Diagnostic method and apparatus for cervical squamous intraepithelial lesions in vitro and in vivo using fluorescence spectroscopy

• US 6,258,576 B1
• Filed: 06/19/1996
• Issued: 07/10/2001
• Est. Priority Date: 06/19/1996
• Status: Expired due to Term

• Current Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Original Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Inventors: Ramanujam, Nirmala, Mahadevan-Jansen, Anita, Richards-Kortum, Rebecca, Mitchell, Michele Follen
• Primary Examiner(s): Bansal, Geetha P.
• Application Number:
  US08/666,021
• Time in Patent Office: 1,847 Days
• Field of Search: 435/172, 425/288
• US Class Current: 435/40.52
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G01N 2021/6419 Excitation at two or more wavelengths
• CPC Class Codes: G01N 2021/6484 Optical fibres
• CPC Class Codes: G01N 21/645 Specially adapted constructive features of fluorimeters
• CPC Class Codes: G01N 21/6486 Measuring fluorescence of biological material, e.g. DNA, RNA, cells G01N21/6428 takes precedence
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present invention involves the use of fluorescence spectroscopy in the diagnosis of cervical cancer and precancer. Using multiple illumination wavelengths, it is possible to (i) differentiate normal or inflamed tissue from squamous intraepithelial lesions (SILs) and (ii) to differentiate high grade SILs from non-high grade SILs. The detection may be performed in vitro or in vivo. Multivariate statistical analysis was employed to reduce the number of fluorescence excitation-emission wavelength pairs needed to re-develop algorithms that demonstrate a minimum decrease in classification accuracy. Fluorescence at excitation-emission wavelength pairs was used to redevelop and test screening and diagnostic algorithms that have a similar classification accuracy to those that employ fluorescence emission spectra at three excitation wavelengths. Both the full-parameter and reduced-parameter screening algorithms discriminate between SILs and non-SILs with a similar specificity and a substantially improved sensitivity relative to Pap smear screening and differentiate high grade SILs from non-high grade SILs.

Claims

1. A method of classifying a sample of tissue of a mammalian anatomical structure, the tissue of which may have various morphological and biochemical states, comprising:

illuminating the sample with electromagnetic radiation wavelengths of about 337 nm, about 380 nm and about 460 nm to produce fluorescence therein having spectral characteristics indicative of a tissue classification relating to different epithelial tissues ranging from normal to neoplastic and inflammation;

detecting a plurality of discrete emission wavelengths from the fluorescence; and

calculating from the emission wavelengths a probability that the sample belongs in the tissue classification.

2. The method of claim 1, wherein the illumination wavelengths are in the ranges of 317-357 nm, 360-400 nm and 440-480 nm.

3. The method of claim 1, wherein the emission wavelengths are about 410 nm, about 460 nm, about 510 nm and about 580 nm for an illumination of about 337 nm;

about 460 nm, about 510 nm, about 580 nm, about 600 nm and about 640 nm for an illumination of about 380 nm; and

about 510, about 580 nm, about 600 nm, about 620 nm, about 640 nm and about 660 nm for an illumination of about 460 nm.

4. The method of claim 1, wherein the sample is illuminated in vitro.

5. The method of claim 1, wherein the sample is illuminated in vivo.

6. The method of claim 4, further comprising obtaining the sample by biopsy.

7. The method of claim 6, wherein the obtaining step further comprises generating a monolayer cell touch preparation or a pellet.

8. The method of claim 7, wherein the obtaining step further comprises ethanol fixation of the tissue sample.

9. The method of claim 1, wherein the illuminating comprises illuminating the sample substantially normal to a surface of the sample, and wherein the detecting step comprises detecting the spectra at an angle of approximately 20°

from normal.

10. The method of claim 1, wherein the calculating step includes discriminating SIL from normal squamous epithelia.

11. The method of claim 1, wherein the calculating step includes discriminating SIL from normal columnar epithelium.

12. The method of claim 1, wherein the calculating step includes discriminating SIL from inflamed tissue.

13. The method of claim 1, wherein the calculating step includes discriminating high grade SIL tissue from low grade SIL tissue.

14. The method of claim 1, wherein the calculating step includes discriminating high grade SIL from normal tissue.

15. The method of claim 1, wherein the calculating step includes discriminating SIL from non-SIL.

16. The method of claim 1, wherein the calculating step includes discriminating high grade SIL from non-high grade SIL.

17. A method of developing a model for differentiating between tissue classifications for a tissue sample, the tissue classifications relating to different epithelial tissues ranging from normal to neoplastic and inflammation, comprising:

providing a plurality of tissue samples belonging to the tissue classifications;

illuminating the samples with electromagnetic radiation wavelengths of about 337 nm, about 380 nm and about 460 nm to produce fluorescence therein;

detecting a plurality of discrete emission wavelengths from the fluorescence;

forming from the emission wavelengths a set of principal components that provide statistically significant differences between the tissue classifications; and

incorporating the principal components into a logistic discriminant analysis to develop a relevant model for differentiating between the tissue classifications.

