Coronary CT Angiography: Variability of CT Scanners and Readers in Measurement of Plaque Volume

Symons, Rolf; Morris, Justin Z.; Wu, Colin O.; Pourmorteza, Amir; Ahlman, Mark A.; Lima, João A.C.; Chen, Marcus Y; Mallek, Marissa; Sandfort, Veit; Bluemke, David A.

doi:10.1148/radiol.2016161670

Cited by 58 publications

(32 citation statements)

References 44 publications

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“…where  is the significance level, P is the study power, f is the value of the factor for different values of  and P (f = 10.5 for a P of 90% and an  error of 0.05),  is the interstudy standard deviation,  is the desired percentage difference to be detected, and n is the sample size needed (18). Chest CT reproducibility and sample size were calculated for both a visual and an AI-assisted analysis, as defined above.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 at Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials

Gieraerts

Dangis

Janssen

et al. 2020

Radiology: Cardiothoracic Imaging

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To compare the prognostic value and reproducibility of visual versus AI-assisted analysis of lung involvement on submillisievert low-dose chest CT in COVID-19 patients. Materials and Methods: This was a HIPAA-compliant, institutional review board-approved retrospective study. From March 15 to June 1, 2020, 250 RT-PCR confirmed COVID-19 patients were studied with low-dose chest CT at admission. Visual and AI-assisted analysis of lung involvement was performed by using a semi-quantitative CT score and a quantitative percentage of lung involvement. Adverse outcome was defined as intensive care unit (ICU) admission or death. Cox regression analysis, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under the curve (AUROC) analysis was performed to compare model performance. Intraclass correlation coefficients (ICCs) and Bland-Altman analysis was used to assess intra-and interreader reproducibility. Results: Adverse outcome occurred in 39 patients (11 deaths, 28 ICU admissions). AUC values from AI-assisted analysis were significantly higher than those from visual analysis for both semiquantitative CT scores and percentages of lung involvement (all P<0.001). Intrareader and interreader agreement rates were significantly higher for AI-assisted analysis than visual analysis (all ICC 0.960 versus 0.885). AI-assisted variability for quantitative percentage of lung involvement was 17.2% (coefficient of variation) versus 34.7% for visual analysis. The sample size to detect a 5% change in lung involvement with 90% power and an  error of 0.05 was 250 patients with AI-assisted analysis and 1014 patients with visual analysis. Conclusion: AI-assisted analysis of lung involvement on submillisievert low-dose chest CT outperformed conventional visual analysis in predicting outcome in COVID-19 patients while I n p r e s s 3 reducing CT variability. Lung involvement on chest CT could be used as a reliable metric in future clinical trials.

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Section: Discussionmentioning

confidence: 99%

Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 at Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials

Gieraerts

Dangis

Janssen

et al. 2020

Radiology: Cardiothoracic Imaging

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“…Further, the difference in vendors and scan parameters used in baseline and follow-up CCTA scans (see Supplementary data online, Table S2) may have influenced the result. 34,35 However, we adjusted the baseline CACS and PV and the difference in vendors and tube voltages between two CCTA scans in the multivariate analysis and tested the consistency of CCTA in quantitative compositional PV assessment to ensure the reliability and reproducibility of CCTA parameters (see Supplementary data online, Figures S2 and S3). 36 Moreover, because no consensus on the use of serial CCTA for CAD monitoring currently exists, 6 an observational study such as the PARADIGM study provides a unique opportunity to evaluate the correlation between CACS progression assessed by CAC scans and the change in plaque burden.…”

Section: Discussionmentioning

confidence: 99%

Differential association between the progression of coronary artery calcium score and coronary plaque volume progression according to statins: the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging (PARADIGM) study

