Severity detection of COVID-19 infection with machine learning of clinical records and CT images.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.

Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study.

PURPOSE: Coronavirus disease 2019 (COVID-19) is a recently declared worldwide pandemic. Triaging of patients into severe and non-severe could further help in targeted management. "Potential severe patients" is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT-based radiomics signature for the prediction of these potential severe COVID-19 patients. METHODS: One hundred fifty COVID-19 patients were enrolled and randomly divided into cross-validation and independent test sets. First, their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the inter- and intra-class correlation coefficients (ICC) analysis, minimum-redundancy maximum-relevance (mRMR) selection, and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow test and Delong test. RESULTS: Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR, and LASSO methods resulted in the selection of ten radiomics features, which made up of the radiomics signature. The differences in the radiomics signature between the potential severe and non-severe groups in cross-validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models. CONCLUSIONS: A CT-based radiomics signature for the prediction of potential severe COVID-19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID-19 pandemic prevention and control.