AIMC Topic: Leukemia, Lymphocytic, Chronic, B-Cell
Clear Filters Showing 21 to 25 of 25 articles
Potential for Process Improvement of Clinical Flow Cytometry by Incorporating Real-Time Automated Screening of Data to Expedite Addition of Antibody Panels.
American journal of clinical pathology
Mar 3, 2022
OBJECTIVES: We desired an automated approach to expedite ordering additional antibody panels in our clinical flow cytometry lab. This addition could improve turnaround times, decrease time spent revisiting cases, and improve consistency.
Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.
Archives of pathology & laboratory medicine
Jan 2, 2022
CONTEXT.—: Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relative...
Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.
Journal of the American Medical Informatics Association : JAMIA
Jul 1, 2020
OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological...
Analysis of Four Types of Leukemia Using Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Scores.
Combinatorial chemistry & high throughput screening
Jan 1, 2020
AIM AND OBJECTIVE: Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Leukemia can be classified into four types: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML)...
Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia.
JCO clinical cancer informatics
May 1, 2019
PURPOSE: Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can ...
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