Tissue Fluorescence Spectroscopy

1. Tissue Fluorescence Spectroscopy Lecture 16

2. 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

4. 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

5. 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

6. 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

7. 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)

8. 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

9. Data analysis: Empirical and statistical algorithms Data pre- processing Data reduction and Feature extraction Classification Normalization Principal Component Analysis Ratio methods

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

11. 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.

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

13. Instrumentation

14. 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

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

16. Principal Component Analysis Spectrum= wi*Bi w=component weight B=component loading describing data variance Component loadings spectra

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

18. 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

19. Fluorescence spectroscopy is a promising tool for the detection of cervical pre-cancerous lesions

20. 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

21. Spectroscopic imaging: fluorescence ratio methods for detection of lung neoplasia B. Palcic et al, Chest 99:742-3, 1991

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

23. 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)

24. Autofluorescence enhances ability to localize small neoplastic lesions S Lam et al. Chest 113: 696-702, 1998

25. Test Definitions Positive predictive value=A/(A+B) Negative predictive value=D/(C+D) Sensitivity=A/(A+C) Specificity=D/(B+D)

26. 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

27. 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

28. 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

29. Collagen and NADH spectra are sufficiently distinct only for some excitation wavelengths 337 nm excitation 358 nm excitation

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

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