Ventricular Geometry From Non-contrast Non-ECG-gated CT Scans

Rahaghi, Farbod N.; Vegas-Sánchez-Ferrero, Gonzalo; Minhas, Jasleen; Come, Carolyn E.; Bruere, Isaac de La; Wells, James M.; González, Germán; Bhatt, Surya P.; Fenster, Brett E.; Díaz, Alejandro A.; Kohli, Puja; Ross, James C.; Lynch, David A.; Bowler, R.P.; Ledesma-Carbayo, María J.; Estépar, Raúl San Jośe; Washko, George R.

doi:10.1016/j.acra.2016.12.007

Cited by 23 publications

(10 citation statements)

References 22 publications

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“…Follow-up of the COPDGene participants is ongoing, and for our study we excluded individuals from the mortality analyses who did not have current follow-up information. The ventricular volume measurements were obtained from prior work, and data in those individuals in whom the measurements could not be performed were excluded from our study (14). Finally, Dlco was measured at one COPDGene site (National Jewish Health, Denver, Colo) in a sample of convenience, and only individuals with Dlco measurements were included in our diffusing capacity analyses (15).…”

Section: Methodsmentioning

confidence: 99%

“…These methods have been described previously and are described in detail in Appendix E1 (online) (4,6,14). …”

Section: Methodsmentioning

confidence: 99%

“…The right ventricular–to–left ventricular (RVLV) volume ratio measurement technique involved the use of a statistical model of the heart’s surface developed from anatomic segmentations obtained from individuals with a range of cardiac diseases and disorders (14). From these surface-fitting models, measures of cardiac morphology, including estimates of both the epicardial (wall and chamber) and endocardial (chamber) volume of each ventricle, can be extracted.…”

Section: Methodsmentioning

confidence: 99%

“…From these surface-fitting models, measures of cardiac morphology, including estimates of both the epicardial (wall and chamber) and endocardial (chamber) volume of each ventricle, can be extracted. For the analyses, the epicardial volumes were used to calculate the RVLV volume ratio (ratio of right ventricular epicardial volume to left ventricular epicardial volume) (14). …”

Section: Methodsmentioning

confidence: 99%

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Interstitial Features at Chest CT Enhance the Deleterious Effects of Emphysema in the COPDGene Cohort

Ash¹,

Harmouche

Ross

et al. 2018

Radiology

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Purpose To determine if interstitial features at chest CT enhance the effect of emphysema on clinical disease severity in smokers without clinical pulmonary fibrosis. Materials and Methods In this retrospective cohort study, an objective CT analysis tool was used to measure interstitial features (reticular changes, honeycombing, centrilobular nodules, linear scar, nodular changes, subpleural lines, and ground-glass opacities) and emphysema in 8266 participants in a study of chronic obstructive pulmonary disease (COPD) called COPDGene (recruited between October 2006 and January 2011). Additive differences in patients with emphysema with interstitial features and in those without interstitial features were analyzed by using t tests, multivariable linear regression, and Kaplan-Meier analysis. Multivariable linear and Cox regression were used to determine if interstitial features modified the effect of continuously measured emphysema on clinical measures of disease severity and mortality. Results Compared with individuals with emphysema alone, those with emphysema and interstitial features had a higher percentage predicted forced expiratory volume in 1 second (absolute difference, 6.4%; P < .001), a lower percentage predicted diffusing capacity of lung for carbon monoxide (Dlco) (absolute difference, 7.4%; P = .034), a 0.019 higher right ventricular–to–left ventricular (RVLV) volume ratio (P = .029), a 43.2-m shorter 6-minute walk distance (6MWD) (P < .001), a 5.9-point higher St George’s Respiratory Questionnaire (SGRQ) score (P < .001), and 82% higher mortality (P < .001). In addition, interstitial features modified the effect of emphysema on percentage predicted Dlco, RVLV volume ratio, 6WMD, SGRQ score, and mortality (P for interaction < .05 for all). Conclusion In smokers, the combined presence of interstitial features and emphysema was associated with worse clinical disease severity and higher mortality than was emphysema alone. In addition, interstitial features enhanced the deleterious effects of emphysema on clinical disease severity and mortality.

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Section: Methodsmentioning

confidence: 99%

“…These methods have been described previously and are described in detail in Appendix E1 (online) (4,6,14). …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Interstitial Features at Chest CT Enhance the Deleterious Effects of Emphysema in the COPDGene Cohort

Ash¹,

Harmouche

Ross

et al. 2018

Radiology

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“…The Agatston score is a well stablished metric used to measure the extent of CAD disease in ECG-gated CT studies 2 . Recently, a clinically meaningful correlation has been shown between the Agatston score computed in cardiac ECG-gated CT and in non ECG-gated chest CTs 3 , enabling the analysis of cardiovascular disease on chest CT studies 4 .…”

Section: Introductionmentioning

confidence: 99%

Automated Agatston score computation in non-ECG gated CT scans using deep learning

Cano-Espinosa¹,

González

Washko

et al. 2018

Medical Imaging 2018: Image Processing

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Introduction: The Agatston score is a well-established metric of cardiovascular disease related to clinical outcomes. It is computed from CT scans by a) measuring the volume and intensity of the atherosclerotic plaques and b) aggregating such information in an index. Objective: To generate a convolutional neural network that inputs a non-contrast chest CT scan and outputs the Agatston score associated with it directly, without a prior segmentation of Coronary Artery Calcifications (CAC). Materials and methods: We use a database of 5973 non-contrast non-ECG gated chest CT scans where the Agatston score has been manually computed. The heart of each scan is cropped automatically using an object detector. The database is split in 4973 cases for training and 1000 for testing. We train a 3D deep convolutional neural network to regress the Agatston score directly from the extracted hearts. Results: The proposed method yields a Pearson correlation coefficient of r = 0.93; p ≤ 0.0001 against manual reference standard in the 1000 test cases. It further stratifies correctly 72.6% of the cases with respect to standard risk groups. This compares to more complex state-of-the-art methods based on prior segmentations of the CACs, which achieve r = 0.94 in ECG-gated pulmonary CT. Conclusions: A convolutional neural network can regress the Agatston score from the image of the heart directly, without a prior segmentation of the CACs. This is a new and simpler paradigm in the Agatston score computation that yields similar results to the state-of-the-art literature.

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