Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign

Kolossváry, Márton; Karády, Júlia; Szilveszter, Bálint; Kitslaar, Pieter H.; Hoffmann, Udo; Merkely, Béla; Maurovich-Horvat, Pál

doi:10.1161/circimaging.117.006843

Cited by 185 publications

(129 citation statements)

References 33 publications

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“…In recent years, Radiomics has been applied to many health- investigations, such as identifying the coronary plaques [33]. In neurology, it is widely applicable for detecting Alzheimer's disease [34] and Parkinson's disease [35].…”

Section: Applicationsmentioning

confidence: 99%

From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities

Afshar

Mohammadi

Plataniotis

et al. 2019

IEEE Signal Process. Mag.

239

177

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Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in "Radiomics". Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional Radiomics workflow is typically based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Nevertheless, recent advancements in deep learning have caused trends towards deep learning-based Radiomics (also referred to as discovery Radiomics). Considering the advantages of these two approaches, there are also hybrid solutions developed to exploit the potentials of multiple data sources. Considering the variety of approaches to Radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article. This manuscript provides a unique interdisciplinary perspective on Radiomics by discussing state-of-the-art signal processing solutions in the context of Radiomics.

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Section: Applicationsmentioning

confidence: 99%

From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities

Afshar

Mohammadi

Plataniotis

et al. 2019

IEEE Signal Process. Mag.

239

177

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show abstract

“…Feature extraction, reduction and model construction were performed on R software, which is a free and open-source tool. 22 SPSS (version 17.0) was used for the correlation tests.…”

Section: Discussionmentioning

confidence: 99%

“…This result was strongly consistent with those of previous studies. 22,26,27 Among all the 10 features, most of them were higherorder statistics features and utilised wavelet transform. Higher-order statistics features could display subtle alterations in tissue morphology more explicitly.…”

Section: Discussionmentioning

confidence: 99%

Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI

et al. 2019

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“…Previous radiomics studies showed that the quantitative radiomic features could represent the changes in pathology and gene level, and thus had encouraging performance in cancer diagnosis, treatment outcome prediction, and prognosis prediction [20][21][22][23]. Besides application in cancer, radiomics has also been used in the heart diseases, such as hypertrophic cardiomyopathy [24] and coronary heart disease [25]. Furthermore, it has also been applied to differentiate primary progressive pulmonary tuberculosis from community-acquired pneumonia in children [26].…”

Section: Introductionmentioning

confidence: 99%

CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study

Fang

et al. 2020

Sci. China Inf. Sci.

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The coronavirus disease 2019 is raging across the world. The radiomics, which explores huge amounts of features from medical image for disease diagnosis, may help the screen of the COVID-19. In this study, we aim to develop a radiomic signature to screen COVID-19 from CT images. We retrospectively collect 75 pneumonia patients from Beijing Youan Hospital, including 46 patients with COVID-19 and 29 other types of pneumonias. These patients are divided into training set (n = 50) and test set (n = 25) at random. We segment the lung lesions from the CT images, and extract 77 radiomic features from the lesions. Then unsupervised consensus clustering and multiple cross-validation are utilized to select the key features that are associated with the COVID-19. In the experiments, while twenty-three radiomic features are found to be highly associated with COVID-19, four key features are screened and used as the inputs of support vector machine to build the radiomic signature. We use area under the receiver operating characteristic curve (AUC) and calibration curve to assess the performance of our model. It yields AUCs of 0.862 and 0.826 in the training set and the test set respectively. We also perform the stratified analysis and find that its predictive ability is not affected by gender, age, chronic disease and degree of severity. In conclusion, we investigate the value of radiomics in screening COVID-19, and the experimental results suggest the radiomic signature could be a potential tool for diagnosis of COVID-19.

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Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign

Abstract: Supplemental Digital Content is available in the text.

Cited by 185 publications

References 33 publications

From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities

From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities

Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI

CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study

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