Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness

Kang, Hunseung; Ahn, Woojin; Jeong, Ju Hyun; Suh, Young Joo; Yang, Dong Hyun; Choi, Hangseok; Hwang, Sung Ho; Yong, Hwan Seok; Oh, Yu-Whan; Kang, Eunyoung; Kim, Cherry

doi:10.1007/s00330-022-09143-1

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…In the present study, we employed an AI-based software validated on non-electrocardiogram (ECG)-gated LDCT using multiinstitutional datasets with manual CAC scoring as the reference standard [25]. The same software yielded better diagnostic performance with 1-mm than 2.5-mm LDCT images [9], supporting our method of analyzing the thinnest reconstruction available. It is worth emphasizing that this approach may lead to potential prognostic implications for participants in LCS trials, that is, in a setting where neither recommended image reconstruction [26] nor ECG-gating is routinely available.…”

Section: Discussionmentioning

confidence: 66%

“…For the automated CAC evaluation, 1-mm images were transferred to a dedicated graphic station (Alienware Area 51 R6 equipped with Dual NVIDIA GeForce RTX 2080 OC graphics) and analyzed using commercial AI software (AVIEW, Coreline Soft, Seoul, Korea) based on a 3-dimensional U-net architecture [7] (Fig 1). The rationale for using 1-mm images was supported by previous data demonstrating a more accurate CAC scoring with 1-mm than thicker slices in LCS LDCTs [8,9]. CAC was assessed using the Agatston score and stratified into the following strata: 0, 1-10, 11-100, 101-400, and > 400 [10].…”

Section: Imaging Acquisition and Analysismentioning

confidence: 99%

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Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial

et al. 2023

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Coronary artery calcium (CAC) is a known risk factor for cardiovascular (CV) events and mortality but is not yet routinely evaluated in low-dose computed tomography (LDCT)-based lung cancer screening (LCS). The present analysis explored the capacity of a fully automated CAC scoring to predict 12-year mortality in the Multicentric Italian Lung Detection (MILD) LCS trial. The study included 2239 volunteers of the MILD trial who underwent a baseline LDCT from September 2005 to January 2011, with a median follow-up of 190 months. The CAC score was measured by a commercially available fully automated artificial intelligence (AI) software and stratified into five strata: 0, 1–10, 11–100, 101–400, and > 400. Twelve-year all-cause mortality was 8.5% (191/2239) overall, 3.2% with CAC = 0, 4.9% with CAC = 1–10, 8.0% with CAC = 11–100, 11.5% with CAC = 101–400, and 17% with CAC > 400. In Cox proportional hazards regression analysis, CAC > 400 was associated with a higher 12-year all-cause mortality both in a univariate model (hazard ratio, HR, 5.75 [95% confidence interval, CI, 2.08–15.92] compared to CAC = 0) and after adjustment for baseline confounders (HR, 3.80 [95%CI, 1.35–10.74] compared to CAC = 0). All-cause mortality significantly increased with increasing CAC (7% in CAC ≤ 400 vs. 17% in CAC > 400, Log-Rank p-value <0.001). Non-cancer at 12 years mortality was 3% (67/2239) overall, 0.8% with CAC = 0, 1.0% with CAC = 1–10, 2.9% with CAC = 11–100, 3.6% with CAC = 101–400, and 8.2% with CAC > 400 (Grey’s test p < 0.001). In Fine and Gray’s competing risk model, CAC > 400 predicted 12-year non-cancer mortality in a univariate model (sub-distribution hazard ratio, SHR, 10.62 [95% confidence interval, CI, 1.43–78.98] compared to CAC = 0), but the association was no longer significant after adjustment for baseline confounders. In conclusion, fully automated CAC scoring was effective in predicting all-cause mortality at 12 years in a LCS setting.

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Section: Discussionmentioning

confidence: 66%

Section: Imaging Acquisition and Analysismentioning

confidence: 99%

Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial

et al. 2023

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“…12,13 CAC was measured with a 3-dimensional U-net architecture-based scoring tool previously validated in electrocardiography-gated and nonelectrocardiography-gated LDCTs. [14][15][16] The software was also tested in a subgroup of BioMILD participants to assess its longitudinal reproducibility and agreement with manual evaluation, showing consistent results (Table S1, Supplemental Digital Content 1, http://links.lww.com/JTI/ A248 and Table S2, Supplemental Digital Content 1, http://links.lww.com/JTI/A248). CAC scores were stratified using prespecified Agatston score strata of 0 to 99, 100 to 399, and ≥ 400, in keeping with the risk categorization adopted in the low-dose cardiac CT population of the Risk Or Benefit IN Screening for CArdiovascular diseases (ROBINSCA) trial.…”

Section: Imaging Acquisition and Analysismentioning

confidence: 74%

“…Emphysema was quantified using the percentage of lung volume occupied by voxels with attenuation of −950 HU or less (percentage of low attenuation areas, %LAA) 12,13 . CAC was measured with a 3-dimensional U-net architecture-based scoring tool previously validated in electrocardiography-gated and non–electrocardiography-gated LDCTs 14–16 . The software was also tested in a subgroup of BioMILD participants to assess its longitudinal reproducibility and agreement with manual evaluation, showing consistent results (Table S1, Supplemental Digital Content 1, http://links.lww.com/JTI/A248 and Table S2, Supplemental Digital Content 1, http://links.lww.com/JTI/A248).…”

Section: Methodsmentioning

confidence: 93%

Automated Coronary Artery Calcium and Quantitative Emphysema in Lung Cancer Screening

