Texture analysis of acute myocardial infarction with CT: First experience study

A coronary CT angiography-derived myocardial radiomics model for predicting adverse outcomes in chronic myocardial infarction

BACKGROUND

The prognostic efficacy of a coronary computed tomography angiography (CCTA)-derived myocardial radiomics model in patients with chronic myocardial infarction (MI) is unclear.

METHODS

In this retrospective study, a cohort of 236 patients with chronic MI who underwent both CCTA and cardiac magnetic resonance (CMR) examinations within 30 days were enrolled and randomly divided into training and testing datasets at a ratio of 7:3. The clinical endpoints were major adverse cardiovascular events (MACE), defined as all-cause death, myocardial reinfarction and heart failure hospitalization. The entire three-dimensional left ventricular myocardium on CCTA images was segmented as the volume of interest for the extraction of radiomics features. Five models, namely the clinical model, CMR model, clinical+CMR model, CCTA-radiomics model, and clinical+CCTA-radiomics model, were constructed using multivariate Cox regression. The prognostic performances of these models were evaluated through receiver operating characteristic curve analysis and the index of concordance (C-index).

RESULTS

Fifty-one (20.16%) patients experienced MACE during a median follow-up of 1439.5 days. The predictive performance of the CCTA-radiomics model surpassed that of the clinical model, CMR model, and clinical+CMR model in both the training (area under the curve (AUC) of 0.904 vs. 0.691, 0.764, 0.785; C-index of 0.88 vs. 0.71, 0.75, 0.76, all p values <0.001) and testing (AUC of 0.893 vs. 0.704, 0.851, 0.888; C-index of 0.86 vs. 0.73, 0.85, 0.85, all p values <0.05) datasets.

CONCLUSIONS

The CCTA-based myocardial radiomics model is a valuable tool for predicting adverse outcomes in chronic MI, providing incremental value to conventional clinical and CMR parameters.

The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management

This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.

Altered Thyroid Feedback Loop in Klinefelter Syndrome: From Infancy Through the Transition to Adulthood

CONTEXT

It has been claimed that thyroid dysfunction contributes to the spectrum of Klinefelter syndrome (KS); however, studies are scarce.

OBJECTIVE

In a retrospective longitudinal study, we aimed at describing the hypothalamic-pituitary-thyroid (HPT) axis and thyroid ultrasonographic (US) appearance in patients with KS throughout the life span.

METHODS

A total of 254 patients with KS (25.9 ± 16.1 years) were classified according to their pubertal and gonadal status and compared with different groups of non-KS age-matched individuals with normal thyroid function, treated and untreated hypogonadism, or chronic lymphocytic thyroiditis. We assessed serum thyroid hormone levels, antithyroid antibodies, US thyroid parameters, and in vitro pituitary type 2 deiodinase (D2) expression and activity.

RESULTS

Thyroid autoimmunity was more prevalent among individuals with KS at all ages, although the antibody (Ab)-negative vs Ab-positive cohorts were not different. Signs of thyroid dysfunction (reduced volume, lower echogenicity, and increased inhomogeneity) were more prominent in KS than in euthyroid controls. Free thyroid hormones were lower in prepubertal, pubertal, and adult patients with KS, whereas thyrotropin values were lower only in adults. Peripheral sensitivity to thyroid hormones was unaltered in KS, suggesting a dysfunctional HPT axis. Testosterone (T) was the only factor associated with thyroid function and appearance. In vitro testing demonstrated an inhibitory effect of T on pituitary D2 expression and activity, supporting enhanced central sensing of circulating thyroid hormones in hypogonadism.

CONCLUSION

From infancy through adulthood, KS is characterized by increased morphofunctional abnormalities of the thyroid gland, combined with a central feedback dysregulation sustained by the effect of hypogonadism on D2 deiodinase.

