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 NADH FAD Collagen Trp Protein expression Structural integrity Metabolic activity Courtesy of Nimmi Ramanujam, University of Wisconsin, Madison

Development of cancer involves a series of changes some of which can be probed by fluorescence protein expression (Trp) metabolic activity (NADH/FAD) nuclear morphology organization structural integrity (collagen) angiogenesis

Instrumentation for clinical tissue fluorescence measurements can be very simple, compact and relatively cheap Courtesy of Urs Utzinger, University of Arizona

Consistent autofluorescence differences have been detected between normal, pre- cancerous and cancerous spectra Non-dysplastic Barrett’s esophagus Low-grade dysplasia High-grade dysplasia Wavelength (nm) Normalized fluorescence intensity 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 pre- processing Normalization Data reduction and Feature extraction Principal Component Analysis Ratio methods Classification

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

Detection of cervical pre-cancerous lesions 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. endocervix ectocervix Transformation zone Colposcopic view of uterine cervix ectocervix endocervix

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

Instrumentation

PRE-PROCESSING Normalized Spectra at Three Excitation Wavelengths Normalized, Mean-scaled Spectra at Three Excitation Wavelengths DIMENSION REDUCTION: PRINCIPAL COMPONENT ANALYSIS CLASSIFICATION: LOGISTIC DISCRIMINATION Posterior Probability of being NS or SIL Posterior Probability of being LG or HG Posterior Probability of being NC or SIL Posterior Probability of being SIL or NON SIL Posterior Probability of being HG SIL or NON HG SIL DEVELOPMENT OF COMPOSITE ALGORITHMS Constituent Algorithm 1 Constituent Algorithm 3Constituent Algorithm 2 (1,2) (1,2,3) SELECTION OF DIAGNOSTIC PRINCIPAL COMPONENTS: T-TEST Composite Screening Algorithm Composite Diagnostic Algorithm 337 nm Excitation 380 nm Excitation 460 nm Excitation Courtesy of N. Ramanujam; Photochem. Photobiol. 64: , 1996

Data Pre- Processing Step 1 Pre- Processing Step 2 Normal squamous Low-grade High-grade Normal columnar

Principal Component Analysis Spectrum=  w i *B i w=component weight B=component loading describing data variance spectra Component loadings

Dimension reduction: Principal Component Analysis Component loadings spectra 337 nm 380 nm 460 nm

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

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 Courtesy of Xillix Technologies ( White light bronchoscopy Autofluorescence ratio image Carcinoma in situ

Autofluorescence enhances ability to localize small neoplastic lesions S Lam et al. Chest 113: , 1998

Test Definitions Has diseaseDoes 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 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 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? wavelength (nm) Intrinsic fluorescence 337 nm excitation 358 nm excitation 381 nm excitation 397 nm excitation 412 nm excitation 425 nm excitation Collagen NADH

Collagen and NADH spectra are sufficiently distinct only for some excitation wavelengths 337 nm excitation358 nm excitation

Tissue absorption and scattering may affect significantly tissue fluorescence scattering –elastic scattering multiple scattering absorption –Hemoglobin, beta carotene fluorescence single scattering epithelium Connective tissue

Is hemoglobin absorption a problem? fluorescence wavelength (nm) reflectance 337 nm excitation wavelength (nm) To get answer use Monte Carlo simulations Analytical Modeling