Observational studyJCO clinical cancer informatics2025
Machine Learning Model Predicts Abnormal Lymphocytosis Associated With Chronic Lymphocytic Leukemia.
Observational study in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Machine Learning Model Predicts Monoclonal Gammopathy Using Routine Laboratory Values.JCO clinical cancer informatics · 2026Article
- Enhancing lymphoma cancer detection using deep transfer learning on histopathological images.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe diagnosis of chronic lymphocytic leukemia (CLL) is often delayed several years in advance of disease. Addressing this care gap would aid in identifying at-risk patients who may benefit from targeted evaluation to prevent adverse outcomes. To our knowledge, to date, however, there are no widely utilized machine learning (ML) models that predict development of CLL. Therefore, the objective of this study was to leverage readily available laboratory data to train and test the performance of ML-based risk models for abnormal lymphocytosis associated with CLL.
methodsThe observational study population was composed of deidentified laboratory data procured from a large US outpatient network. The 7-year longitudinal data set included 1,090,707 adult patients with the following inclusion criteria: age 50 to 75 years and initial absolute lymphocyte count (ALC) <5 × 10
resultsThe 12-variable risk classifier model accurately predicted ALC ≥5 × 10
conclusionOur ML risk classifier accurately predicts abnormal lymphocytosis associated with CLL using routine laboratory data. Although prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition for patients at a risk of CLL.
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