The incremental value of CCTA-derived myocardial radiomics signature for ischemia diagnosis with reference to CT myocardial perfusion imaging

OBJECTIVES

To explore the incremental value of myocardial radiomics signature derived from static coronary computed tomography angiography (CCTA) for identifying myocardial ischemia based on stress dynamic CT myocardial perfusion imaging (CT-MPI).

METHODS

Patients who underwent CT-MPI and CCTA were retrospectively enrolled from two independent institutions, one used as training and the other as testing. Based on CT-MPI, coronary artery supplying area with relative myocardial blood flow (rMBF) value <0.8 was considered ischemia. Conventional imaging features of target plaques which caused the most severe narrowing of the vessel included area stenosis, lesion length (LL), total plaque burden, calcification burden, non-calcification burden, high-risk plaque (HRP) score, and CT fractional flow reserve (CT-FFR). Myocardial radiomics features were extracted at three vascular supply areas from CCTA images. The optimized radiomics signature was added to the conventional CCTA features to build the combined model (radiomics + conventional).

RESULTS

There were 168 vessels from 56 patients enrolled in the training set, and the testing set consisted of 135 vessels from 45 patients. From either cohort, HRP score, LL, stenosis ≥50% and CT-FFR ≤0.80 were associated with ischemia. The optimal myocardial radiomics signature consisted of nine features. The detection of ischemia using the combined model was significantly improved compared with conventional model in both training and testing set (AUC 0.789 vs 0.608, p < 0.001; 0.726 vs 0.637, p = 0.045).

CONCLUSIONS

Myocardial radiomics signature extracted from static CCTA combining with conventional features could provide incremental value to diagnose specific ischemia.

ADVANCES IN KNOWLEDGE

Myocardial radiomics signature extracted from CCTA may capture myocardial characteristics and provide incremental value to detect specific ischemia when combined with conventional features.

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.

Introduction to radiomics for a clinical audience

Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.

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.

Myocardial Radiomics Texture Features Associated with Increased Coronary Calcium Score—First Results of a Photon-Counting CT

The coronary artery calcium score is an independent risk factor of the development of adverse cardiac events. The severity of coronary artery calcification may influence the myocardial texture. Due to higher spatial resolution and signal-to-noise ratio, new CT technologies such as PCCT may improve the detection of texture alterations depending on the severity of coronary artery calcification. In this retrospective, single-center, IRB-approved study, left ventricular myocardium was segmented and radiomics features were extracted using pyradiomics. The mean and standard deviation with the Pearson correlation coefficient for correlations of features were calculated and visualized as boxplots and heatmaps. Random forest feature selection was performed. Thirty patients (26.7% women, median age 58 years) were enrolled in the study. Patients were divided into two subgroups depending on the severity of coronary artery calcification (Agatston score 0 and Agatston score ≥ 100). Through random forest feature selection, a set of four higher-order features could be defined to discriminate myocardial texture between the two groups. When including the additional Agatston 1–99 groups as a validation, a severity-associated change in feature intensity was detected. A subset of radiomics features texture alterations of the left ventricular myocardium was associated with the severity of coronary artery calcification estimated by the Agatston score.

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.

Comparison Study of Myocardial Radiomics Feature Properties on Energy-Integrating and Photon-Counting Detector CT

The implementation of radiomics-based, quantitative imaging parameters is hampered by a lack of stability and standardization. Photon-counting computed tomography (PCCT), compared to energy-integrating computed tomography (EICT), does rely on a novel detector technology, promising better spatial resolution and contrast-to-noise ratio. However, its effect on radiomics feature properties is unknown. This work investigates this topic in myocardial imaging. In this retrospective, single-center IRB-approved study, the left ventricular myocardium was segmented on CT, and the radiomics features were extracted using pyradiomics. To compare features between scanners, a t-test for non-paired samples and F-test was performed, with a threshold of 0.05 set as a benchmark for significance. Feature correlations were calculated by the Pearson correlation coefficient, and visualization was performed with heatmaps. A total of 50 patients (56% male, mean age 56) were enrolled in this study, with equal proportions of PCCT and EICT. First-order features were, nearly, comparable between both groups. However, higher-order features showed a partially significant difference between PCCT and EICT. While first-order radiomics features of left ventricular myocardium show comparability between PCCT and EICT, detected differences of higher-order features may indicate a possible impact of improved spatial resolution, better detection of lower-energy photons, and a better signal-to-noise ratio on texture analysis on PCCT.

