Prediction of B/T Subtype and ETV6–RUNX1 Translocation in Pediatric Acute Lymphoblastic Leukemia by Deep Learning Analysis of Giemsa-Stained Whole Slide Images of Bone Marrow Aspirates

Background: Accurate determination of B/T-cell lineage and the presence of the ETV6–RUNX1 translocation is critical for diagnosing acute lymphoblastic leukemia (ALL), as these factors influence treatment decisions and outcomes. However, these diagnostic processes often rely on advanced tools unavailable in low-resource settings, creating a need for alternative solutions. Procedure: We developed a deep learning pipeline to analyze Giemsa-stained bone marrow (BM) aspirate smears. The models were trained to distinguish between ALL, acute myeloid leukemia (AML), and non-leukemic BM samples, predict B- and T-cell lineage in ALL, and detect the presence of the ETV6–RUNX1 translocation. The performance was evaluated using cross-validation (CV) and an external validation cohort. Results: The models achieved a statistically significant area under the curve (AUC) of 0.99 in distinguishing ALL from AML and control samples. In cross-validation (CV), the models achieved a cross-validation AUC of 0.74 for predicting B/T subtypes. For predicting ETV6–RUNX1 translocation, the models achieved an AUC of 0.80. External cohort validation confirmed significant AUCs of 0.72 for B/T subtype classification and 0.69 for ETV6–RUNX1 translocation prediction. Conclusions: Convolutional neural networks (CNNs) demonstrate potential as a diagnostic tool for pediatric ALL, enabling the identification of B/T lineage and ETV6–RUNX1 translocation from Giemsa-stained smears. These results pave the way for future utilization of CNNs as a diagnostic modality for pediatric leukemia in low-resource settings, where access to advanced diagnostic techniques is limited.