Radiomic features of plaques derived from coronary CT angiography to identify hemodynamically significant coronary stenosis, using invasive FFR as the reference standard

Beyond plaque segmentation: a combined radiomics-deep learning approach for automated CAD-RADS classification

Introduction

Coronary Artery Disease (CAD) is a leading cause of global mortality, accurate stenosis grading is crucial for treatment planning, it currently requires time-consuming manual assessment and suffers from interobserver variability. Few deep learning methods have been proposed for automated scoring, but none have explored combining radiomic and autoencoder (AE)-based features. This study develops a machine learning approach combining radiomic and AE-based features for stenosis grade evaluation from multiplanar reconstructed images (MPR) cardiac computed tomography (CCTA) images.

Methods

The dataset comprised 2,548 CCTA-derived MPR images from 220 patients, classified as no-CAD, non-obstructive CAD or obstructive CAD. Sixty-four AE-based and 465 2D radiomic features, were processed separately or combined. The dataset was split into training (85%) and test (15%) sets. Relevant features were selected and input to a random forest classifier. A cascade pipeline stratified the three classes via two sub-tasks: (a) no CAD vs. CAD, and (b) nonobstructive vs. obstructive CAD.

Results

The AE-based model identified 17 and 6 features as relevant for the sub-task (a) and (b), respectively, while 44 and 30 features were selected in the radiomic model. The two models reached an overall balanced accuracy of 0.68 and 0.82 on the test set, respectively. Fifteen and 35 features were indeed selected in the combined model which outperformed the single ones achieving on the test set an overall balanced accuracy, sensitivity and specificity of 0.91, 0.91, and 0.94, respectively.

Conclusion

Integration of radiomics and deep learning shows promising results for stenosis assessment in CAD patients.

Radiomics and dosiomics approaches to estimate lung function after stereotactic body radiation therapy in patients with lung tumors

Lung function assessment is essential for determining the optimal treatment strategy for radiation therapy in patients with lung tumors. This study aimed to develop radiomics and dosiomics approaches to estimate pulmonary function test (PFT) results in post-stereotactic body radiation therapy (SBRT). Sixty-four patients with lung tumors who underwent SBRT were included. Models were created to estimate the PFT results at 0-6 months (Cohort 1) and 6-24 months (Cohort 2) after SBRT. Radiomics and dosiomics features were extracted from the computed tomography (CT) images and dose distributions, respectively. To estimate the PFT results, Models A (dose-volume histogram [DVH] + radiomics features) and B (DVH + radiomics + dosiomics features) were created. In the PFT results, the forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) were estimated using each model, and the ratio of FEV1 to FVC (FEV1/FVC) was calculated. The Pearson's correlation coefficient (Pearson r) and area under the curve (AUC) for FEV1/FVC (< 70%) were calculated. The models were evaluated by comparing them with the conventional calculation formulae (Conventional). The Pearson r (FEV1/FVC) values were 0.30, 0.64, and 0.69 for Conventional and Models A and B (Cohort 2), respectively, and the AUC (FEV1/FVC < 70%) values were 0.63, 0.80, and 0.78, respectively. This study demonstrates the possibility of estimating lung function after SBRT using radiomics and dosiomics features based on planning CT images and dose distributions.

Cardiac Radiomics Analyses in Times of Photon-counting Computed Tomography for Personalized Risk Stratification in the Present and in the Future

