Topological Data Analysis of Vascular Disease: A Theoretical Framework

Nicponski, John; Jung, Jae-Hun

doi:10.3389/fams.2020.00034

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…Specifically, diagnostic tests such as the ECG have provided the first applications of TDA in converting simple waveforms to numeric data 23,24 . More recently, TDA has been proposed as a method for the assessment of vascular diseases 25 and has even provided improved predictive capacity for detecting acute coronary syndrome or revascularization in patients with coronary plaques than through the use of more commonly used clinical markers, such as risk factors, stenosis, and high-risk plaque features 26 .…”

Section: Discussionmentioning

confidence: 99%

A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data

Siva,

Singh,

Hathaway

et al. 2024

Sci Rep

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To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the “pattern” of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.

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Section: Discussionmentioning

confidence: 99%

A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data

Siva,

Singh,

Hathaway

et al. 2024

Sci Rep

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“…Atherosclerosis, a condition characterized by the buildup of plaque on the inner walls of arteries, is a common cause of arterial stenosis. Nicponski and collaborators [36] demonstrated the use of persistent homology to assess the severity of stenosis in different types of stenotic vessels. They introduced the concept of critical failure value, which applies onedimensional homology to these vessels as a way to quantify the degree of stenosis.…”

Section: Stenosis and Vascular Datamentioning

confidence: 99%

Topological Data Analysis in Cardiovascular Signals: An Overview

Hernández-Lemus,

Miramontes,

Martínez-García

2024

Entropy

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Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

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“…PH is readily computable [26], robust to noise [27] and its outputs are interpretable. In recent years, improved computational feasibility of PH has increased its applications to (high-dimensional) data in many contexts [28,29], including studies of the shape of brain arteries [30], neurons [31], the neural code [32], airways [33], stenosis [34], zebrafish patterns [35], ion aggregation [36], contagion dynamics [37], proteins [38], spatial networks [39][40][41][42][43], and geometric anomalies [44]. In oncology, PH has been used to construct new biomarkers [40,45,46], to classify tumours [47,48] and genetic alterations [49] and to quantify patterns of immune cell infiltration into solid tumours [50].…”

Section: Introductionmentioning

confidence: 99%

The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia

Chulián,

Stolz,

Martínez-Rubio

et al. 2023

PLoS Comput Biol

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Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and “empty spaces” in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as ‘low risk’. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies.

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Topological Data Analysis of Vascular Disease: A Theoretical Framework

Cited by 7 publications

References 32 publications

A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data

A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data

Topological Data Analysis in Cardiovascular Signals: An Overview

The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia

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