Automated analysis of acute myeloid leukemia minimal residual disease using a support vector machine

• Published in: Oncotarget Vol. 7; no. 44; pp. 71915 - 71921
• Main Authors: Ni, Wanmao, Hu, Beili, Zheng, Cuiping, Tong, Yin, Wang, Lei, Li, Qing-Qing, Tong, Xiangmin, Han, Yong
• Format: Journal Article
• Language: English
• Published: United States Impact Journals LLC 01-11-2016
• Subjects: Adolescent; Adult; Aged; Antigens, CD7 - analysis; Female; Flow Cytometry; Humans; Leukemia, Myeloid, Acute - diagnosis; Leukemia, Myeloid, Acute - immunology; Male; Middle Aged; Neoplasm, Residual; Research Paper; Support Vector Machine; Young Adult; flow cytometry; minimal residual disease; support vector machine; acute myeloid leukemia; immunophenotyping
• ISSN: 1949-2553; 1949-2553
• DOI: 10.18632/oncotarget.12430

We investigated the ability of support vector machines (SVM) to analyze minimal residual disease (MRD) in flow cytometry data from patients with acute myeloid leukemia (AML) automatically, objectively and standardly. The initial disease data and MRD review data in the form of 159 flow cytometry standard 3.0 files from 36 CD7-positive AML patients in whom MRD was detected more than once were exported. SVM was used for training with setting the initial disease data to 1 as the flag and setting 15 healthy persons to set 0 as the flag. Based on the two training groups, parameters were optimized, and a predictive model was built to analyze MRD data from each patient. The automated analysis results from the SVM model were compared to those obtained through conventional analysis to determine reliability. Automated analysis results based on the model did not differ from and were correlated with results obtained through conventional analysis (correlation coefficient c = 0.986, P > 0.05). Thus the SVM model could potentially be used to analyze flow cytometry-based AML MRD data automatically, objectively, and in a standardized manner.