Fig. 4: Prediction models based on plasma proteome for biopsy-verified fibrosis, inflammation and steatosis.
From: Noninvasive proteomic biomarkers for alcohol-related liver disease
a–i, Prediction models. a,d,g. ROC–AUC statistics in fivefold cross-validation repeated ten times in a protein panel-based logistic regression model for detection of significant liver fibrosis (F2–4) (a), mild inflammatory activity (NAS I2–5) (d) and any steatosis (NAS S1–3) (g). b,c,e,f,h,i, F1 score (b,e,h) and balanced accuracy (c,f,i) in cross-validation of the above-mentioned logistic regression models in comparison with best-in-class existing markers for fibrosis (b,c), inflammation (e,f) and steatosis (h,i). Performance of existing markers was calculated based on both their established clinical cutoffs if applicable (indicated by ‘test’) and machine learning cutoffs (indicated by ‘model’). Error bars represent s.d. b,c,e,f,h,i, n = 50 independent experiments in the fivefold 10× cross-validation procedure; data presented as mean ± s.d.
