AIMC Topic: Leukemia, Myeloid, Acute
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Blood-based proteomic profiling identifies OSMR as a novel biomarker of AML outcomes.
Blood
Jun 19, 2025
Inflammation is increasingly recognized as a critical factor in acute myeloid leukemia (AML) pathogenesis. We performed blood-based proteomic profiling of 251 inflammatory proteins in 543 patients with newly diagnosed AML. Using a machine learning mo...
Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights.
PloS one
Jan 1, 2025
BACKGROUND: Acute Myeloid Leukemia (AML) exhibits significant heterogeneity in clinical outcomes, yet current prognostic stratification systems based on genetic alterations alone cannot fully capture this complexity. This study aimed to develop an in...
High-dimensional Immune Profiles and Machine Learning May Predict Acute Myeloid Leukemia Relapse Early following Transplant.
Journal of immunology (Baltimore, Md. : 1950)
Nov 15, 2024
Identification of early immune signatures associated with acute myeloid leukemia (AML) relapse following hematopoietic stem cell transplant (HSCT) is critical for patient outcomes. We analyzed PBMCs from 58 patients with AML undergoing HSCT, focusing...
Application of m6A regulators to predict transformation from myelodysplastic syndrome to acute myeloid leukemia via machine learning.
Medicine
Jul 12, 2024
Myelodysplastic syndrome (MDS) frequently transforms into acute myeloid leukemia (AML). Predicting the risk of its transformation will help to make the treatment plan. Levels of expression of N6-methyladenosine (m6A) regulators is difference in patie...
Evaluation of a machine-learning model based on laboratory parameters for the prediction of acute leukaemia subtypes: a multicentre model development and validation study in France.
The Lancet. Digital health
May 1, 2024
BACKGROUND: Acute leukaemias are life-threatening haematological cancers characterised by the infiltration of transformed immature haematopoietic cells in the blood and bone marrow. Prompt and accurate diagnosis of the three main acute leukaemia subt...
Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from Pappenheim-stained bone marrow smears.
Blood advances
Jan 9, 2024
The detection of genetic aberrations is crucial for early therapy decisions in acute myeloid leukemia (AML) and recommended for all patients. Because genetic testing is expensive and time consuming, a need remains for cost-effective, fast, and broadl...
Clinical insights into hematologic malignancies and comparative analysis of molecular signatures of acute myeloid leukemia in different ethnicities using an artificial intelligence offering.
Medicine
Dec 23, 2021
Next generation sequencing generates copious amounts of genomics data, causing manual interpretation to be laborious and non-scalable while remaining subjective (even for highly trained specialists). We evaluated the performance of the artificial int...
Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia.
Blood
Nov 11, 2021
Although genomic alterations drive the pathogenesis of acute myeloid leukemia (AML), traditional classifications are largely based on morphology, and prototypic genetic founder lesions define only a small proportion of AML patients. The historical su...
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