Dataset Information

Radiomics-based discrimination of coronary chronic total occlusion and subtotal occlusion on coronary computed tomography angiography.

ABSTRACT:

Objectives

Differentiating chronic total occlusion (CTO) from subtotal occlusion (SO) is often difficult to make from coronary computed tomography angiography (CCTA). We developed a CCTA-based radiomics model to differentiate CTO and SO.

Methods

A total of 66 patients with SO underwent CCTA before invasive angiography and were matched to 66 patients with CTO. Comprehensive imaging analysis was conducted for all lesioned vessels, involving the automatic identification of the lumen within the occluded segment and extraction of 1,904 radiomics features. Radiomics models were then constructed to assess the discriminative value of these features in distinguishing CTO from SO. External validation of the model was performed using data from another medical center.

Results

Compared to SO patients, CTO patients had more blunt stumps (internal: 53/66 (80.3%) vs. 39/66 (59.1%); external: 36/50 (72.0%) vs. 20/50 (40.0%), both p < 0.01), longer lesion length (internal: median length 15.4 mm[IQR: 10.4-22.3 mm] vs. 8.7 mm[IQR: 4.9-12.6 mm]; external:11.8 mm[IQR: 6.1-23.4 mm] vs. 6.2 mm[IQR: 3.5-9.1 mm]; both p < 0.001). Sixteen unique radiomics features were identified after the least absolute shrinkage and selection operator regression. When added to the combined model including imaging features, radiomics features provided increased value for distinguishing CTO from SO (AUC, internal: 0.772 vs. 0.846; p = 0.023; external: 0.718 vs. 0.781, p = 0.146).

Conclusions

The occluded segment vessels of CTO and SO have different radiomics signatures. The combined application of radiomics features and imaging features based on CCTA extraction can enhance diagnostic confidence.

SUBMITTER: Li J

PROVIDER: S-EPMC11005149 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Publications

Radiomics-based discrimination of coronary chronic total occlusion and subtotal occlusion on coronary computed tomography angiography.

Li Jun J Ren Lichen L Guo Hehe H Yang Haibo H Cui Jingjing J Zhang Yonggao Y

BMC medical imaging 20240409 1

<h4>Objectives</h4>Differentiating chronic total occlusion (CTO) from subtotal occlusion (SO) is often difficult to make from coronary computed tomography angiography (CCTA). We developed a CCTA-based radiomics model to differentiate CTO and SO.<h4>Methods</h4>A total of 66 patients with SO underwent CCTA before invasive angiography and were matched to 66 patients with CTO. Comprehensive imaging analysis was conducted for all lesioned vessels, involving the automatic identification of the lumen ...[more]

PMID: 38594629

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Project description:ObjectivesThe aim of this study was to precisely phenotype culprit and nonculprit lesions in myocardial infarction (MI) and lesions in stable coronary artery disease (CAD) using coronary computed tomography angiography (CTA)-based radiomic analysis.BackgroundIt remains debated whether any single coronary atherosclerotic plaque within the vulnerable patient exhibits unique morphology conferring an increased risk of clinical events.MethodsA total of 60 patients with acute MI prospectively underwent coronary CTA before invasive angiography and were matched to 60 patients with stable CAD. For all coronary lesions, high-risk plaque (HRP) characteristics were qualitatively assessed, followed by semiautomated plaque quantification and extraction of 1,103 radiomic features. Machine learning models were built to examine the additive value of radiomic features for discriminating culprit lesions over and above HRP and plaque volumes.ResultsCulprit lesions had higher mean volumes of noncalcified plaque (NCP) and low-density noncalcified plaque (LDNCP) compared with the highest-grade stenosis nonculprits and highest-grade stenosis stable CAD lesions (NCP: 138.1 mm3 vs 110.7 mm3 vs 102.7 mm3; LDNCP: 14.2 mm3 vs 9.8 mm3 vs 8.4 mm3; both Ptrend < 0.01). In multivariable linear regression adjusted for NCP and LDNCP volumes, 14.9% (164 of 1,103) of radiomic features were associated with culprits and 9.7% (107 of 1,103) were associated with the highest-grade stenosis nonculprits (critical P < 0.0007) when compared with highest-grade stenosis stable CAD lesions as reference. Hierarchical clustering of significant radiomic features identified 9 unique data clusters (latent phenotypes): 5 contained radiomic features specific to culprits, 1 contained features specific to highest-grade stenosis nonculprits, and 3 contained features associated with either lesion type. Radiomic features provided incremental value for discriminating culprit lesions when added to a machine learning model containing HRP and plaque volumes (area under the receiver-operating characteristic curve 0.86 vs 0.76; P = 0.004).ConclusionsCulprit lesions and highest-grade stenosis nonculprit lesions in MI have distinct radiomic signatures compared with lesions in stable CAD. Within the vulnerable patient may exist individual vulnerable plaques identifiable by coronary CTA-based precision phenotyping.

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Project description:BackgroundBoth acute myocarditis patients and normal cohort usually present with normal coronary computed tomography angiography (CCTA) performance, and the performance of CCTA radiomics on the prediction for myocarditis is still unclear. This study aims to build a clinical prediction model for acute myocarditis using CCTA-based radiomics.MethodsA total of 215 consecutive patients from the Affiliated Jinhua Hospital, Zhejiang University School of Medicine (Center 1) and Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (Center 2) who underwent CCTA and were diagnosed as normal or acute myocarditis were enrolled. All CCTA images of myocardium were automatically segmented to extract radiomics features. Pearson correlation analysis was used to identify features that were highly correlated with others. The application of the 5-fold cross-validation test reduced reliance on a single training set and provided more robust performance estimation. The best radiomics prediction model was chosen and combined with the clinical labels to construct a clinical-radiomics model for the classification of patients as with or without myocarditis.ResultsPearson's correlation and least absolute shrinkage and selection operator (LASSO) regression analyses identified 10 radiomics features and 7 clinical features which demonstrated the best correlation. The receiver operating characteristic curves of the three models that used the support vector machine (SVM) demonstrated the best performance. The area under the curves (AUCs) of Model 1 (Rad-score model) using training and test datasets were 0.970 (0.949-0.991) and 0.912 (0.832-0.992), respectively. The AUCs of Model 2 (clinical factors model) for the training and test datasets were 0.992 (0.983-1.000) and 0.943 (0.875-1.000), respectively. Model 3 (clinical factors and Rad-score model) demonstrated the best results, with AUCs of 1.000 (0.999-1.000) and 0.951 (0.880-1.000) in the training and test datasets, respectively.ConclusionsThe CCTA-based radiomics model constructed using machine learning demonstrated good performance for predicting myocarditis.

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Dataset Information

Radiomics-based discrimination of coronary chronic total occlusion and subtotal occlusion on coronary computed tomography angiography.

Objectives

Methods

Results

Conclusions

Publications

Radiomics-based discrimination of coronary chronic total occlusion and subtotal occlusion on coronary computed tomography angiography.

Similar Datasets

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