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10.1007/s00259-020-05075-4

http://scihub22266oqcxt.onion/10.1007/s00259-020-05075-4
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33094432!7581467!33094432
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suck abstract from ncbi


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pmid33094432      Eur+J+Nucl+Med+Mol+Imaging 2021 ; 48 (5): 1478-1486
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  • Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures #MMPMID33094432
  • Wang H; Wang L; Lee EH; Zheng J; Zhang W; Halabi S; Liu C; Deng K; Song J; Yeom KW
  • Eur J Nucl Med Mol Imaging 2021[May]; 48 (5): 1478-1486 PMID33094432show ga
  • 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.
  • |*COVID-19[MESH]
  • |*Deep Learning[MESH]
  • |China[MESH]
  • |Humans[MESH]
  • |Retrospective Studies[MESH]
  • |SARS-CoV-2[MESH]


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