1. Neural Network Analysis of Flow Cytometry Immunophenotype Data Mehrshad Mokhtaran M.D. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 43, NO. 8, AUGUST 1996 Ravi Kothari,* Member, IEEE, Hernani Cualing, and Thiagarajan Balachander

4. Acute Leukemia Definition –Malignant Event –Replace the bone marrow with blast –Clinical Complication: Anemia, Infection, Bleeding –Rapidly fatal –With appropriate therapy, the natural history can be markedly altered, and many patient can be cured.

5. Acute Leukemia Etiology: –Radiation –Oncogenic Viruses –Genetic and Congenital Factors –Chemical and Drugs

6. Acute Leukemia Incidence: –Annual new case (All Leukemia): 8 to 10 per 100,000. –Remained static over the past three decades. –ALL:11% CLL:29% AML:46% CML:14% –3% of all cancer in United States –ALL is most common cancer in children(<15y) –ALL is second cause of death in children(<15y) –ALL has tow maximum incidence per age –AML gradually increases with age –Half of AML cases occur in patients younger than 50 y

7. Acute Leukemia Pathophysiology:

8. Acute Leukemia Classification –Morphology –Cytochemistry –Cell-surface markers –Cytoplasmic markers –Cytogenetics –Oncogene expression

9. Acute Leukemia Must important Distinction is between: AML & ALL –Clinical behavior, prognosis, response to therapy AML (FAB) –M0, M1, M2, M3: Increasing degree of differentiation –M4, M5: Monocytic lineage –M6: Erytroid cell linage –M7: Acute Megakaryocytic Leukemia ALL (FAB) –L1 –L2 –L3

10. Acute Leukemia Cell-surface Markers: –AML Normal immature myeloid cells and blast cells from most patient with AML: CD13, CD14, CD33, CD34 M6, M7: Antigens restricted to red cell and platelet lineage AML may express: HLA-DR antigen 10-20%: B- or T-cell lineage –ALL 60% of ALL: CALLA(CD10) (early pre-B-cell differentiation state) Pre-B-cell ALL: 20% CALLA-positive that have intracytoplasmic immunoglobulin B-cell ALL(5%): Immunoglobulin on cell surface T-cell ALL(20%): CD5, CD3 or CD2 (normal early T-cell) Null cell ALL (15%): Fail to express CALLA, B-, T-cell markers 25% of ALL: Myeloid antigens

11. Acute Leukemia Cytogenetics and Molecular biology:

13. Acute Leukemia Clinical Manifestations: –Decreaseing normal marrow function: Anemia: Fatigue, pallor, headache, angina or heart failure Thrombocytopenia: Bleeding(petechiae, ecchymosess, bleeding gums, epistaxis) Granulocytopenic(AML>ALL) : Infections (Bacterial) –Invasioning of normal organs by leukemic blasts (ALL>AML): Enlargement of lymph nodes, liver, spleen Bone pain Skin (Leukemia cutis) Leukemic meningitis: Headache, nausea CNS (particular in relapse): palsies and seizures Testicular involvement (particular in relapse) Any soft tissue (AML>ALL): Chloroma, myeloblastoma –Specific subtype of leukemia: M3: DIC (Disseminated intravascular coagulation)

14. Acute Leukemia Laboratory Manifestations: –CBC –Bone marrow aspiration and biopsy –PT (Prothrombin Time) & PTT (Partial Thromboplastin Time) –LDH (Lactate dehydrogenase) –…

15. Acute Leukemia Treatment: –Combination Chemotherapy –Bone Marrow Transplantation –Stabilization: Hematological Metabolical Psychological

16. Introduction Data Collection Classifier Design Results Discussion Conclusion

17. Introduction Immunophenotype data Flow cytometry Lineage & Differentiation ALL: Immature (CALLA+), Pre-B, Mature-B, T-Lymphoblastic Response to chemotherapy AML: M1,M2,…,M8 No relevant prognosis

