Noninvasive prediction of severe histopathology in drug-induced liver injury using a dual elastography-based machine learning model

Objectives

To develop and validate a machine learning (ML) model integrating dual elastography, clinical features, and serum biomarkers for noninvasive prediction of severe drug-induced liver injury (DILI).

Materials and methods

This prospective multicenter study enrolled consecutive DILI patients undergoing liver biopsy and dual elastography. Severe DILI was defined as Scheuer inflammation grade plus fibrosis stage ≥ 5 (G + S ≥ 5). Dual elastography-derived activity index (A index) and fibrosis index (F index) correlated with pathological inflammation (G0–4) and fibrosis (S0–4) stages. The dataset was stratified and split 7:3 into training and test sets. LASSO regression was applied for feature selection. Eight ML models were constructed and compared, optimized using 5-fold cross-validation and Bayesian methods. Performance was evaluated by area under the curve (AUC), sensitivity, and specificity. SHapley Additive exPlanations (SHAP) were used to interpret the models.

Results

A total of 305 participants were included (median age 49 years, IQR 40–56; 98 male), comprising 55 with severe DILI and 250 without. A and F indices increased with inflammation grade and fibrosis stage, respectively (p < 0.01). Combining clinical and dual elastography features with serum biomarkers, the optimized regularized regression model performed best in the test set (AUC 0.862 [95% CI: 0.776–0.947]; sensitivity 81.2%; specificity 74.7%). SHAP analysis identified the dual elastography indices collectively as the dominant predictors. An online risk calculator was developed from this model.

Conclusion

We developed an explainable, high-performing and dual elastography-based ML model to predict severe DILI, facilitating risk stratification and preliminary management.

Key Points

Question Severe drug-induced liver injury (DILI) leads to a poor prognosis, yet early noninvasive identification remains challenging due to non-specific serum markers and invasive biopsy.

Findings The regularized regression model integrating dual elastography, clinical features and serum biomarkers, non-invasively and robustly predicted severe histologic injury in DILI.

Clinical relevance This dual elastography-based machine learning model offers a noninvasive solution for the early identification of DILI patients with severe tissue injury, minimizing unnecessary biopsy and improving prognosis. Clinicians can utilize the online tool for real-time risk stratification at: https://wznng666.shinyapps.io/RR55555/.

Graphical Abstract