Automated Analysis of Flow Cytometry Data for B-Cell Lymphoma

Flow cytometry, a powerful tool for the diagnosis of hematolymphoid malignancies including B-cell lymphomas, is an innovative technique that measures the fluorescence of suspended cells. Traditionally, the method of evaluating the research and clinical study of flow cytometry data is done by the process of manual gating along with a review by pathologists using their accumulated knowledge. The problem with manual processing is that it is labor-intensive, time-consuming, and subject to human error. Although several computerized methods are available for flow cytometry data processing, most of the current automatic techniques have not been fully developed. In this dissertation, based on the discoveries found in my research, a computational model is proposed to detect B-lymphocyte neoplasms using flow cytometry data by building healthy and sick profiles. The technique is based on using a cell-capture rate that is defined to measure the fitness of a test subject using a particular profile. By examining the cell-capture rate of a test case with all profiles, the disease type can be determined. To strengthen the system, a confidence level of diagnosis is defined to assist the physician in making a better decision. This technique is validated by comparing the diagnosis result, given by the proposed algorithm, with the hospital’s information. In addition, this method is also tested by analyzing test cases of minimal residual disease, obtained from a group of patients with fewer B-cell lymphoma cells. When patients exhibits this condition, the difficulty of automated diagnosis is greatly increased. Finally, the validity of the automated system is supported by the strong correlation between the results from the automated system diagnosis and the conventional manual process.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Houston

Year dc:date.issued

2016

Author dc:creator

• Shih, Ming-Chih 1977-

Advisor dc:contributor.advisor

• Huang, Stephen

Committee members dc:contributor.committeemember

• Leiss, Ernst L.
• Chen, Guoning
• Zu, Youli

• Flow Cytometry
• Minimal Residual Disease
• Computational Model
• B-cell lymphoma

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10657/3253

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/3253