The need for effective early detection and optimal therapy monitoring of cardiovascular diseases as the leading cause of death has led to an adaptation of the guidelines with a focus on cardiac computed tomography (CCTA) in patients with a low to intermediate risk of coronary heart disease (CHD). In particular, the introduction of photon-counting computed tomography (PCCT) in CT diagnostics promises significant advances through higher temporal and spatial resolution, and also enables advanced texture analysis, known as radiomics analysis. Originally developed in oncological imaging, radiomics analysis is increasingly being used in cardiac imaging and research. The aim is to generate imaging biomarkers that improve the early detection of cardiovascular diseases and therapy monitoring.The present study summarizes the current developments in cardiac CT texture analysis with a particular focus on evaluations of PCCT data sets in different regions, including the myocardium, coronary plaques, and pericoronary/epicardial fat tissue.These developments could revolutionize the diagnosis and treatment of cardiovascular diseases and significantly improve patient prognoses worldwide. The aim of this review article is to shed light on the current state of radiomics research in cardiovascular imaging and to identify opportunities for establishing it in clinical routine in the future. · Radiomics: Enables deeper, objective analysis of cardiovascular structures via feature quantification.. · PCCT: Provides a higher quality image, improving stability and reproducibility in cardiac CT.. · Early detection: PCCT and radiomics enhance cardiovascular disease detection and management.. · Challenges: Technical and standardization issues hinder widespread clinical application.. · Future: Advancing PCCT technologies could soon integrate radiomics in routine practice.. · Ayx I, Bauer R, Schönberg SO et al. Cardiac Radiomics Analyses in Times of Photon-counting Computed Tomography for Personalized Risk Stratification in the Present and in the Future. Rofo 2025; DOI 10.1055/a-2499-3122.

Enhancing cardiovascular risk stratification: Radiomics of coronary plaque and perivascular adipose tissue - Current insights and future perspectives

Radiomics, the quantitative extraction and mining of features from radiological images, has recently emerged as a promising source of non-invasive image-based cardiovascular biomarkers, potentially revolutionizing diagnostics and risk assessment. This review explores its application within coronary plaques and pericoronary adipose tissue, particularly focusing on plaque characterization and cardiac events prediction. By shedding light on the current state-of-the-art, achievements, and prospective avenues, this review contributes to a deeper understanding of the evolving landscape of radiomics in the context of coronary arteries. Finally, open challenges and existing gaps are emphasized to underscore the need for future efforts aimed at ensuring the robustness and reliability of radiomics studies, facilitating their clinical translation.

Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death

This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98-5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800-0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging

Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.

Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment

Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective

Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.

Radiomics in Cardiac Computed Tomography

In recent years, there has been an increasing recognition of coronary computed tomographic angiography (CCTA) and gated non-contrast cardiac CT in the workup of coronary artery disease in patients with low and intermediate pretest probability, through the readjustment guidelines by medical societies. However, in routine clinical practice, these CT data sets are usually evaluated dominantly regarding relevant coronary artery stenosis and calcification. The implementation of radiomics analysis, which provides visually elusive quantitative information from digital images, has the potential to open a new era for cardiac CT that goes far beyond mere stenosis or calcification grade estimation. This review offers an overview of the results obtained from radiomics analyses in cardiac CT, including the evaluation of coronary plaques, pericoronary adipose tissue, and the myocardium itself. It also highlights the advantages and disadvantages of use in routine clinical practice.

Ultrasonic Imaging of Cardiovascular Disease Based on Image Processor Analysis of Hard Plaque Characteristics

Cardiovascular disease detection and analysis using ultrasonic imaging expels errors in manual clinical trials with precise outcomes. It requires a combination of smart computing systems and intelligent image processors. The disease characteristics are analyzed based on the configuration and precise tuning of the processing device. In this article, a characteristic extraction technique (CET) using knowledge learning (KL) is introduced to improve the analysis precision. The proposed method requires optimal selection of disease features and trained similar datasets for improving the characteristic extraction. The disease attributes and accuracy are identified using the standard knowledge update. The image and data features are segmented using the variable processor configuration to prevent false rates. The false rates due to unidentifiable plaque characteristics result in weak knowledge updates. Therefore, the segmentation and data extraction are unanimously performed to prevent feature misleads. The knowledge base is updated using the extracted and identified plaque characteristics for consecutive image analysis. The processor configurations are manageable using the updated knowledge and characteristics to improve precision. The proposed method is verified using precision, characteristic update, training rate, extraction ratio, and time factor.

