Fig. 3From: CT-based radiomic nomogram for predicting the severity of patients with COVID-19Feature selection and radiomics signature building using the least absolute shrinkage and selection operator (LASSO) binary logistic regression. A The parameter (Ī») was screened using tenfold cross-validation method and parameter (Ī») between two dotted lines was the optimal values using the minimum criteria and the 1 standard error of the minimum criteria (the 1SE criteria). B LASSO coefficient profiles of the 14 radiological features. A coefficient profile plot was conducted against the log (Ī») sequence. Ī» value of 0.052, with log(Ī»)ā=āāā2.96 was selected (1āāāSE criteria) based on tenfold cross-validation. Vertical line was drawn at the value selected, which resulted in 4 non-zero coefficients. C, D Significant difference in radiomics score is shown between non-severe and severe groups in primary (C) and validation cohorts (D). E, F The receiver operating characteristic (ROC) curve for the radiomics signature. The calibration curves represent calibration of radiomics signature in terms of consistency between the predicted severe probabilities of COVID-19 and observed severe probabilities of COVID-19. The x-axis represents the predicted severe probabilities while the y-axis represents the actual probabilities. The actual probability was calculated by the formula PAā=ā[1ā+āexpāāā(axā+āb)]āā1, where xā=ālogit (p), p is the predicted probability, a is the slope estimates, and b is the corrected intercept. The 45Ā° dotted line represents the perfect prediction of an ideal model and the dotted lines represents the performance of the built nomogram model, a closer fit to the dotted line represents a better predictionBack to article page