Quality assessment of radiomics research in cardiac CT: a systematic review

OBJECTIVES

To assess the quality of current radiomics research on cardiac CT using radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) systems.

METHODS

Systematic searches of PubMed and EMBASE were performed to identify all potentially relevant original research articles about cardiac CT radiomics. Fifteen original research articles were selected. Two cardiac radiologists assessed the quality of the methodology adopted in those studies according to the RQS and TRIPOD guidelines. Basic adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science.

RESULTS

Among the 15 included articles, six (40%) were about coronary artery disease and six (40%) were about myocardial infarction. The mean RQS was 9.9 ± 7.3 (27.4% of the ideal score of 36), and the basic adherence rate was 44.6%. Fourteen (93.3%) and nine (60%) studies performed feature selection and validation, but only two (13.3%) of them performed external validation. Two studies (13.3%) were prospective, and only one study (6.7%) conducted calibration analysis and stated the potential clinical utility. None of the studies conducted phantom study and cost-effective analysis. The overall adherence rate for TRIPOD was 63%.

CONCLUSION

The quality of radiomics studies in cardiac CT is currently insufficient. A higher level of evidence is required, and analysis of clinical utility and calibration of model performance need to be improved.

KEY POINTS

• The quality of science of radiomics studies in cardiac CT is currently insufficient. • No study conducted a phantom study or cost-effective analysis, with further limitations being demonstrated in a high level of evidence for radiomics studies. • Analysis of clinical utility and calibration of model performance need to be improved, and a higher level of evidence is required.

Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram

BACKGROUND

Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics.

PURPOSE

This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS).

METHODS

CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram.

RESULTS

The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible.

CONCLUSION

The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.

Cardiac Computed Tomography Radiomics-Based Approach for the Detection of Left Ventricular Remodeling in Patients with Arterial Hypertension

The aim of the study is to verify the feasibility of a radiomics based approach for the detection of LV remodeling in patients with arterial hypertension. Cardiac Computed Tomography (CCT) and clinical data of patients with and without history of arterial hypertension were collected. In one image per patient, on a 4-chamber view, left ventricle (LV) was segmented using a polygonal region of interest by two radiologists in consensus. A total of 377 radiomics features per region of interest were extracted. After dataset splitting (70:30 ratio), eleven classification models were tested for the discrimination of patients with and without arterial hypertension based on radiomics data. An Ensemble Machine Learning (EML) score was calculated from models with an accuracy >60%. Boruta algorithm was used to extract radiomic features discriminating between patients with and without history of hypertension. Pearson correlation coefficient was used to assess correlation between EML score and septum width in patients included in the test set. EML showed an accuracy, sensitivity and specificity of 0.7. Correlation between EML score and LV septum width was 0.53 (p-value < 0.0001). We considered LV septum width as a surrogate of myocardial remodeling in our population, and this is the reason why we can consider the EML score as a possible tool to evaluate myocardial remodeling. A CCT-based radiomic approach for the identification of LV remodeling is possible in patients with past medical history of arterial hypertension.

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Purpose of Review

This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.

Recent Findings

Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.

Summary

The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.

The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications

Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we review the literature on artificial intelligence and its utility in cardiology. We provide a detailed description of concepts of artificial intelligence tools like machine learning, deep learning, and cognitive computing. This review discusses the current evidence, application, prospects, and limitations of artificial intelligence in cardiology.

Texture analysis imaging "what a clinical radiologist needs to know"

Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.

Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review

Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.

Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography

Objectives

To evaluate whether radiomics signature of pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could improve the prediction of future acute coronary syndrome (ACS) within 3 years.

