This study established an interpretable machine learning model to predict the severity of coronavirus disease 2019 (COVID-19) and output the most crucial deterioration factors. Clinical information, laboratory tests, and chest computed tomography (CT) scans at admission were collected. Two experienced radiologists reviewed the scans for the patterns, distribution, and CT scores of lung abnormalities. Six machine learning models were established to predict the severity of COVID-19. After parameter tuning and performance comparison, the optimal model was explained using Shapley Additive explanations to output the crucial factors. This study enrolled and classified 198 patients into mild ( n = 162 ; 46.93 ± 14.49 years old) and severe ( n = 36 ; 60.97 ± 15.91 years old) groups. The severe group had a higher temperature ( 37.42 ± 0.99 °C vs. 36.75 ± 0.66 °C), CT score at admission, neutrophil count, and neutrophil-to-lymphocyte ratio than the mild group. The XGBoost model ranked first among all models, with an AUC, sensitivity, and specificity of 0.924, 90.91%, and 97.96%, respectively. The early stage of chest CT, total CT score of the percentage of lung involvement, and age were the top three contributors to the prediction of the deterioration of XGBoost. A higher total score on chest CT had a more significant impact on the prediction. In conclusion, the XGBoost model to predict the severity of COVID-19 achieved excellent performance and output the essential factors in the deterioration process, which may help with early clinical intervention, improve prognosis, and reduce mortality.

Objective: To compare and evaluate radiomics models to preoperatively predict b-catenin mutation in patients with hepatocellular carcinoma (HCC).Methods: Ninety-eight patients who underwent preoperative gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were retrospectively included. Volumes of interest were manually delineated on arterial phase, portal venous phase, delay phase, and hepatobiliary phase (HBP) images. Radiomics features extracted from different combinations of imaging phases were analyzed and validated. A linear support vector classifier was applied to develop different models.Results: Among all 15 types of radiomics models, the model with the best performance was seen in the R HBP radiomics model. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity of the R HBP radiomics model in the training and validation cohorts were 0.86 (95% confidence interval [CI], 0.75-0.93), 0.75, 1.0, and 0.65 and 0.82 (95% CI, 0.63-0.93), 0.73, 0.67, and 0.76, respectively. The combined model integrated radiomics features in the R HBP radiomics model, and signatures in the clinical model did not improve further compared to the single HBP radiomics model with AUCs of 0.86 and 0.76. Good calibration for the best R HBP radiomics model was displayed in both cohorts; the decision curve showed that the net benefit could achieve 0.15. The most important radiomics features were low Frontiers in Oncology frontiersin.org 01

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