Balbi

Sabia

Ledda

et al. 2023

Journal of Thoracic Imaging

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Purpose: To assess automated coronary artery calcium (CAC) and quantitative emphysema (percentage of low attenuation areas [%LAA]) for predicting mortality and lung cancer (LC) incidence in LC screening. To explore correlations between %LAA, CAC, and forced expiratory value in 1 second (FEV1) and the discriminative ability of %LAA for airflow obstruction. Materials and Methods: Baseline low-dose computed tomography scans of the BioMILD trial were analyzed using an artificial intelligence software. Univariate and multivariate analyses were performed to estimate the predictive value of %LAA and CAC. Harrell C-statistic and time-dependent area under the curve (AUC) were reported for 3 nested models (Modelsurvey: age, sex, pack-years; Modelsurvey-LDCT: Modelsurvey plus %LAA plus CAC; Modelfinal: Modelsurvey-LDCT plus selected confounders). The correlations between %LAA, CAC, and FEV1 and the discriminative ability of %LAA for airflow obstruction were tested using the Pearson correlation coefficient and AUC-receiver operating characteristic curve, respectively. Results: A total of 4098 volunteers were enrolled. %LAA and CAC independently predicted 6-year all-cause (Modelfinal hazard ratio [HR], 1.14 per %LAA interquartile range [IQR] increase [95% CI, 1.05-1.23], 2.13 for CAC ≥400 [95% CI, 1.36-3.28]), noncancer (Modelfinal HR, 1.25 per %LAA IQR increase [95% CI, 1.11-1.37], 3.22 for CAC ≥400 [95%CI, 1.62-6.39]), and cardiovascular (Modelfinal HR, 1.25 per %LAA IQR increase [95% CI, 1.00-1.46], 4.66 for CAC ≥400, [95% CI, 1.80-12.58]) mortality, with an increase in concordance probability in Modelsurvey-LDCT compared with Modelsurvey (P<0.05). No significant association with LC incidence was found after adjustments. Both biomarkers negatively correlated with FEV1 (P<0.01). %LAA identified airflow obstruction with a moderate discriminative ability (AUC, 0.738). Conclusions: Automated CAC and %LAA added prognostic information to age, sex, and pack-years for predicting mortality but not LC incidence in an LC screening setting. Both biomarkers negatively correlated with FEV1, with %LAA enabling the identification of airflow obstruction with moderate discriminative ability.

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“…Consequently, the clinical application of AI-based automatic CAC scoring has been extended to chest CT [ 30 31 ]. Automatic CAC scoring on low-dose chest CT showed excellent reliability with manual CAC scoring, but the reliability of CAC score-based severity categorization varies among datasets with different scan protocols [ 30 32 ] Therefore, the improvement of an AI-based automatic scoring algorithm specific to the scanning protocol is necessary to apply automatic CAC scoring to chest CT. In contrast, the modified length-based grading method suggested in our study has advantages in that it is less affected by the scan protocols.…”

Section: Discussionmentioning

confidence: 99%

A Modified Length-Based Grading Method for Assessing Coronary Artery Calcium Severity on Non-Electrocardiogram-Gated Chest Computed Tomography: A Multiple-Observer Study

et al. 2023

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Objective To validate a simplified ordinal scoring method, referred to as modified length-based grading, for assessing coronary artery calcium (CAC) severity on non-electrocardiogram (ECG)-gated chest computed tomography (CT). Materials and Methods This retrospective study enrolled 120 patients (mean age ± standard deviation [SD], 63.1 ± 14.5 years; male, 64) who underwent both non-ECG-gated chest CT and ECG-gated cardiac CT between January 2011 and December 2021. Six radiologists independently assessed CAC severity on chest CT using two scoring methods (visual assessment and modified length-based grading) and categorized the results as none, mild, moderate, or severe. The CAC category on cardiac CT assessed using the Agatston score was used as the reference standard. Agreement among the six observers for CAC category classification was assessed using Fleiss kappa statistics. Agreement between CAC categories on chest CT obtained using either method and the Agatston score categories on cardiac CT was assessed using Cohen’s kappa. The time taken to evaluate CAC grading was compared between the observers and two grading methods. Results For differentiation of the four CAC categories, interobserver agreement was moderate for visual assessment (Fleiss kappa, 0.553 [95% confidence interval {CI}: 0.496–0.610]) and good for modified length-based grading (Fleiss kappa, 0.695 [95% CI: 0.636–0.754]). The modified length-based grading demonstrated better agreement with the reference standard categorization with cardiac CT than visual assessment (Cohen’s kappa, 0.565 [95% CI: 0.511–0.619 for visual assessment vs. 0.695 [95% CI: 0.638–0.752] for modified length-based grading). The overall time for evaluating CAC grading was slightly shorter in visual assessment (mean ± SD, 41.8 ± 38.9 s) than in modified length-based grading (43.5 ± 33.2 s) ( P < 0.001). Conclusion The modified length-based grading worked well for evaluating CAC on non-ECG-gated chest CT with better interobserver agreement and agreement with cardiac CT than visual assessment.

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Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness

Cited by 13 publications

References 21 publications

Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial

Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial

Automated Coronary Artery Calcium and Quantitative Emphysema in Lung Cancer Screening

A Modified Length-Based Grading Method for Assessing Coronary Artery Calcium Severity on Non-Electrocardiogram-Gated Chest Computed Tomography: A Multiple-Observer Study

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