An Automatic Diagnosis of B cell lymphomas by Using Flow Cytometry Data

Transcription

1 An Automatic Diagnosis of B cell lymphomas by Using Flow Cytometry Data Ming-Chih Mitch Shih 2014 Computer Science PhD Showcase Event

2 About this project We designed a diagnosis system which uses the expert (doctor) knowledge to screen patient with two types of lymphomas. This computation approach is more accurate than the current manual process with less labor and cost. The results generated by the analysis of hematopathologists at TMHS will be used to validate our results.

3 Abstract Flow cytometry (FC) is a popular ancillary study to aid in the diagnosis of B cell lymphomas. The technology produces a huge data set that requires further analysis. Current FC analysis is based mostly on manual gating, which may be tedious and timeconsuming. We proposed a computational system to detect the lymphoma cells and to make a diagnosis.

4 Agenda Introduction Traditional Approaches and Challenges Approach and Result Summary Publications

5 Introduction

6 Flow Cytometor Side Scatter CD Sensor 3 Fluidics System Sensor CD Sensor 2 cell CD Sensor 1 Forward Scatter Sensor Laser Beam

7 Obtained by Flow Cytometor From

8 Data Description Data volume typically between 10 5 and 10 6 records, Contains some noise that must be removed.

9 Traditional Approach

10 Disadvantage Labor-Intensive Time-Consuming Bias and inconsistency Processing was restricted to two dimensions at a time

11 Approach and Result

12 Clustering

13 Clustering

14

15 Profile Building Algorithm Step 1: [Initialization] Given X, use the K-mean algorithm to find k clusters of X. The output of K-mean are: M (i), V (i) and W (i), the means, co-variance, and the weight of the k clusters. Step 2: [Clustering] Use the EM (Expectation Maximization) Algorithm to compute a better clustering of X with initial values M = M (i), V = V (i), and W = W (i). Step 3: [Ellipsoid Construction] Construct k ellipsoids with Means M and Co-variance V and weight W. The ellipsoid should include all data points within m std of the center of its cluster. 15

16 Test by Different Profiles Testing Q CLL R Q Normal FL 16

17 Alignment There are many reasons that clusters of cells and a profile does not match. Machine calibration Variation of cells Medical biological reasons Shifting is try to adjust the profiles to minimize the non-medical reasons but to keep the medical differences between a test case and a profile. Not easy. This is where we incorporated medical expertise into the algorithm. 17

18 Fitting (Testing)

19 Cell Capture Rate (CCR)

20 3-D CCR Plot

21 Diagnosis Finding the distance of the CCR to the Axis Let D = {Normal, CLL, FL} be the set of all diagnoses, and CCR j be the CCR for each j in D. Compute the distance from the CCR vector of a test case to the corresponding axis as

22 Multi-Profile Testing 22

23 Small Population (Cancer cells)

24 Correlation 60% Correlation y = x R² = % 40% Automatic CCR 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Methodist Manual Gating

25 Monitoring Check the change of the cancer cell populations over time. Check the effectiveness of one treatment Check if a patient is fully recovered. Check if there is a relapse.

26 4/22/ Days 174 Days 3/18/11 9/8/11 The patient had chemotherapy in 8/2011.

27 Summary Classify different types of cells (T cells, B cells, Cancer cells) Detect different types of B cell lymphomas (CLL, FL, etc..) Our system is work on small population Our system can be used to monitor a patient

28 Name of Advisor: Dr. Stephen Huang Area of Research: Biomedical Computing First semester at the UHCS PhD Program: Fall 2005 Date of PhD proposal: (month,year) May 2008 Date of planned defense: (month,year) July 2015 PhD Dissertation Committee Members: Dr. Huang (CS, UH) Dr. Eick (CS, UH) Dr. Shah (CS, UH) Dr. Yuan (ET, UH) Dr. Zu (Hematopathology, Methodist)

29 Refereed Journal Publications M.C. Shih, S.H. Huang, Y. Zu, R. Donohue, C. Chang. Automatic B cell lymphoma detection using flow cytometry data, BMC Genomics 2013, 14 (Suppl 7):S1. Impact Factor: 4.4 Refereed Conference Publications M.C. Shih, S.H. Huang, Y. Zu, R. Donohue, C. Chang. Automatic B cell lymphoma detection using flow cytometry data, 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Page(s): 1-6. Travel Awards: $600 M.C. Shih, R. Donohue, L. Zhang, C. Chang, S.H. Huang, Y. Zu. An Automatic Diagnosis of Several Types of Lymphoma by Flow Cytometry Data, 2012 United States & Canadian Academy of Pathology Annual Meeting. Poster M.C. Shih, S.H. Huang, C. Chang. A Multidimensional Flow Cytometry Data Classification, 2009 IEEE 9th International Conference on Bioinformatics and BioEngineering (BIBE), Page(s): Poster M.C. Shih, C. Karmonik, G. Zouridakis, E. Simpson, H. Haykal, S.H. Appel. Differences Between Normal Subjects and ALS Patients Using Diffusion Tensor Imaging, 25th Annual Houston Conference on Biomedical Engineering Research. Poster G. Zouridakis, C. Karmonik, M.C. Shih, E. Simpson, H. Haykal, S.H. Appel. Brain White Mater Differences Between Normal Subjects and ALS Patients Assessed by Diffusion Tensor Imaging, ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers. Poster C. Karmonik, M.C. Shih, G. Zouridakis, E. Simpson, H. Haykal, S.H. Appel. Fractional Anisotropy of Water Diffusion in Brain White Matter in Amytrophic Lateral Sclerosis Determined with Magnetic Resonance DTI, 24th Annual Houston Conference on Biomedical Engineering Research. Poster

30 Questions?

31 Profile Building Algorithm

32 Fitting Algorithm