Lee

Sung

Andreini

et al. 2019

European Heart Journal - Cardiovascular Imaging

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Aims Coronary artery calcium score (CACS) is a strong predictor of major adverse cardiac events (MACE). Conversely, statins, which markedly reduce MACE risk, increase CACS. We explored whether CACS progression represents compositional plaque volume (PV) progression differently according to statin use. Methods and results From a prospective multinational registry of consecutive patients (n = 2252) who underwent serial coronary computed tomography angiography (CCTA) at a ≥ 2-year interval, 654 patients (61 ± 10 years, 56% men, inter-scan interval 3.9 ± 1.5 years) with information regarding the use of statins and having a serial CACS were included. Patients were divided into non-statin (n = 246) and statin-taking (n = 408) groups. Coronary PVs (total, calcified, and non-calcified; sum of fibrous, fibro-fatty, and lipid-rich) were quantitatively analysed, and CACS was measured from both CCTAs. Multivariate linear regression models were constructed for both statin-taking and non-statin group to assess the association between compositional PV change and change in CACS. In multivariate linear regression analysis, in the non-statin group, CACS increase was positively associated with both non-calcified (β = 0.369, P = 0.004) and calcified PV increase (β = 1.579, P < 0.001). However, in the statin-taking group, CACS increase was positively associated with calcified PV change (β = 0.756, P < 0.001) but was negatively associated with non-calcified PV change (β = −0.194, P = 0.026). Conclusion In the non-statin group, CACS progression indicates the progression of both non-calcified and calcified PV progression. However, under the effect of statins, CACS progression indicates only calcified PV progression, but not non-calcified PV progression. Thus, the result of serial CACS should be differently interpreted according to the use of statins.

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“…A recently published systematic review implies that CCTA has a potential role in assessment on the response of statin therapy on plaque volume and composition [40]. Such serial plaque assessments demand usage of the same software [41, 42]. In our study, plaque assessments were performed with a software previously shown to have a high degree of inter-observer variability on calcified and mixed lesions [23].…”

Section: Discussionmentioning

confidence: 99%

Effects of long-term statin-treatment on coronary atherosclerosis in patients with inflammatory joint diseases

et al. 2019

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BackgroundThe effect of statins over time on coronary atherosclerosis in patients with inflammatory joint diseases (IJD) is unknown. Our aim was to evaluate the change in coronary plaque morphology and volume in long-term statin-treated patients with IJD.MethodsSixty-eight patients with IJD and carotid artery plaque(s) underwent coronary computed tomography angiography before and after a mean of 4.7 (range 4.0–6.0) years of statin treatment. The treatment target for low density lipoprotein cholesterol (LDL-c) was ≤1.8 mmol/L. Changes in plaque volume (calcified, mixed/soft and total) and coronary artery calcification (CAC) from baseline to follow-up were assessed using the 17-segment American Heart Association-model.ResultsMedian (IQR) increase in CAC after statin treatment was 38 (5–236) Agatston units (p<0.001). Calcified and total plaque volume increased with 5.6 (0.0–49.1) and 2.9 (0.0–23.5) mm3, respectively (p<0.001 for both). The median (IQR) change in soft/mixed plaque volume was -10 (-7.1–0.0), p = <0.001. Patients who had obtained the LDL-c treatment target at follow-up, experienced reduced progression of both CAC and total plaque volume compared to patients with LDL-c >1.8mmol/L (21 [2–143] vs. 69 [16–423], p = 0.006 and 0.65 [-1.0–13.9] vs. 13.0 [0.0–60.8] mm3, p = 0.019, respectively).ConclusionsA progression of total atherosclerotic plaque volume in statin-treated patients with IJD was observed. However, soft/mixed plaque volume was reduced, suggesting an alteration in plaque composition. Patients with recommended LDL-c levels at follow-up had reduced atherosclerotic progression compared to patients with LDL-c levels above the treatment target, suggesting a beneficial effect of treatment to guideline-recommended lipid targets in IJD patients.

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Coronary CT Angiography: Variability of CT Scanners and Readers in Measurement of Plaque Volume

Cited by 58 publications

References 44 publications

Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 at Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials

Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 at Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials

Differential association between the progression of coronary artery calcium score and coronary plaque volume progression according to statins: the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging (PARADIGM) study

Effects of long-term statin-treatment on coronary atherosclerosis in patients with inflammatory joint diseases

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