Tissue Fluorescence
Spectroscopy
Lecture 16
Outline
• Steady-state fluorescence
– Instrumentation and Data Analysis Methods
• Statistical methods: Principal components analysis
• Empirical methods: Ratio imaging
• Modeling: Quantitative extraction of biochemical info
– Fluorescence in disease diagnostics
– Fluorescence in disease therapeutics
Fluorescence spectra provide a
rich source of information on
tissue state
1.5
450
FAD
Excitation (nm)
1
Protein expression
400
NADH
350
Collagen
300
Trp
350
400
450
0.5
Structural integrity
0
Metabolic activity
-0.5
500
550
600
-1
Emission (nm)
Courtesy of Nimmi Ramanujam, University of Wisconsin, Madison
Development of cancer involves a series of changes
some of which can be probed by fluorescence
•organization
•protein expression (Trp)
•metabolic activity (NADH/FAD) •structural integrity (collagen)
•angiogenesis
•nuclear morphology
Instrumentation for clinical tissue fluorescence
measurements can be very simple, compact and
relatively cheap
Control
CCD
Light
Source
Imaging
Spectrograph
Optical fiber probe
Courtesy of Urs Utzinger, University of Arizona
Consistent autofluorescence differences
have been detected between normal, precancerous and cancerous spectra
Non-dysplastic Barrett’s
esophagus
Low-grade dysplasia
High-grade dysplasia
1.0
0.8
0.6
0.4
0.2
0.0
300
400
500
600
Wavelength (nm)
700
Promising studies in
•GI tract
•Cervix
•Lung
•Oral cavity
•Breast
•Artery
•Bladder
Methods of data analysis
• Main goal for fluorescence diagnostics:
Identify fluorescence features that can
be used to identify/classify tissue as
normal or diseased.
• Main approaches
– Statistical
– Empirical
– Model Based
Data analysis: Empirical and
statistical algorithms
Data preprocessing
Normalization
Data reduction
and
Feature extraction
Principal Component
Analysis
Ratio methods
Classification
Detection of cervical precancerous lesions using
fluorescence spectroscopy:
Principal components analysis
Rebecca Richards Kortum group
UT Austin
Detection of cervical pre-cancerous
lesions
ectocervix
ectocervix
Colposcopic view of
uterine cervix
endocervix
Transformation
zone
endocervix
•
•
•
•
•
•
•
During the natural lifetime of a woman, squamous epithelium which lines the ectocervix
gradually replaces the columnar epithelium of the endocervix, within an area known as the
transformation zone. The replacement of columnar epithelium by squamous epithelium is
known as squamous metaplasia.
Most pre-cancerous lesions of the cervix develop within the transformation zone.
The Papanicolaou (Pap) smear is the standard screening test for cervical abnormalities
If a Pap smear yields atypical results, the patient undergoes a colposcopy, i.e. magnified
(typically 6X to 15X) visualization of the cervix.
3-6% acetic acid is applied to the cervix and abnormal areas are biopsied and evaluated
histo
4-6 billion dollars are spent annually in the US alone for colposcopic evaluation and treatment
Major disadvantage colposcopic evaluation is its wide range of sensitivity (87-99%) and
specificity (23-87%), even in expert hands.