18. Data Collection Flow cytometry immunophenotype data of cases with leukemia or reactive bone marrow were collected retrospectively from computerized archival database. Selection Criterion: –Confirmed diagnosis –Complete flow cytometry antibody panel result Total cases: 170 –151 leukemia and 19 nonleukemia –62 children and 89 adults –81 males and 70 females

19. First Phase Lineage Categories Categorize into: –Reactive –ALL –Remission –Mixed AML-ALL –AML

21. Second Phase Categorize the ALL Cases into subcategories based on differentiation Categorize into: –Pre-B –CALLA+ –T Phenotype Not include: Mature-B (Difficulty in obtaining sufficient data for meaningful interpretation)

23. Data Validation / Training set size = 33-50% Only Bone marrow phenotypes (Most Sensetive specific) Excluded: Peripheral blood and cerebro-spinal fluids immunophenotype Flow cytometry immunophenotype data: –Mean fluorescence intensity of a minimum of 10000 cells analyzed using either a red or green fluorescence tagged antibody

24. Data 27 Standardized and most commonly used monoclonal antibodies with defined specificities. Not all of these are utilized for each case. Average of 15 antibodies for each case. At least ten antibodies are commonly used for acute leukemia as a standard practice. With a zero value if an antibody was not used An additional binary input denoting past diagnosis of leukemia, were used as input a neural network classifier.

26. Classifier Design A feed-forward neural network Trained using back propagation algorithm

28. Classifier How many hidden layer neurons are needed for a particular task? –Having a large number of redundant weights leads to over fitting

29. Classifier Given a network with a certain number of inputs, hidden layer neurons, and output, how many training sample are needed to achieve good generalization? For accuracy of (1-ε): p ≥ O(W/ε) p: Number of training sample. W: Total number of weights in the network.

30. Classifier Perturbation: To generate a large number of cases by introducing small variation in actual cases. Optimal Brain Damage: The weight which least increase the error can be eliminated Optimal Brain Surgeon: The sensitivity of an interconnection is expressed as the cumulative sum of the changes experienced by a weight, during training. Weight Decay: Each weight has a tendency to decay to zero with a rate proportional to the magnitude of the weight.

31. Classifier Inputs: 27 + 1 Hidden: 50 Progressively increasing the number of hidden neurons until acceptable performance was achieved on training data. Output: –First phase (Based on lineage): 5 –Second phase (Based on differentiation): 3 Learning rate (η): 0.1 Weight Decay Coefficient (λ): 0.05

32. Results Mean error was acceptably low (0.0001) in both the cases. First phase weights : –Total: 1650 –Nonzero: 1106 –Very small value(<0.1): 544 Second phase weights : –Total: 1550 –Nonzero: 446 –Very small value(<0.1): 1104

33. Fig. 2. Performance of the network for categorization into reactive and the lineage categories of leukemia (ALL, Remission, Mixed AML-ALL, and AML).

34. Fig. 3. Performance of the network for categorization of ALL cases into subcategories based on differentiation (Pre-B, CALLA+, and T Phenotype).

35. Result Generalization Error: –First phase: 10.3% –Second phase: 10.0% Back propagation without the complexity regulation term (Weight Decay): –Generalization performance was poor

36. Discussion Clustering-based methods fall into one of two categories: –Partitioning –Hierarchical

37. Discussion Partitioning: –e.g., k-means, c-means fuzzy clustering –Divide the inputs, so that members of a cluster are close to each other and far away from other clusters –The shared specificity of some monoclonal antibodies make this extremely difficult.

38. Discussion Hierarchical: –e.g., centroid sorting, linkage methods –Try to merge two closest data points together at each step, and repeat the process until there is only one cluster. –Have a better chance of succeeding due to the variability in immunophenotype data –An error in merging made earlier on is propagated throughout.

39. Conclusion Off line retraining Extract rules from trained networks