Download Neural Network Analysis of Flow Cytometry Immunophenotype Data

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 43, NO. 8, AUGUST 1996
Neural Network Analysis of Flow Cytometry
Immunophenotype Data
Ravi Kothari,* Member, IEEE, Hernani Cualing, and Thiagarajan Balachander
Mehrshad Mokhtaran M.D.
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.
Acute Leukemia
• Etiology:
– Radiation
– Oncogenic Viruses
– Genetic and Congenital Factors
– Chemical and Drugs
Acute Leukemia
• Incidence:
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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
Acute Leukemia
• Pathophysiology:
Acute Leukemia
• Classification
– Morphology
– Cytochemistry
– Cell-surface markers
– Cytoplasmic markers
– Cytogenetics
– Oncogene expression
Acute Leukemia
• Must important Distinction is between: AML & ALL
– Clinical behavior, prognosis, response to therapy
• AML (FAB)
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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
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
Acute Leukemia
• Cytogenetics and Molecular biology:
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):
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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)
Acute Leukemia
• Laboratory Manifestations:
– CBC
– Bone marrow aspiration and biopsy
– PT (Prothrombin Time) & PTT (Partial
Thromboplastin Time)
– LDH (Lactate dehydrogenase)
–…
Acute Leukemia
• Treatment:
– Combination Chemotherapy
– Bone Marrow Transplantation
– Stabilization:
• Hematological
• Metabolical
• Psychological
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Introduction
Data Collection
Classifier Design
Results
Discussion
Conclusion
Introduction
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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
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
First Phase
• Lineage Categories
• Categorize into:
– Reactive
– ALL
– Remission
– Mixed AML-ALL
– AML
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)
Data
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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
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.
Classifier Design
• A feed-forward neural network
• Trained using back propagation algorithm
Classifier
• How many hidden layer neurons are
needed for a particular task?
– Having a large number of redundant weights
leads to over fitting
Classifier
• Given a network with a certain number of inputs, hidden layer
neurons, and output,
needed
how many training sample are
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.
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.
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
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
Fig. 2. Performance of the network for categorization into reactive and the lineage
categories of leukemia (ALL, Remission, Mixed AML-ALL, and AML).
Fig. 3. Performance of the network for categorization of ALL cases into
subcategories based on differentiation (Pre-B, CALLA+, and T Phenotype).
Result
• Generalization Error:
– First phase: 10.3%
– Second phase: 10.0%
• Back propagation without the complexity
regulation term (Weight Decay):
– Generalization performance was poor
Discussion
• Clustering-based methods fall into one of
two categories:
– Partitioning
– Hierarchical
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.
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.
Conclusion
• Off line retraining
• Extract rules from trained networks