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.
【저자키워드】 machine learning, Explainable AI, coronavirus disease 2019 pneumonia, CT chest, AI interpretability, 【초록키워드】 COVID-19, deep learning, Pneumonia, hospital, Human, diagnostic, Diagnosis, China, Viral pneumonia, sensitivity, specificity, Cohort, Features, Sensitivity and specificity, outbreak, Patient, USA, machine learning algorithm, external validation, Predictive, Radiologists, Non-COVID-19, Classifier, supplementary material, clinical symptom, positive, LASSO, Deep Learning Method, feature, Result, collected, develop, were used, underlie, CT sign, patients with COVID-19, pneumonia lesion, with COVID-19, 【제목키워드】 COVID-19, deep learning,