Regulation of cardiovascular calcification by lipids and lipoproteins

PURPOSE OF REVIEW

Lipids and lipoproteins have long been known to contribute to atherosclerosis and cardiovascular calcification. One theme of recent work is the study of lipoprotein (a) [Lp(a)], a lipoprotein particle similar to LDL-cholesterol that carries a long apoprotein tail and most of the circulating oxidized phospholipids.

RECENT FINDINGS

In-vitro studies show that Lp(a) stimulates osteoblastic differentiation and mineralization of vascular smooth muscle cells, while the association of Lp(a) with coronary artery calcification continues to have varying results, possibly because of the widely varying threshold levels of Lp(a) chosen for association analyses. Another emerging area in the field of cardiovascular calcification is pathological endothelial-to-mesenchymal transition (EndMT), the process whereby endothelial cell transition into multipotent mesenchymal cells, some of which differentiate into osteochondrogenic cells and mineralize. The effects of lipids and lipoproteins on EndMT suggest that they modulate cardiovascular calcification through multiple mechanisms. There are also emerging trends in imaging of calcific vasculopathy, including: intravascular optical coherence tomography for quantifying plaque characteristics, PET with a radiolabeled NaF tracer, with either CT or MRI to detect coronary plaque vulnerability.

SUMMARY

Recent work in this field includes studies of Lp(a), EndMT, and new imaging techniques.

Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients

PURPOSE

We aimed to assess radiomics approaches for estimating three pulmonary function test (PFT) results (forced expiratory volume in one second [FEV1], forced vital capacity [FVC], and the ratio of FEV1 to FVC [FEV₁/FVC]) using data extracted from chest computed tomography (CT) images.

METHODS

This retrospective study included 85 lung cancer patients (mean age, 75 years ±8; 69 men) who underwent stereotactic body radiotherapy between 2012 and 2020. Their pretreatment chest breath-hold CT and PFT data before radiotherapy were obtained. A total of 107 radiomics features (Shape: 14, Intensity: 18, Texture: 75) were extracted using two methods: extraction of the lung tissue (<-250 HU) (APPROACH 1), and extraction of small blood vessels and lung tissue (APPROACH 2). The PFT results were estimated using the least absolute shrinkage and selection operator regression. Pearson's correlation coefficients (r) were determined for all PFT results, and the area under the curve (AUC) was calculated for FEV₁/FVC (<70 %). Finally, we compared our approaches with the conventional formula (Conventional).

RESULTS

For the estimated FEV₁/FVC, the Pearson's r were 0.21 (P =.06), 0.69 (P <.01), and 0.73 (P <.01) for Conventional, APPROACH 1, and APPROACH 2, respectively; the AUCs for FEV₁/FVC (<70 %) were 0.67 (95 % confidence interval [CI]: 0.55, 0.79), 0.82 (CI: 0.72, 0.91; P =.047) and 0.86 (CI: 0.78, 0.94; P =.01), respectively.

CONCLUSIONS

The radiomics approach performed better than the conventional equation and may be useful for assessing lung function based on CT images.

Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA)

Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.

Cardiac computed tomography radiomics: a narrative review of current status and future directions

BACKGROUND AND OBJECTIVE

In an era of profound growth of medical data and rapid development of advanced imaging modalities, precision medicine increasingly requires further expansion of what can be interpreted from medical images. However, the current interpretation of cardiac computed tomography (CT) images mainly depends on subjective and qualitative analysis. Radiomics uses advanced image analysis to extract numerous quantitative features from digital images that are unrecognizable to the naked eye. Visualization of these features can reveal underlying connections between image phenotyping and biological characteristics and support clinical outcomes. Although research into radiomics on cardiovascular disease began only recently, several studies have indicated its potential clinical value in assessing future cardiac risk and guiding prevention and management strategies. Our review aimed to summarize the current applications of cardiac CT radiomics in the cardiovascular field and discuss its advantages, challenges, and future directions.