Methods

We designed a retrospective case-control study that patients with ACS (n = 90) were well matched to patients with no cardiac events (n = 1496) during 3 years follow-up, then which were randomly divided into training and test datasets with a ratio of 3:1. A total of 107 radiomics features were extracted from PCAT surrounding lesions and 14 conventional plaque characteristics were analyzed. Radiomics score, plaque score, and integrated score were respectively calculated via a linear combination of the selected features, and their performance was evaluated with discrimination, calibration, and clinical application.

Results

Radiomics score achieved superior performance in identifying patients with future ACS within 3 years in both training and test datasets (AUC = 0.826, 0.811) compared with plaque score (AUC = 0.699, 0.640), with a significant difference of AUC between two scores in the training dataset (p = 0.009); while the improvement of integrated score discriminating capability (AUC = 0.838, 0.826) was non-significant. The calibration curves of three predictive models demonstrated a good fitness respectively (all p > 0.05). Decision curve analysis suggested that integrated score added more clinical benefit than plaque score. Stratified analysis revealed that the performance of three predictive models was not affected by tube voltage, CT version, different sites of hospital.

Conclusion

CCTA-based radiomics signature of PCAT could have the potential to predict the occurrence of subsequent ACS. Radiomics-based integrated score significantly outperformed plaque score in identifying future ACS within 3 years.

Key Points

• Plaque score based on conventional plaque characteristics had certain limitations in the prediction of ACS.

• Radiomics signature of PCAT surrounding plaques could have the potential to improve the predictive ability of subsequent ACS.

• Radiomics-based integrated score significantly outperformed plaque score in the identification of future ACS within 3 years.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08109-z.

Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease

ABSTRACT

Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.

Radiomics: The Next Frontier of Cardiac Computed Tomography

Radiomics uses advanced image analysis to extract massive amounts of quantitative information from digital images, which is not otherwise distinguishable to the human eye. The mined data can be used to explore and establish new and undiscovered correlations between these imaging features and clinical end points. Cardiac computed tomography (CT) is a first-line imaging modality for evaluating coronary artery disease and has a primary role in the assessment of cardiac structures. Conventional interpretation of cardiac CT images relies mostly on subjective and qualitative analysis, as well as basic geometric quantification. To date, some proof-of-concept studies have demonstrated the feasibility and diagnostic performance of cardiac CT radiomics analysis. This review describes the current literature on radiomics in cardiac CT and discusses its advantages, challenges, and future directions. Although much evidences are needed in this field, cardiac CT radiomics has a lot to offer to patients and physicians with potential to define cardiac disease phenotypes on imaging with higher precision.

Prognostic value of SPECT myocardial perfusion entropy in high-risk type 2 diabetic patients

PURPOSE

Risk stratification of patients with type 2 diabetes mellitus (T2D) remains suboptimal. We hypothesized that myocardial perfusion entropy (MPE) quantified from SPECT myocardial perfusion images may provide incremental prognostic value in T2D patients independently from myocardial ischemia.

METHODS

T2D patients with very high and high cardiovascular risk were prospectively included (n = 166, 65 ± 12 years). Stress perfusion defect was quantified by visual evaluation of SPECT MPI. SPECT MPI was also used for the quantification of rest and stress MPE. The primary end point was major adverse cardiac events (MACEs) defined as cardiac death, myocardial infarction (MI), and myocardial revascularization > 3 months after SPECT.

RESULTS

Forty-four MACEs were observed during a 4.6-year median follow-up. Significant differences in stress MPE were observed between patients with and without MACEs (4.19 ± 0.46 vs. 3.93 ± 0.40; P ≤ .01). By Kaplan-Meier analysis, the risk of MACEs was significantly higher in patients with higher stress MPE (log-rank P ≤ 01). Stress MPE and stress perfusion defect (SSS ≥ 4) were significantly associated with the risk of MACEs (hazard ratio 2.77 and 2.06, respectively, P < .05 for both) after adjustment for clinical and imaging risk predictors as identified from preliminary univariate analysis. MPE demonstrated incremental prognostic value over clinical risk factors, stress test EKG and SSS as evidenced by nested models showing improved Akaike information criterion (AIC), reclassification (global continuous net reclassification improvement [NRI]: 63), global integrated discrimination improvement (IDI: 6%), and discrimination (change in c-statistic: 0.66 vs 0.74).

