A Radiomics Approach to Analyze Cardiac Alterations in Hypertension

Cetin, Irem; Petersen, Steffen E.; Napel, Sandy; Cámara, Óscar; Ballester, Miguel Ángel González; Lekadir, Karim

doi:10.1109/isbi.2019.8759440

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…For feature selection, we used the sequential forward feature selection (SFFS) method as it has demonstrated good performance in previous CMR radiomics studies (15,26). The termination criterion was set to 2% in all experiments following literature standards, i.e., the process was stopped if an added feature did not increase model performance beyond the termination criterion.…”

Section: Identification Of Optimal Radiomic Signaturesmentioning

confidence: 99%

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

Cetin

Raisi‐Estabragh

Petersen

et al. 2020

Front. Cardiovasc. Med.

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Cardiovascular magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping by analyzing multiple quantifiers of shape and tissue texture. In this paper, we assess, in the largest sample published to date, the performance of CMR radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors. We evaluated five risk factor groups from the first 5,065 UK Biobank participants: hypertension (n = 1,394), diabetes (n = 243), high cholesterol (n = 779), current smoker (n = 320), and previous smoker (n = 1,394). Each group was randomly matched with an equal number of healthy comparators (without known cardiovascular disease or risk factors). Radiomics analysis was applied to short axis images of the left and right ventricles at end-diastole and end-systole, yielding a total of 684 features per study. Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. We evaluated the degree of separation achieved by the identified radiomics signatures using area under curve (AUC), receiver operating characteristic (ROC), and statistical testing. Logistic regression with L1-regularization was the optimal ML model. Compared to conventional imaging indices, radiomics signatures improved the discrimination of risk factor vs. healthy subgroups as assessed by AUC [diabetes: 0.80 vs. 0.70, hypertension: 0.72 vs. 0.69, high cholesterol: 0.71 vs. 0.65, current smoker: 0.68 vs. 0.65, previous smoker: 0.63 vs. 0.60]. Furthermore, we considered clinical interpretation of risk-specific radiomics signatures. For hypertensive individuals and previous smokers, the surface area to volume ratio was smaller in the risk factor vs. healthy subjects; perhaps reflecting a pattern of global concentric hypertrophy in these conditions. In the diabetes subgroup, the most discriminatory radiomics feature was the median intensity of the myocardium at end-systole, which suggests a global alteration at the myocardial tissue level. Cetin et al. CMR-Radiomics Due to CV-RF This study confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease. We demonstrate such analysis may have utility beyond conventional CMR metrics for improved detection and understanding of the early effects of cardiovascular risk factors on cardiac structure and tissue.

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Section: Identification Of Optimal Radiomic Signaturesmentioning

confidence: 99%

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

Cetin

Raisi‐Estabragh

Petersen

et al. 2020

Front. Cardiovasc. Med.

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“…It is problematic to directly train or to fine-tune a deep learning model due to the need of sufficient data in the training stage of deep classification models. To deal with the limited data, similar to [57], five-fold-cross validation is used to choose the most suitable model for robustly predicting the diagnostic of new arrival patients and new examinations.Moreover, the pretrained model can be directly used to extract highly representative features for the early detection of TLI. Regarding the choice of feature compression methods, roipooling [58], designed for the input images with random size/length, was not added into our comparison.…”

Section: Discussionmentioning

confidence: 99%

Deep Longitudinal Feature Representations for Detection of Postradiotherapy Brain Injury at Presymptomatic Stage

Zhong

Zhang

et al. 2020

IEEE Access

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Temporal lobe injury (TLI), a form of nervous system damage in the brain, is a major neurological complication after radiation therapy (RT). TLI must be highly valued because of the irreversible brain injury. This paper aims to develop a predictive pipeline, called deep longitudinal feature representations (DLFR), to detect TLI at the presymptomatic stage accurately via the learning of effective deep longitudinal feature representations. DLFR characterizes high-level information and developmental changes within and across subjects The DLFR consists of four components: (i) extraction of deep features from a pretrained ResNet50 model; (ii) compression of learned highly representative features by the global max pooling; (iii) fusion of deep longitudinal features for the fully use of all follow-up data; (iv) random forest-based prediction of the diagnostic status. In total, 244 nasopharyngeal carcinoma patients before and after RT with a follow-up period of 0 ∼ 9 years were included for analysis. All patients were divided into four different latency groups, and the current latency was used for training to predict the diagnostic status of the next latency. The AUCs of the predicted three different latency groups using DLFR were 0.64 ± 0.11, 0.76 ± 0.10, and 0.88 ± 0.05, while those of radiomics features were 0.56 ± 0.06, 0.63 ± 0.03, and 0.53 ± 0.04, and those of histogram of oriented gradients features were 0.60 ± 0.09, 0.52 ± 0.03, and 0.58 ± 0.06. Most importantly, the AUCs of the predicted three different latency groups for white matter regions were 0.66 ± 0.10, 0.80 ± 0.09, and 0.78 ± 0.09. Our proposed method can dynamically detect TLI at the presymptomatic stage, which can enable the administration of preventive neurological intervention.

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“…The resultant accuracy was 0.87, significantly higher than the clinician's results, 0.7. Cetin et al (38) identified HHD from healthy controls in 200 subjects with SVM and sequential forward feature selection. The predictive power of selected radiomics (AUC = 0.76) was substantially improved compared to conventional indices (AUC = 0.62).…”

Section: Dimensionality Reduction and Feature Selectionmentioning

confidence: 99%

Image-Based Cardiac Diagnosis With Machine Learning: A Review

Martín-Isla

Campello

Izquierdo

et al. 2020

Front. Cardiovasc. Med.

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177

118

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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.

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A Radiomics Approach to Analyze Cardiac Alterations in Hypertension

Cited by 12 publications

References 10 publications

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

Deep Longitudinal Feature Representations for Detection of Postradiotherapy Brain Injury at Presymptomatic Stage

Image-Based Cardiac Diagnosis With Machine Learning: A Review

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