AIMC Topic: Precursor B-Cell Lymphoblastic Leukemia-Lymphoma

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An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia.

EBioMedicine May 28, 2024
BACKGROUND: The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using ...
Humans Artificial Intelligence Female Biomarkers, Tumor Hematologic Neoplasms Male Gene Expression Profiling Child Computational Biology Translational Research, Biomedical In Situ Hybridization, Fluorescence Precursor B-Cell Lymphoblastic Leukemia-Lymphoma
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Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

Scientific reports Apr 9, 2024
This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cyto...
Humans Artificial Intelligence Retrospective Studies Leukemia, Myeloid, Acute Deep Learning Acute Disease Flow Cytometry Immunophenotyping Precursor B-Cell Lymphoblastic Leukemia-Lymphoma
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CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre-B-cell acute lymphoblastic leukaemia.

British journal of haematology Feb 23, 2021
Disease relapse is the greatest cause of treatment failure in paediatric B-cell acute lymphoblastic leukaemia (B-ALL). Current risk stratifications fail to capture all patients at risk of relapse. Herein, we used a machine-learning approach to identi...
Humans Machine Learning Female Male Risk Assessment Infant Transcriptome Adolescent Child, Preschool Child Predictive Value of Tests Interleukin-1beta Acute Disease Survival Analysis Treatment Failure Chemokines Recurrence Precursor B-Cell Lymphoblastic Leukemia-Lymphoma MARVEL Domain-Containing Proteins
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Automated Flow Cytometric MRD Assessment in Childhood Acute B- Lymphoblastic Leukemia Using Supervised Machine Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology Jul 7, 2019
Minimal residual disease (MRD) as measured by multiparameter flow cytometry (FCM) is an independent and strong prognostic factor in B-cell acute lymphoblastic leukemia (B-ALL). However, reliable flow cytometric detection of MRD strongly depends on op...
Humans Child Flow Cytometry Immunophenotyping Bone Marrow Supervised Machine Learning Reference Standards Neoplasm, Residual Precursor B-Cell Lymphoblastic Leukemia-Lymphoma
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