CONCLUSIONS

Stress MPE provided independent and incremental prognostic information for the prediction of MACEs in diabetic patients.

TRIAL REGISTRATION NUMBER

NCT02316054 (12/12/2014).

Ultrasonography-Based Radiomics of Screening-Detected Ductal Carcinoma In Situ According to Visibility on Mammography

ABSTRACT

Ductal carcinoma in situ (DCIS) has different prognostic factors according to the detection modality. The purpose of this study was to compare parameters from a radiomic analysis of ultrasonography (US) images for DCIS detected on screening mammography (MMG) and US and detected on screening US only. A total of 154 surgically confirmed DCIS visible on US were included. Regions of interest were drawn onto US images of DCIS, and texture analysis was performed. Lesions were classified into those detected by both US and MMG (the US-MMG group) and those detected by US only (the US group). Analysis parameters were compared between the US-MMG group and the US group. Ninety-six lesions were included in the US-MMG group and 58 lesions in the US group. Energy, entropy, maximum, mean absolute deviation, range, SD, and variance were significantly higher in the US-MMG group than the US group. Kurtosis, skewness, and uniformity were significantly lower in the US-MMG group than the US group. Among the 22 gray-level cooccurrence matrix parameters, 18, 21, 22, 20, and 21 parameters were significantly different between the 2 groups in 0, 45, 90, and 135 degrees and the average value. Among the 11 gray-level run-length matrix parameters, 6, 6, 7, 7, and 6 parameters were significantly different in 0, 45, 90, and 135 degrees and the average value. Inverse variance and gray-level nonuniformity were the most different features between the 2 groups. Screening-detected DCIS showed different radiomic features according to the detection modality.

Artificial Intelligence and Texture Analysis in Cardiac Imaging

PURPOSE OF REVIEW

The aim of this structured review is to summarize the current research applications and opportunities arising from artificial intelligence (AI) and texture analysis with regard to cardiac imaging.

RECENT FINDINGS

Current research findings suggest tremendous potential for AI in cardiac imaging, especially with regard to objective image analyses, overcoming the limitations of an observer-dependent subjective image interpretation. Researchers have used this technique across multiple imaging modalities, for instance to detect myocardial scars in cardiac MR imaging, to predict contrast enhancement in non-contrast studies, and to improve image acquisition and reconstruction. AI in medical imaging has the potential to provide novel, much-needed applications for improving patient care pertaining to the cardiovascular system. While several shortcomings are still present in the current methodology, AI may serve as a resourceful assistant to radiologists and clinicians alike.

Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection

Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemic stroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segment the ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Steps followed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classification and optimal feature group (FG) selection. The first order features, gray level run length matrix features, gray level co-occurrence matrix features and Hu's moment features are extracted. Classification is done using logistic regression (LR), support vector machine classifier (SVMC), random forest classifier (RFC) and neural network classifier (NNC). This proposed approach effectively detects ischemic stroke lesion with a classification accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained by the LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.

Estimation of Renal Function Using Unenhanced Computed Tomography in Upper Urinary Tract Stones Patients

Objectives: The aim of this study was to determine whether unenhanced computed tomography (CT) imaging can estimate differential renal function (DRF) in patients with chronic unilateral obstructive upper urinary tract stones.

Materials and Methods: This was a single-center retrospective study of 76 patients. All the patients underwent unenhanced CT and nuclear renography (RG) at an interval of 4 to 6 weeks due to chronic unilateral obstructive urinary stones. Renal CT measurements (RCMs), including residual parenchymal volume (RPV) and volumetric CT texture analysis parameters, were obtained through a semiautomatic method. Percent RCMs were calculated and compared with renal function determined by RG.

