qbew4lsu

Property	Value
?:abstract	PURPOSE: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework. METHODS: A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA. All the pneumonia lesions on CT images were manually delineated by four radiologists. One hundred eighty-four patients (n = 93 COVID-19 positive; n = 91 COVID-19 negative; 24,216 pneumonia lesions from 12,001 CT image slices) from two hospitals from China served as discovery cohort for model development. Thirty-two patients (17 COVID-19 positive, 15 COVID-19 negative; 7883 pneumonia lesions from 3799 CT image slices) from a US hospital served as external validation cohort. A bi-directional adversarial network-based framework and PyRadiomics package were used to extract deep learning and radiomics features, respectively. Linear and Lasso classifiers were used to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia. RESULTS: 120-dimensional deep learning image features and 120-dimensional radiomics features were extracted. Linear and Lasso classifiers identified 32 high-dimensional deep learning image features and 4 radiomics features associated with COVID-19 pneumonia diagnosis (P < 0.0001). Both models achieved sensitivity > 73% and specificity > 75% on external validation cohort with slight superior performance for radiomics Lasso classifier. Human expert diagnostic performance improved (increase by 16.5% and 11.6% in sensitivity and specificity, respectively) when using a combined deep learning-radiomics model. CONCLUSIONS: We uncover specific deep learning and radiomics features to add insight into interpretability of machine learning algorithms and compare deep learning and radiomics models for COVID-19 pneumonia that might serve to augment human diagnostic performance. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00259-020-05075-4) contains supplementary material, which is available to authorized users.
is ?:annotates of	<https://research.tib.eu/covid-19/entity/qbew4lsu_hasAnnotation_C0020517> <https://research.tib.eu/covid-19/entity/qbew4lsu_hasAnnotation_C1457887> <https://research.tib.eu/covid-19/entity/qbew4lsu_hasAnnotation_C1514562>
?:creator	<https://research.tib.eu/covid-19/entity/Wang%2C_Hongmei%3B_Wang%2C_Lu%3B_Lee%2C_Edward_H.%3B_Zheng%2C_Jimmy%3B_Zhang%2C_Wei%3B_Halabi%2C_Safwan%3B_Liu%2C_Chunlei%3B_Deng%2C_Kexue%3B_Song%2C_Jiangdian%3B_Yeom%2C_Kristen_W.>
?:doi	10.1007/s00259-020-05075-4
?:doi	<https://research.tib.eu/covid-19/entity/10.1007/s00259-020-05075-4>
?:journal	Eur_J_Nucl_Med_Mol_Imaging
?:license	cc-by
?:pdf_json_files	document_parses/pdf_json/a1ec44fc3da3b7dfe482bc23aeefea2af3a57cd2.json
?:pmc_json_files	document_parses/pmc_json/PMC7581467.xml.json
?:pmcid	<https://research.tib.eu/covid-19/entity/PMC7581467>
?:pmid	<https://research.tib.eu/covid-19/entity/33094432.0>
?:pmid	33094432.0
?:publication_isRelatedTo_Disease	<https://research.tib.eu/covid-19/entity/COVID-19>
?:sha_id	<https://research.tib.eu/covid-19/entity/a1ec44fc3da3b7dfe482bc23aeefea2af3a57cd2>
?:source	Medline; PMC
?:title	Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures
?:type	<https://research.tib.eu/covid-19/vocab/Publication>
?:year	2020-10-23

Property

Value

?:abstract

PURPOSE: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework. METHODS: A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA. All the pneumonia lesions on CT images were manually delineated by four radiologists. One hundred eighty-four patients (n = 93 COVID-19 positive; n = 91 COVID-19 negative; 24,216 pneumonia lesions from 12,001 CT image slices) from two hospitals from China served as discovery cohort for model development. Thirty-two patients (17 COVID-19 positive, 15 COVID-19 negative; 7883 pneumonia lesions from 3799 CT image slices) from a US hospital served as external validation cohort. A bi-directional adversarial network-based framework and PyRadiomics package were used to extract deep learning and radiomics features, respectively. Linear and Lasso classifiers were used to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia. RESULTS: 120-dimensional deep learning image features and 120-dimensional radiomics features were extracted. Linear and Lasso classifiers identified 32 high-dimensional deep learning image features and 4 radiomics features associated with COVID-19 pneumonia diagnosis (P < 0.0001). Both models achieved sensitivity > 73% and specificity > 75% on external validation cohort with slight superior performance for radiomics Lasso classifier. Human expert diagnostic performance improved (increase by 16.5% and 11.6% in sensitivity and specificity, respectively) when using a combined deep learning-radiomics model. CONCLUSIONS: We uncover specific deep learning and radiomics features to add insight into interpretability of machine learning algorithms and compare deep learning and radiomics models for COVID-19 pneumonia that might serve to augment human diagnostic performance. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00259-020-05075-4) contains supplementary material, which is available to authorized users.

is ?:annotates of