Multicenter validation of an explainable machine-learning model for APL, AMoL, and ALL

Background Acute leukemias require rapid subtype identification to guide timely treatment. Differentiation of acute lymphoblastic leukemia (ALL), acute monocytic leukemia (AMoL/M5), and acute promyelocytic leukemia (APL/M3) often relies on specialized tests that may not be immediately available. This study evaluated differentiation only among patients already confirmed to have one of these three diagnoses, rather than screening an unselected population with suspected acute leukemia. Methods This multicenter retrospective study included 9,398 newly diagnosed, previously untreated patients with established APL/M3, AMoL/M5, or ALL between May 2019 and May 2025 at four tertiary hospitals in China. Relapse, remission, follow-up, and non-initial disease episodes were excluded. Twenty-four routine hematological parameters were evaluated. One center served as the development cohort with five-fold stratified cross-validation, while three independent centers were reserved for external validation. Ten machine-learning algorithms were compared. Performance was assessed using AUC, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. SHapley Additive exPlanations (SHAP) supported feature selection and interpretation. Results An 18-feature random forest model achieved a development-stage cross-validated AUC of 0.93 and external AUCs of 0.89, 0.87, and 0.90. Overall sensitivity, specificity, PPV, NPV, accuracy, and F1 score were 0.78, 0.90, 0.81, 0.91, 0.81, and 0.79, respectively. SHAP identified leukocyte, erythrocyte, and platelet indices as the most important features. A Streamlit application was developed for clinical evaluation. Using a development-derived APL alert threshold of 0.0966, APL sensitivity was 99.02% in development and 96.32%–97.94% across external cohorts, with PPVs of 54.34% and 35.58%–84.25%, respectively. External validation showed center-specific calibration shifts, indicating that predicted probabilities should not be interpreted as universally calibrated absolute diagnostic probabilities. Conclusions Among patients already confirmed to have APL/M3, AMoL/M5, or ALL, this explainable model enabled low-cost preliminary differentiation using routine hematological data. It may support subtype prioritization and confirmatory testing but cannot replace standard diagnostics, identify other diagnoses, or screen unselected patients. Prospective multinational and workflow-based validation is required before routine implementation.