Results: The strongest Pearson coefficient between percent RCM and DRF was reflected by RPV (r = 0.957, P < 0.001). Combinations of RPV and other parameters did not significantly improve the correlation compared with RPV alone (r = 0.957 vs. r = 0.957, 0.957, 0.887, 0.815, and 0.956 for combination with Hounsfield unit, parenchymal voxel, skewness, kurtosis, and entropy, respectively; all P < 0.001). Percent RPV was subsequently introduced into linear regression, and the equation y = −2.66 + 1.07^* × (P < 0.001) was derived to calculate predicted DRF. No statistically difference was found between predicted DRF using the equation and observed DRF according to RG (P = 0.959).

Conclusion: Unenhanced CT imaging can estimate DRF in patients with chronic unilateral obstructive upper urinary tract stones, and RG might not be necessary as a conventional method in clinical.

Computed tomography for myocardial characterization in ischemic heart disease: a state-of-the-art review

This review provides an overview of the currently available computed tomography (CT) techniques for myocardial tissue characterization in ischemic heart disease, including CT perfusion and late iodine enhancement. CT myocardial perfusion imaging can be performed with static and dynamic protocols for the detection of ischemia and infarction using either single- or dual-energy CT modes. Late iodine enhancement may be used for the analysis of myocardial infarction. The accuracy of these CT techniques is highly dependent on the imaging protocol, including acquisition timing and contrast administration. Additionally, the options for qualitative and quantitative analysis and the accuracy of each technique are discussed.

Artificial intelligence and radiomics in pediatric molecular imaging

In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and analysis of a large number of quantitative parameters from medical images, radiomics is an evolving field in precision medicine with the ultimate goal of the discovery of new imaging biomarkers for disease. Radiomics has already shown promising results in extracting diagnostic, prognostic, and molecular information latent in medical images. After acquisition of the medical images as part of the standard of care, a region of interest is defined often via a manual or semi-automatic approach. An algorithm then extracts and computes quantitative radiomics parameters from the region of interest. Whereas radiomics captures quantitative values of shape and texture based on predefined mathematical terms, neural networks have recently been used to directly learn and identify predictive features from medical images. Thereby, neural networks largely forego the need for so called "hand-engineered" features, which appears to result in significantly improved performance and reliability. Opportunities for radiomics and neural networks in pediatric nuclear medicine/radiology/molecular imaging are broad and can be thought of in three categories: automating well-defined administrative or clinical tasks, augmenting broader administrative or clinical tasks, and unlocking new methods of generating value. Specific applications include intelligent order sets, automated protocoling, improved image acquisition, computer aided triage and detection of abnormalities, next generation voice dictation systems, biomarker development, and therapy planning.

Image-Based Cardiac Diagnosis With Machine Learning: A Review

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.

Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

Background

Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification.

Methods

This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions.

Results

Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii).

Conclusions

TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

Electronic supplementary material

The online version of this article (10.1186/s41747-019-0121-6) contains supplementary material, which is available to authorized users.

Value of Computed Tomography Radiomic Features for Differentiation of Periprosthetic Mass in Patients With Suspected Prosthetic Valve Obstruction

Background:

We aimed to determine whether quantitative computed tomography radiomic features can aid in differentiating between the causes of prosthetic valve obstruction (PVO) in patients who had undergone prosthetic valve replacement.

Methods:

This retrospective study included 39 periprosthetic masses in 34 patients who underwent cardiac computed tomography scan from January 2014 to August 2017 and were clinically suspected as PVO. The cause of PVO was assessed by redo-surgery and follow-up imaging as standard reference, and classified as pannus, thrombus, or vegetation. Visual analysis was performed to assess the possible cause of PVO on axial and valve-dedicated views. Computed tomography radiomic analysis of periprosthetic masses was performed and radiomic features were extracted. The advantage of radiomic score compared with visual analysis for differentiation of pannus from other abnormalities was assessed.

