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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

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slide2

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

1

400

0.5

Excitation (nm)

0

350

-0.5

300

-1

350

400

450

500

550

600

Emission (nm)

Fluorescence spectra provide a rich source of information on tissue state

FAD

Protein expression

Structural integrity

Metabolic activity

NADH

Collagen

Trp

Courtesy of Nimmi Ramanujam, University of Wisconsin, Madison

slide5

Development of cancer involves a series of changes some of which can be probed by fluorescence

  • organization
  • structural integrity (collagen)
  • angiogenesis
  • protein expression (Trp)
  • metabolic activity (NADH/FAD)
  • nuclear morphology
slide6

Control

CCD

Light

Source

Imaging

Spectrograph

Optical fiber probe

Instrumentation for clinical tissue fluorescence measurements can be very simple, compact and relatively cheap

Courtesy of Urs Utzinger, University of Arizona

slide7
Consistent autofluorescence differences have been detected between normal, pre-cancerous and cancerous spectra

Non-dysplastic Barrett’s

esophagus

Low-grade dysplasia

High-grade dysplasia

Normalized fluorescence intensity

  • Promising studies in
  • GI tract
  • Cervix
  • Lung
  • Oral cavity
  • Breast
  • Artery
  • Bladder

Wavelength (nm)

methods of data analysis
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 analysis: Empirical and statistical algorithms

Data pre-

processing

Data reduction

and

Feature extraction

Classification

Normalization

Principal Component

Analysis

Ratio methods

slide10

Detection of cervical pre-cancerous lesions using fluorescence spectroscopy: Principal components analysisRebecca Richards Kortum group UT Austin

slide11

Detection of cervical pre-cancerous lesions

ectocervix

ectocervix

endocervix

Colposcopic view of

uterine cervix

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
Major tissue histopathological classifications
  • Normal squamous epithelium
  • Squamous metaplasia
  • Low-grade squamous intraepithelial lesion
  • High-grade squamous intraepithelial lesion
  • Carcinoma
slide14

337 nm Excitation 380 nm Excitation 460 nm Excitation

PRE-PROCESSING

Normalized Spectra at

Three Excitation Wavelengths

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

Constituent Algorithm 3

Constituent Algorithm 2

Posterior Probability of being NS or SIL

Posterior Probability of being LG or HG

Posterior Probability of being NC or SIL

DEVELOPMENT OF COMPOSITE ALGORITHMS

Composite Screening Algorithm

Composite Diagnostic Algorithm

(1,2)

(1,2,3)

Posterior Probability of being SIL or NON SIL

Posterior Probability of being HG SIL or NON HG SIL

Courtesy of N. Ramanujam; Photochem. Photobiol. 64: 720-735, 1996

slide15

Data

Pre-

Processing

Step 1

Normal squamous

Low-grade

High-grade

Normal columnar

Pre-

Processing

Step 2

principal component analysis
Principal Component Analysis

Spectrum= wi*Bi

w=component weight

B=component loading describing data variance

Component loadings

spectra

dimension reduction principal component analysis
Dimension reduction: Principal Component Analysis

spectra

Component loadings

337 nm

380 nm

460 nm

pca step 2 calculate probability of belonging to category based on component weights and classify
PCA Step 2: Calculate probability of belonging to category based on component weights and classify

▲Low-grade SIL

●High-grade SIL

□Normal squamous

▲Low-grade SIL

●High-grade SIL

□Normal columnar

□ Non-dysplastic Barrett’s esophagus

X Dysplatic Barrett’s esophagus

fluorescence spectroscopy is a promising tool for the detection of cervical pre cancerous lesions
Fluorescence spectroscopy is a promising tool for the detection of cervical pre-cancerous lesions
spectroscopic analysis using pca
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
Spectroscopic imaging: fluorescence ratio methods for detection of lung neoplasia

B. Palcic et al, Chest 99:742-3, 1991

life schematic
LIFE schematic

B. Palcic et al, Chest 99:742-3, 1991

detection of lung carcinoma in situ using the life imaging system
Detection of lung carcinoma in situ using the LIFE imaging system

Carcinoma in situ

Autofluorescence ratio

image

White light bronchoscopy

Courtesy of Xillix Technologies (www.xillix.com)

autofluorescence enhances ability to localize small neoplastic lesions
Autofluorescence enhances ability to localize small neoplastic lesions

S Lam et al. Chest 113: 696-702, 1998

test definitions
Test Definitions

Positive predictive value=A/(A+B)

Negative predictive value=D/(C+D)

Sensitivity=A/(A+C)

Specificity=D/(B+D)

statistical definitions
Statistical definitions
  • Positive predictive value: probability that patient has the disease when restricted to those patients who test positive
  • Negative predictive value: probability that patient doesn’t have the disease when restricted to those patients who test negative
  • Sensitivity: probability that the test is positive given to a group of patients with the disease
  • Specificity: probability that the test is negative given to a group of patients without the disease
fluorescence imaging based on ratio methods
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
What are the origins of the observed differences?

Collagen

NADH

Intrinsic fluorescence

Intrinsic fluorescence

wavelength (nm)

wavelength (nm)

337 nm excitation

358 nm excitation

381 nm excitation

397 nm excitation

412 nm excitation

425 nm excitation

collagen and nadh spectra are sufficiently distinct only for some excitation wavelengths
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
Tissue absorption and scattering may affect significantly tissue fluorescence
  • scattering
    • elastic scattering
      • multiple scattering
  • single scattering

epithelium

  • absorption
    • Hemoglobin, beta carotene

Connective

tissue

  • fluorescence
slide31

reflectance

wavelength (nm)

Is hemoglobin absorption a problem?

337 nm excitation

fluorescence

To get answer use

Monte Carlo simulations

Analytical Modeling

wavelength (nm)