METHODS

We searched for English-language articles published between January 2010 and August 2021 in the databases of PubMed, Embase, and Google Scholar. The keywords used in the search included computed tomography or CT, radiomics, cardiovascular or cardiac.

KEY CONTENT AND FINDINGS

The current applications of radiomics in cardiac CT were found to mainly involve research into coronary plaques, perivascular adipose tissue (PVAT), myocardial tissue, and intracardiac lesions. Related findings on cardiac CT radiomics suggested the technique can assist the identification of vulnerable plaques or patients, improve cardiac risk prediction and stratification, discriminate myocardial pathology and etiologies behind intracardiac lesions, and offer new perspective and development prospects to personalized cardiovascular medicine.

CONCLUSIONS

Cardiac CT radiomics can gather additional disease-related information at a microstructural level and establish a link between imaging phenotyping and tissue pathology or biology alone. Therefore, cardiac CT radiomics has significant clinical implications, including a contribution to clinical decision-making. Along with advancements in cardiac CT imaging, cardiac CT radiomics is expected to provide more precise phenotyping of cardiovascular disease for patients and doctors, which can improve diagnostic, prognostic, and therapeutic decision making in the future.

Diagnostic efficacy of CCTA and CT-FFR based on risk factors for myocardial ischemia

BACKGROUND

Coronary artery coronary computed tomography angiography (CCTA) can observe the degree of coronary artery stenosis and fractional flow reserve (FFR) can diagnose hemodynamic abnormalities caused by coronary artery stenosis. However, noninvasive imaging examination that can both observe the above two methods at the same time has not yet been elucidated.

OBJECTIVE

To investigate the diagnostic efficacy of CCTA and computed tomography-derived fractional flow reserve (CT-FFR) based on different risk factors for myocardial ischemia.

METHODS

Patients undergoing CCTA in our hospital from August 18, 2020 to April 28, 2021 were randomly selected, and the data were subjected to CT-FFR analysis. Vascular characteristics were measured, including total plaque volume, calcified plaque volume, non-calcified plaque volume, plaque length, and lumen stenosis, and the patients were categorized into a non-ischemia group (FFR > 0.8) and an ischemia group (FFR ≤ 0.8). Plaque characteristics were compared between the two groups, and logistic regression analysis was employed to explore the correlations between plaque characteristics and ischemic lesions.

RESULTS

From a total of 122 patients enrolled in the study, there were 218 vascular branches with FFR > 0.8 and 174 vascular branches with FFR ≤ 0.8. There were significant group differences in total plaque volume, calcified plaque volume, plaque length, and lumen stenosis > 50% (n). The obtained data were as follows: non-ischemic group 10.57 (4.80, 259.65), ischemic group 14.87 (3.39, 424.45), Z = 9.772, p = 0.002, non-ischemic group 10.57 (0, 168.77), ischemic group 14.87 (0, 191.00), Z = 2.503, p ≤ 0.001), non-ischemic group 8.17 (37.05, 40.53), ischemic group 8.38 (56.66, 86.47), Z = 5.923, p = 0.016, and lumen stenosis > 50%, non-ischemic group 46, ischemic group 90, x2 = 14.77, p ≤ 0.001. The regression analysis results indicated that total plaque volume, calcified plaque volume, plaque length and lumen stenosis > 50% were risk factors for myocardial ischemia, with ORs and p values of (2.311, p = 0.002), (1.021, p = 0.004), (2.159, p < 0.001), and (0.181, p < 0.001), respectively.

CONCLUSION

Total plaque volume, calcified plaque volume, plaque length and lumen stenosis > 50% are predictors for myocardial ischemia. Coronary artery CCTA combined with CT-FFR could simultaneously observe the anatomical stenosis and evaluate myocardial blood supply at the functional level. Thus, myocardial ischemia could be better diagnosed.