Major tissue histopathological
classifications
•
•
•
•
•
Normal squamous epithelium
Squamous metaplasia
Low-grade squamous intraepithelial lesion
High-grade squamous intraepithelial lesion
Carcinoma
Instrumentation
30 Hz rep rate
5 ns pulse duration
Nitrogen
Pumped Dye
Laser
Nitrogen
Pumped Dye
Laser
Intensified
Polychromator
Diode Array
excitation fibers
337 nm
380 nm
460 nm
collection fibers
Spectral Resolution: 10 nm
probe
Gate Pulser
Controller
Computer
excitation fibers
collection fibers
quartz
shield
NS
0.4
0.3
LG
0.2
HG
NC
0.1
0
350
400
450
500
550
600
650
0.3
NS
0.2
NC
HG
0.1
LG
0
400
450
Wavelength (nm)
337 nm Excitation
Fluorescence Intensity
Fluorescence Intensity
Fluorescence Intensity
0.5
500
550
600
650
0.3
NS
0.2
0.1
NC
0
460
510
Wavelength (nm)
560
610
Wavelength (nm)
380 nm Excitation
PRE-PROCESSING
Normalized Spectra at
Three Excitation Wavelengths
LG
460 nm Excitation
Normalized, Mean-scaled Spectra
at Three Excitation Wavelengths
DIMENSION REDUCTION: PRINCIPAL COMPONENT ANALYSIS
SELECTION OF DIAGNOSTIC PRINCIPAL COMPONENTS: T-TEST
CLASSIFICATION: LOGISTIC DISCRIMINATION
Constituent Algorithm 1
Posterior Probability
of being NS or SIL
Constituent Algorithm 3
Posterior Probability
of being LG or HG
Constituent Algorithm 2
Posterior Probability
of being NC or SIL
DEVELOPMENT OF COMPOSITE ALGORITHMS
Composite Screening Algorithm
(1,2)
Posterior Probability of being
SIL or NON SIL
(1,2,3) Composite Diagnostic Algorithm
Posterior Probability of being
HG SIL or NON HG SIL
Courtesy of N. Ramanujam; Photochem. Photobiol. 64: 720-735, 1996
660
Fluorescence Intensity
(c.u.)
Data
0.5
0.4
0.3
0.2
0.1
0
350
450
550
650
PreProcessing
Step 1
Normalized
Fluorescence Intensity
(c.u.)
Wavelength (nm)
1.2
1
0.8
0.6
0.4
0.2
0
350
450
550
650
PreProcessing
Step 2
Normalized,
mean-scaled
Fluorescence
Wavelength (nm)
0.4
0.2
0
-0.2350
-0.4
450
550
Wavelength (nm)
650
Normal squamous
Low-grade
High-grade
Normal columnar
Principal Component Analysis
Spectrum= wi*Bi
Normalized
Fluorescence Intensity
(c.u.)
w=component weight
B=component loading describing data variance
1.2
1
0.8
0.6
0.4
0.2
0
350
Component loadings
spectra
450
550
Wavelength (nm)
650
Normalized
Fluorescence Intensity
(c.u.)
Dimension reduction: Principal
Component Analysis
1.2
1
0.8
0.6
0.4
0.2
0
350
spectra
337 nm
450
550
650
Normalized Fluorescence
Intensity (c.u.)
Wavelength (nm)
1.2
1
380 nm
0.8
0.6
0.4
0.2
0
400
500
600
Normalized Fluorescence
Intensity (c.u.)
Wavelength (nm)
1
460 nm
0.8
0.6
0.4
0.2
0
460
560
Wavelength (nm)
660
Component loadings
PCA Step 2: Calculate probability of
belonging to category based on
component weights and classify
Posterior Probability of SIL
1
▲Low-grade SIL
0.75
●High-grade SIL
Low Grade SIL
0.5
High Grade SIL
Normal Squamous
□Normal squamous
0.25
0
0
50
100
150
200
Sample Number
□ Non-dysplastic Barrett’s
esophagus
X Dysplatic Barrett’s
esophagus
Posterior Probability of SIL
1
▲Low-grade SIL
0.75
Grade SIL
●Low
High-grade SIL
High Grade SIL
0.5
□Normal columnar
Normal Columnar
0.25
0
80
100
120
140
Sample Number
160
180
200
Fluorescence spectroscopy is a promising
tool for the detection of cervical precancerous lesions
Classification
Pap Smear Screening
SILs vs. NON SILs
Sensitivity
Specificity
62% ±23
68%±21
HG SIL vs. Non HG SIL
Sensitivity
Specificity
N/A
N/A
Colposcopy in Expert Hands
94%±6
48%±23
79%±23
76%±13
