Distinguishing Reactive Lymphocytes From Blasts Using Fractal Chromatin Patterns.
Introduction: Of all the cells identified in peripheral blood smears, reactive lymphocytes (RLs) and blasts are considered especially difficult to differentiate. Blasts and RLs are present in distinct diseases that carry unique prognoses and treatments; however, there are currently no definitive met...
| Publicado en: | International Journal of Laboratory Hematology Vol. 47; no. 6; pp. 1064 - 1074 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189332389&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189332389 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17515521 47EA jtl: International Journal of Laboratory Hematology issn: 17515521 maglogo: Y pubinfo: dt: Dec2025 vid: 47 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 189332389 187307825 189332389 189332389 10.1111/ijlh.14541 189332389 ppf: 1064 ppct: 10 formats: tig: atl: Distinguishing Reactive Lymphocytes From Blasts Using Fractal Chromatin Patterns. aug: au: Gordhamer, Abigail Tullis, Henry Cordner, Ryan affil: Department of Microbiology and Molecular Biology, Brigham Young University, Provo Utah, , USA sug: subj: Leukocytes Pathology Leukocytes Metabolism Chemistry, Analytical Random Forest Algorithms Cell Nucleus Human Machine Learning Comparative Studies Leukemia, Myeloid, Acute Leukemia, Lymphocytic, Acute Infection Infectious Mononucleosis Microscopy Descriptive Statistics Data Analysis Software T-Tests Sensitivity and Specificity Predictive Value of Tests ROC Curve Factor Analysis Artificial Intelligence Hematologic Tests Autoanalyzers Image Processing, Computer Assisted ab: Introduction: Of all the cells identified in peripheral blood smears, reactive lymphocytes (RLs) and blasts are considered especially difficult to differentiate. Blasts and RLs are present in distinct diseases that carry unique prognoses and treatments; however, there are currently no definitive methods to distinguish these cells morphologically. Methods: We developed a method to distinguish between blasts and RLs based on the quantification of fractal chromatin patterns. Nuclei from white blood cell images were isolated, and the fractal patterns were quantified using The Workflow of Matrix Biology Informatics (TWOMBLI) software. Quantified fractals were compared using the t‐test. The data was further split into training and testing sets. Models (random forest and k‐nearest neighbors) were selected through cross‐validation on the training sets. Performance metrics, including area under the curve (AUC), accuracy, precision, specificity, and sensitivity, were determined for the selected models on the testing sets. Principal component analysis (PCA) was also performed. Results: Our most general model was able to identify RLs and blast subtypes with an average 84.2% accuracy and an AUC of 0.844. Testing on the holdout set gave every model an area under the curve greater than 0.815. PCA revealed two components that account for 50% of the data's variance. Conclusion: Our results suggest that a classification algorithm can effectively distinguish between blasts and RLs based solely on fractal chromatin patterns. It is possible that a similar algorithm could be utilized in the clinical hematology laboratory to assist in distinguishing RLs and blasts in peripheral blood smears. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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