Robust Detection of Chronic Lymphocytic Leukemia with Support Vector Machines and Flow Cytometry

Abstract Aim: Our aim is to build a precise automatic tool for the diagnosis of CLL with the help of machine learning algorithms and flow cytometry immunophenotypic data. Methods: We run experiments with two machine learning methods. First one is decision tree which was previously used in other similar works and second one is support vector machines which is considered to be a more robust classification method. Results : Among the 40 CLL patients from the test set, the model correctly predicts 38 of them and among the 20 other B-CLPD patients, the model predicts 18 of them correctly. Its sensitivity, which is the fraction of true positive predictions among all positive samples, is 95% (38/40). Conclusion : The model achieves very high accuracies on our leave out test set. This model can be a useful tool for automatic CLL diagnosis.

Keywords:

CLL, flow cytometry machine learning,

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1.Mato A, Jahnke J, Li P, et al. Real-world treatment and outcomes among older adults with chronic lymphocytic leukemia before the novel agents era. Haematologica. 2018; 103(10): 462-5. https://doi.org/10.3324/haematol.2017.185868
2.Hallek M. Chronic lymphocytic leukemia: 2020 update on diagnosis, risk stratification and treatment. Am J Hematol. 2019; 94(11): 1266-87. https://doi.org/10.1002/ajh.25595
3.Alaggio R, Amador C, Anagnostopoulos I, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms [published correction appears in Leukemia. 2023 Jul 19;:]. Leuke¬mia. 2022;36(7):1720-48. https://doi.org/10.1038/s41375-022-01620-2
4.Matutes E, Owusu-Ankomah K, Morilla R, et al. The immunological profile of B-cell disorders and proposal of a scoring system for the diagnosis of CLL. Leu¬kemia. 1994; 8(10): 1640-5.
5.Vergnolle I, Ceccomarini T, Canali A, et al. Use of a hybrid intelligence deci¬sion tree to identify mature B-cell neoplasms. Cytometry B Clin Cytom. 2023; 10.1002/cyto.b.22136. https://doi.org/10.1002/cyto.b.22136
6.Moraes LO, Pedreira CE, Barrena S, et al. A decision-tree approach for the differential diagnosis of chronic lymphoid leukemias and peripheral B-cell lymphomas. Comput Methods Programs Biomed. 2019; 178: 85-90. https://doi.org/10.1016/j.cmpb.2019.06.014
7.Frater JL, McCarron KF, Hammel JP, et al. Typical and atypical chronic lym¬phocytic leukemia differ clinically and immunophenotypically. Am J Clin Pathol. 2001; 116(5): 655-64. https://doi.org/10.1309/7Q1J-1AA8-DU4Q-PVLQ
8.Ozdemir ZN, Falay M, Parmaksiz A, et al. A novel differential diagnosis algo¬rithm for chronic lymphocytic leukemia using immunophenotyping with flow cytometry. Hematol Transfus Cell Ther. 2023; 45(2): 176-181. https://doi.org/10.1016/j.htct.2021.08.012