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.

Abstract

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.