Full-Parameter
Composite Algorithm
Reduced-Parameter
Composite Algorithm
82%±1.4
68%±0.0
79%±2
78%±6
84%±1.5
65%±2
78%±0.7
74%±2
Spectroscopic analysis using
PCA
• Uses full spectrum information to
optimize sensitivity and specificity
• Relatively easy to implement
(automated software)
• Provides no intuition with regards to the
origin of spectral differences
Spectroscopic imaging:
fluorescence ratio methods for
detection of lung neoplasia
B. Palcic et al, Chest 99:742-3, 1991
LIFE schematic
B. Palcic et al, Chest 99:742-3, 1991
Detection of lung carcinoma in
situ using the LIFE imaging
system
Carcinoma in situ
White light bronchoscopy Autofluorescence ratio
image
Courtesy of Xillix Technologies (www.xillix.com)
Autofluorescence enhances ability
to localize small neoplastic lesions
Severe dysplasia/Worse
WLB
WLB+LIFE
Intraepithelial Neoplasia
WLB
WLB+LIFE
Sensitivity
0.25
0.67
0.09
0.56
Positive predictive value
0.39
0.33
0.14
0.23
Negative predictive value
0.83
0.89
0.84
0.89
False positive rate
0.10
0.34
0.10
0.34
Relative sensitivity
S Lam et al. Chest 113: 696-702, 1998
2.71
6.3
Test Definitions
Has disease
Does not have
disease
Tests positive
(A)
True positive
(B)
False positive
(A+B)
Total # who test
positive
Tests negative
(C)
False negative
(D)
True negative
(C+D)
Total # who test
negative
(A+C)
Total # who have
disease
(B+D)
Total # who do not
have disease
Sensitivity=A/(A+C)
Specificity=D/(B+D)
Positive predictive value=A/(A+B)
Negative predictive value=D/(C+D)
Statistical definitions
•
Positive predictive value: probability that patient has the disease when restricted
to those patients who test positive
TP
PPV 
•
TP  FP
Negative predictive value: probability that patient doesn’t have the disease when
restricted to those patients who test negative
TN
NPV 
•
sensitivit y 
•
TN  FN
Sensitivity: probability that the test is positive given to a group of patients with the
disease
TP
TP  FN
Specificity: probability that the test is negative given to a group of patients
without the disease
TN
specificit y 
TN  FP
Fluorescence imaging based
on ratio methods
• Wide field of view (probably a huge
advantage for most clinical settings)
• Eliminates effects of distance and angle
of illumination
• Easy to implement
• Provides no intuition with regards to
origins of spectral differences
What are the origins of the
observed differences?
0.12
0.12
Collagen
0.10
0.08
0.08
0.06
0.06
0.04
0.04
0.02
0.02
0.00
0.00
350
400
450
500
550
600
NADH
0.10
650
wavelength (nm)
337 nm excitation
358 nm excitation
381 nm excitation
700
750
350
400
450
500
550
600
wavelength (nm)
397 nm excitation
412 nm excitation
425 nm excitation
650
700
750
Collagen and NADH spectra are
sufficiently distinct only for some excitation
wavelengths
337 nm excitation
358 nm excitation
Tissue absorption and scattering
may affect significantly tissue
fluorescence
• scattering
– elastic scattering
• multiple scattering
• single scattering
Epithelium
epithelium
• absorption
– Hemoglobin, beta carotene
Connective
tissue
• fluorescence
Connective Tissue
Is hemoglobin absorption a problem?
1.0
fluorescence
0.9
337 nm excitation
0.8
0.7
0.6
0.5
0.4
To get answer use
0.3
0.2
0.1
0.0
300
400
500
600
700
800
wavelength (nm)
0 .4 0
Monte Carlo
simulations
reflectance
0 .3 5
Analytical Modeling
0 .3 0
0 .2 5
0 .2 0
0 .1 5
0 .1 0
300
400
500
600
wavelength (nm)
700
800