Classification of Anatomical Tree Structures

Learning COPD Sensitive Filters in Pulmonary CT

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