Results:

Of 39 masses, there were 20 cases of pannus, 11 of thrombus, and 8 of vegetation on final diagnosis. The radiomic score was significantly higher in the pannus group compared with nonpannus group (mean, −0.156±0.422 versus −0.883±0.474; P <0.001). The area under the curve of radiomic score for diagnosis of pannus was 0.876 (95% CI, 0.731–0.960). Combination of radiomic score and visual analysis showed a better performance for the differentiation of pannus than visual analysis alone.

Conclusions:

Compared with visual analysis, computed tomography radiomic features may have added value for differentiating pannus from thrombus or vegetation in patients with suspected PVO.

Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible

OBJECTIVES

The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images.

MATERIALS AND METHODS

In this institutional review board-approved retrospective study, we included non-contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls. Texture analysis of the left ventricle was performed using free-hand regions of interest, and texture features were classified twice (Model I: controls versus acute MI versus chronic MI; Model II: controls versus acute and chronic MI). For both classifications, 6 commonly used machine learning classifiers were used: decision tree C4.5 (J48), k-nearest neighbors, locally weighted learning, RandomForest, sequential minimal optimization, and an artificial neural network employing deep learning. In addition, 2 blinded, independent readers visually assessed noncontrast CCT images for the presence or absence of MI.

RESULTS

In Model I, best classification results were obtained using the k-nearest neighbors classifier (sensitivity, 69%; specificity, 85%; false-positive rate, 0.15). In Model II, the best classification results were found with the locally weighted learning classification (sensitivity, 86%; specificity, 81%; false-positive rate, 0.19) with an area under the curve from receiver operating characteristics analysis of 0.78. In comparison, both readers were not able to identify MI in any of the noncontrast, low radiation dose CCT images.

CONCLUSIONS

This study indicates the ability of texture analysis and machine learning in detecting MI on noncontrast low radiation dose CCT images being not visible for the radiologists' eye.

Texture analysis of myocardial infarction in CT: Comparison with visual analysis and impact of iterative reconstruction

OBJECTIVES

To compare texture analysis (TA) with subjective visual diagnosis of myocardial infarction (MI) in cardiac computed tomography (CT) and to evaluate the impact of iterative reconstruction (IR).

METHODS

Ten patients (4 women, mean age 68 ± 11 years) with confirmed chronic MI and 20 controls (8 women, mean age 52 ± 11 years) with no cardiac abnormality underwent contrast-enhanced cardiac CT with the same protocol. Images were reconstructed with filtered back projection (FBP) and with advanced modeled IR at strength levels 3-5. Subjective diagnosis of MI was made by three independent, blinded readers with different experience levels. Classification of MI was performed using machine learning-based decision tree models for the entire data set and after splitting into training and test data to avoid overfitting.

RESULTS

Subjective visual analysis for diagnosis of MI showed excellent intrareader (kappa: 0.93) but poor interreader agreement (kappa: 0.3), with variable performance at different image reconstructions. TA showed high performance for all image reconstructions (correct classifications: 94%-97%, areas under the curve: 0.94-0.99). After splitting into training and test data, overall lower performances were observed, with best results for IR at level 5 (correct classifications: 73%, area under the curve: 0.65).

CONCLUSIONS

As compared with subjective, nonreliable visual analysis of inexperienced readers, TA enables objective and reproducible diagnosis of chronic MI in cardiac CT with higher accuracy. IR has a considerable impact on both subjective and objective image analysis.

Acute Myocardial Infarct

This article reviews the imaging manifestations of acute myocardial infarction (MI) on computed tomography (CT) accompanied by case examples and illustrations. This is preceded by a review of the pathophysiology of MI (acute and chronic), a summary of its clinical presentation, and a brief synopsis of the technical aspects of cardiac CT. Several examples of the appearance of acute MI and its complications are shown on routine and cardiac tailored CT, and a sample of the latest advances in imaging technique, including dual-energy CT, are introduced.