Liver stiffness-platelet ratio index and machine learning models for the noninvasive diagnosis of significant fibrosis in chronic hepatitis B

Figure 1 Study workflow and analytic pipeline. Treatment-naïve chronic hepatitis B patients were screened, eligible participants were enrolled, and fibrosis stages were recorded using Scheuer scoring (including intermediate half-stages). Nonsignificant fibrosis was defined as S0-S1.5 and significant fibrosis as S2-S4. The liver stiffness-platelet ratio index was derived from liver stiffness measurement and platelet count and evaluated alongside conventional noninvasive tests and machine learning/deep learning models using a 70%/30% training/validation split. DL: Deep learning; LPRI: Liver stiffness measurement to platelet ratio index; LSM: Liver stiffness measurement; ML: Machine learning; PLT: Platelet count; T_low/T_high: LPRI thresholds for triage.

Figure 2 Comparison of feature selection methods. A: SelectKBest; B: Recursive feature elimination; C: Least absolute shrinkage and selection operator regression. AFP: Alpha-fetoprotein; ALP: Alkaline phosphatase; GGT: Gamma-glutamyl transferase; HBV: Hepatitis B virus; HBeAb: Hepatitis B e antibody; HBeAg: Hepatitis B e antigen; HBsAb: Hepatitis B surface antibody; HBsAg: Hepatitis B surface antigen; INR: International normalized ratio; WBC: White blood cell.

Figure 3 Receiver operating characteristic curves for eight noninvasive tests in the training set and validation set. A: Training set; B: Validation set. Pairwise DeLong tests are reported in Supplementary Table 1. aMAP: Age-male-alkaline phosphatase-platelets risk score; APAG: Aspartate aminotransferase to platelet and age-gender model; APRI: Aspartate aminotransferase to platelet ratio index; AUC: Area under the curve; FIB-4: Fibrosis-4; GPR: Gamma-glutamyl transpeptidase to platelet ratio; LPRI: Liver stiffness-platelet ratio index.

Figure 4 Receiver operating characteristic curves comparing machine learning/deep learning models trained on 26 baseline features and 26 baseline features plus liver stiffness-platelet ratio index on the imbalanced dataset. A: 26 baseline features; B: 26 baseline features plus liver stiffness-platelet ratio index on the imbalanced dataset. AUC: Area under the curve; KAN: Kolmogorov-Arnold Network; NuSVC: Nu-support vector classification; SVM: Support vector machine.

Figure 5 Global feature importance assessed by SHapley Additive exPlanations. A-C: Models without liver stiffness-platelet ratio index – random forest, gradient boosting, and histogram-based gradient boosting; D-F: Corresponding models with liver stiffness-platelet ratio index. AFP: Alpha-fetoprotein; ALB: Albumin; ALP: Alkaline phosphatase; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; DBIL: Direct bilirubin; GLB: Globulin; HBV: Hepatitis B virus; HBeAb: Hepatitis B e antibody; HBeAg: Hepatitis B e antigen; HBsAb: Hepatitis B surface antibody; HBsAg: Hepatitis B surface antigen; HGB: Hemoglobin; INR: International normalized ratio; LPRI: Liver stiffness-platelet ratio index; LSM: Liver stiffness measurement; NE: Neutrophil; PLT: Platelet; PT: Prothrombin time; TBIL: Total bilirubin; WBC: White blood cell; γGGT: Gamma-glutamyl transferase.

Figure 6 Proposed clinical workflow for liver stiffness-platelet ratio index-based triage of significant fibrosis in treatment-naïve chronic hepatitis B. Patients are stratified into rule-out, indeterminate, and rule-in zones using lower and upper liver stiffness-platelet ratio index thresholds; complex machine learning/deep learning models may provide incremental accuracy in selected settings. aMAP: Age-male-alkaline phosphatase-platelets risk score; APAG: Aspartate aminotransferase to platelet and age-gender model; APRI: Aspartate aminotransferase to platelet ratio index; FIB-4: Fibrosis-4; GPR: Gamma-glutamyl transpeptidase to platelet ratio; KAN: Kolmogorov-Arnold Network; LPRI: Liver stiffness-platelet ratio index; NIT: Noninvasive test; PF: Random forest; S Index: S-Index; SVM: Support vector machine.