{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T10:21:32Z","timestamp":1784629292386,"version":"3.55.0"},"reference-count":40,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Blood &amp; Cancer"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Accurate determination of B\/T\u2010cell lineage and the presence of the ETV6\u2013RUNX1 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\u2010resource settings, creating a need for alternative solutions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Procedure<\/jats:title><jats:p>We developed a deep learning pipeline to analyze Giemsa\u2010stained bone marrow (BM) aspirate smears. The models were trained to distinguish between ALL, acute myeloid leukemia (AML), and non\u2010leukemic BM samples, predict B\u2010 and T\u2010cell lineage in ALL, and detect the presence of the ETV6\u2013RUNX1 translocation. The performance was evaluated using cross\u2010validation (CV) and an external validation cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The models achieved a statistically significant area under the curve (AUC) of 0.99 in distinguishing ALL from AML and control samples. In cross\u2010validation (CV), the models achieved a cross\u2010validation AUC of 0.74 for predicting B\/T subtypes. For predicting ETV6\u2013RUNX1 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\u2013RUNX1 translocation prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Convolutional neural networks (CNNs) demonstrate potential as a diagnostic tool for pediatric ALL, enabling the identification of B\/T lineage and ETV6\u2013RUNX1 translocation from Giemsa\u2010stained smears. These results pave the way for future utilization of CNNs as a diagnostic modality for pediatric leukemia in low\u2010resource settings, where access to advanced diagnostic techniques is limited.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pbc.31797","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T23:24:25Z","timestamp":1747869865000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of B\/T Subtype and ETV6\u2013RUNX1 Translocation in Pediatric Acute Lymphoblastic Leukemia by Deep Learning Analysis of Giemsa\u2010Stained Whole Slide Images of Bone Marrow Aspirates"],"prefix":"10.1002","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6348-835X","authenticated-orcid":false,"given":"Arkadi","family":"Piven","sequence":"first","affiliation":[{"name":"Department of Computer Science Technion Israel Institute of Technology  Haifa 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