Machine Learning-based CT Radiomics Model Distinguishes COVID-19 From Non-COVID-19 Pneumonia (preprint)

Background: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19).Methods: In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the region of interest (ROI), and the radiomic features were extracted. The Support Vector Machine(SVM) model was built on the combination of the 4 groups of features, including radiomic features, traditional radiological features, quantifying features and clinical features, by repeated cross-validation procedure and the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results: For the SVM model that built on the combination of 4 groups of features(integrated model), the per-exam AUC of 0.925(95% CI: 0.856 to 0.994) was reached for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816(95% CI: 0.651 to 0.917) and 0.923(95% CI: 0.621 to 0.996), respectively. For the SVM models that built on radiomic features, radiological features, quantifying features and clinical features individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607 and 0.739 respectively, significantly lower than the integrated model, except for the radiomic model.Conclusion: The machine learning-based CT radiomics models may accurately detect COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.

Machine learning-based CT radiomics model Distinguishes COVID-19 from other viral pneumonia (preprint)

Purpose To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19).Methods In this retrospective study, a machine learning-based CT radiomics model was developed to extract features from chest CT exams for the detection of COVID-19. Other viral-pneumonia CT exams of the corresponding period were also included. The radiomics features extracted from the region of interest (ROI), the radiological features evaluated by the radiologists, the quantity features calculated by the AI segmentation and evaluation, and the clinical parameters including clinical symptoms, epidemiology history and biochemical results were enrolled in this study. The SVM model was built and the performance on the testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results For the SVM model that built on the radiomics features only, it reached an AUC of 0.688(95% CI 0.496 to 0.881) on the testing cohort. After the radiological features were enrolled, the AUC achieved 0.696(95% CI 0.501 to 0.892), then the AUC reached 0.753(95% CI 0.596 to 0.910) after the quantity features were included. Our final model employed all the features, reached the per-exam sensitivity and specificity for differentiating COVID-19 was 29 of 38 (0.763, 95% CI: 0.598 to 0.886]) and 12 of 13 (0.923, 95% CI: 0.640 to 0.998]), respectively, with an AUC of 0.968(95% CI 0.911 to 1.000). Conclusion The machine learning-based CT radiomics models may accurately detect COVID-19 and differentiate it from other viral pneumonia.