Dataset Information

Development of a computed tomography calcium scoring technique for assessing calcinosis distribution, pattern and burden in dermatomyositis.

ABSTRACT:

Objectives

To utilize whole-body CT imaging and calcium scoring techniques as tools for calcinosis assessment in a prospective cohort of patients with adult and juvenile dermatomyositis (DM and JDM, respectively).

Methods

Thirty-one patients (14 DM and 17 JDM) who fulfilled Bohan and Peter Classification criteria as probable or definite DM, the EULAR-ACR criteria for definite DM, and with calcinosis identified by physical examination or prior imaging studies were included. Non-contrast whole-body CT scans were obtained using low-dose radiation procedures. Scans were read qualitatively and quantitated. We calculated the sensitivity and specificity of calcinosis detection of physician physical exam against CT. We quantified calcinosis burden using the Agatston scoring technique.

Results

We identified five distinct calcinosis patterns: Clustered, Disjoint, Interfascial, Confluent and Fluid-filled. Novel locations of calcinosis were observed, including the cardiac tissue, pelvic and shoulder bursa, and the spermatic cord. Quantitative measures using Agatston scoring for calcinosis were used in regional distributions across the body. Physician physical exams had a sensitivity of 59% and a specificity of 90% compared with CT detection. A higher calcium score correlated with higher Physician Global Damage, Calcinosis Severity scores, and disease duration.

Conclusion

Whole-body CT scans and the Agatston scoring metric define distinct calcinosis patterns and provide novel insights relating to calcinosis in DM and JDM patients. Physicians' physical examinations underrepresented the presence of calcium. Calcium scoring of CT scans correlated with clinical measures, which suggests that this method may be used to assess calcinosis and follow its progression.

SUBMITTER: Cervantes BA

PROVIDER: S-EPMC10765154 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Development of a computed tomography calcium scoring technique for assessing calcinosis distribution, pattern and burden in dermatomyositis.

Cervantes Briana A BA Gowda Prateek P Rider Lisa G LG Miller Frederick W FW Chen Marcus Y MY Schiffenbauer Adam A

Rheumatology (Oxford, England) 20240101 1

<h4>Objectives</h4>To utilize whole-body CT imaging and calcium scoring techniques as tools for calcinosis assessment in a prospective cohort of patients with adult and juvenile dermatomyositis (DM and JDM, respectively).<h4>Methods</h4>Thirty-one patients (14 DM and 17 JDM) who fulfilled Bohan and Peter Classification criteria as probable or definite DM, the EULAR-ACR criteria for definite DM, and with calcinosis identified by physical examination or prior imaging studies were included. Non-con ...[more]

PMID: 37286372

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Project description:ObjectivesCoronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.MethodsFully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers).ResultsOf 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan.ConclusionsThis artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation.Key points• Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.

Project description:PurposeModern non-invasive evaluation of Coronary Artery Disease (CAD) requires non-contrast low dose Computed Tomography (CT) imaging for determination of Calcium Scoring (CACS) and contrast-enhanced imaging for evaluation of vascular stenosis. Several methods for calculation of CACS from contrast-enhanced images have been proposed before. The main principle for that is generation of virtual non-contrast images by iodine subtraction from a contrast-enhanced spectral CT dataset. However, those techniques have some limitations: Dual-Source CT imaging can lead to increased radiation exposure, and switching of the tube voltage (rapid kVp switching) can be associated with slower rotation speed of the gantry and is thus prone to motion artefacts that are especially critical in cardiac imaging. Both techniques cannot simultaneously acquire spectral data. A novel technique to overcome these difficulties is spectral imaging with a dual-layer detector. After absorption of the lower energetic photons in the first layer, the second layer detects a hardened spectrum of the emitted radiation resulting in registration of two different energy spectra at the same time. The objective of the present investigation was to evaluate the accuracy of virtual non-contrast CACS computed from spectral data in comparison to standard non-contrast imaging.MethodsWe consecutively investigated 20 patients referred to Coronary Computed Tomography Angiography (CCTA) with suspicion of CAD using a Dual-Layer spectral CT system (IQon; Philips Healthcare, The Netherlands). CACS was calculated from both, real- and virtual non-contrast images by certified software for medical use. Correlation analyses for real- and virtual non-contrast images and agreement evaluation with Bland-Altman-Plots were performed.ResultsMean patient age was 57.7 ± 14 years (n = 20). 13 patients (65%) were male. Inter-quartile-range of clinical CACS was 0-448, the mean was 334. Correlation of CACS from real- and virtual non-contrast images was very high (0.94); p < 0.0001. The slope was 2.3 indicating that values from virtual non-contrast images are approximately half of the results obtained from real non-contrast data. Visual analysis of Bland-Altman-Plot shows good accordance of both methods when results from virtual non-contrast data are multiplied by the slope of the logistic regression model (2.3). The acquired power of this results is 0.99.ConclusionDetermination of Calcium Score from contrast enhanced CCTA using spectral imaging with a dual-layer detector is feasible and shows good agreement with the conventional technique when a proportionality factor is applied. The observed difference between both methods is due to an underestimation of plaque volume, and-to an even greater extend -an underestimation of plaque density with the virtual non-contrast approach. Our data suggest that radiation exposure can be reduced through omitting additional native scans for patients referred to CCTA when using a dual-layer spectral system without the usual limitations of dual energy analysis.

Dataset Information

Development of a computed tomography calcium scoring technique for assessing calcinosis distribution, pattern and burden in dermatomyositis.

Objectives

Methods

Results

Conclusion

Publications

Development of a computed tomography calcium scoring technique for assessing calcinosis distribution, pattern and burden in dermatomyositis.

Similar Datasets

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