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Abstract Chronic myeloid leukemia (CML) typically occurs in late adulthood. Pediatric CML is a rare form of leukemia. In all age groups, the characteristic genetic driver of the disease is the BCR::ABL1 fusion gene. However, additional genomic events contribute to leukemic transformation, which is not yet well-characterized in pediatric CML. We investigated the mutational landscape of pediatric CML to determine whether predisposing germline variants may play a role in early-age disease development. Whole exome sequencing and targeted sequencing were performed in pediatric and adult CML samples to identify age-related germline and somatic variants in addition to the BCR::ABL1 translocation. Germline variants were detected in about 60% of pediatric patients with CML, with predominantly hematopoietic genes affected, most frequently ASXL1, NOTCH1, KDM6B, and TET2. The number of germline variants was significantly lower in adult patients with CML. If only confirmed pathogenic variants were regarded as cancer-predisposing variants, the occurrence was ~ 10% of pediatric CML, which is comparable to other hematological malignancies and most childhood cancer entities in general. We hypothesize that the interaction with the strong oncogene BCR::ABL1 may also favor the development of leukemia by weaker variants in the same genes. In pediatric patients, the germline variants of genes associated with clonal hematopoiesis may increase the likelihood that an incidental BCR::ABL1 translocation triggers the early manifestation of CML.

Abstract Given the high prevalence of artificial intelligence (AI) research in medicine, the development of deep learning (DL) algorithms based on image recognition, such as the analysis of bone marrow aspirate (BMA) smears, is rapidly increasing in the field of hematology and oncology. The models are trained to identify the optimal regions of the BMA smear for differential cell count and subsequently detect and classify a number of cell types, which can ultimately be utilized for diagnostic purposes. Moreover, AI is capable of identifying genetic mutations phenotypically. This pipeline has the potential to offer an accurate and rapid preliminary analysis of the bone marrow in the clinical routine. However, the intrinsic complexity of hematological diseases presents several challenges for the automatic morphological assessment. To ensure general applicability across multiple medical centers and to deliver high accuracy on prospective clinical data, AI models would require highly heterogeneous training datasets. This review presents a systematic analysis of models for cell classification and detection of hematological malignancies published in the last 5 years (2019–2024). It provides insight into the challenges and opportunities of these DL‐assisted tasks.