18. The method of claim 17, wherein the illumination wavelengths are in the ranges of 317-357 nm, 360-400 nm and 440-480 nm.

19. The method of claim 17, wherein the emission wavelengths are:

about 410 nm, about 460 nm, about 510 nm and about 580 nm for an illuminating wavelength of about 337 nm;

about 460 nm, about 510 nm, about 580 nm, about 600 nm and about 640 nm for an illuminating wavelength of about 380 nm; and

about 510, about 580 nm, about 600 nm, about 620 nm, about 640 nm and about 660 nm for an illuminating wavelength of about 460 nm.

20. A method of classifying a sample of tissue of a mammalian anatomical structure, the tissue of which may have various morphological and biochemical states, comprising:

illuminating the sample with electromagnetic radiation wavelengths of about 337 nm, about 380 nm and about 460 nm to produce fluorescence having spectral characteristics indicative of a tissue classification relating to different epithelial tissues ranging from normal to neoplastic and inflammation;

detecting a plurality of emission wavelengths from the fluorescence;

obtaining principal components PC1, PC3 and PC7 from the emission wavelengths; and

establishing from the principal components PC1, PC3 and PC7 a probability that the sample belongs in the tissue classification.

21. A method of classifying a sample of tissue of a mammalian anatomical structure, the tissue of which may have various morphological and biochemical states, comprising:

illuminating the sample with electromagnetic radiation wavelengths of about 337 nm, about 380 nm and about 460 nm to produce fluorescence having spectral characteristics indicative of a tissue classification relating to different epithelial tissues ranging from normal to neoplastic and inflammation;

detecting a plurality of emission wavelengths from the fluorescence;

obtaining principal components PC1, PC2, PC4, and PC5 from the emission wavelengths; and

establishing from the principal components PC1, PC2, PC4 and PC5 a probability that the sample belongs in the tissue classification.

22. A method of classifying a sample of tissue of a mammalian anatomical structure, the tissue of which may have various morphological and biochemical states, comprising:

illuminating the sample with electromagnetic radiation wavelengths of about 337 nm, about 380 nm and about 460 nm to produce fluorescence having spectral characteristics indicative of a tissue classification relating to different epithelial tissues ranging from normal to neoplastic and inflammation;

detecting a plurality of emission wavelengths from the fluorescence;

obtaining principal components PC1, PC3, PC6, and PC8 from the emission wavelengths; and

establishing from the principal components PC1, PC3 PC6 and PC8 a probability that the sample belongs in the tissue classification.

23. A method of classifying a tissue of a patient in a particular one of a plurality of tissue classifications relating to different epithelial tissues ranging from normal to neoplastic and inflammation, comprising:

identifying the patient with a predetermined population having a prior probability of tissue belonging to the particular tissue classification;

applying a plurality of excitation wavelengths to a plurality of locations on the tissue of the patient;

obtaining from the applications of the excitation wavelengths in the applying step respective sets of fluorescence spectral data, each comprising a plurality of discrete emission wavelengths;

preprocessing the sets of fluorescence spectral data;

concatenating the preprocessed fluorescence spectral data into respective vectors for the tissue locations;

processing the vectors with a matrix of reduced eigenvectors that display statistically significant differences for the tissue classifications in the population; and

calculating a posterior probability for each of the locations on the tissue of the patient that the tissue belongs to the particular tissue classification from the processed vectors, from the prior probabilities, and from distribution functions of principal component scores for the tissue classifications in the population.

24. A method as in claim 23, wherein the emission wavelengths are:

about 410 nm, about 460 nm, about 510 nm and about 580 nm for an excitation wavelength of about 337 nm;

about 460 nm, about 510 nm, about 580 nm, about 600 nm and about 640 nm for an excitation wavelength of about 380 nm; and

about 510, about 580 nm, about 600 mn, about 620 nm, about 640 nm and about 660 nm for an excitation wavelength of about 460 nm.

25. A method as in claim 23 further comprising displaying the tissue of the patient graphically, wherein locations on the tissue of the patient having a posterior probability greater than a predetermined threshold are displayed in a different color than locations on the tissue of the patient having a posterior probability less than the predetermined threshold.

26. A method of developing an index for calculating a probability that a tissue of a living organism belongs to one of a plurality of tissue classifications relating to different epithelial tissues ranging from normal to neoplastic and inflammation, comprising:

applying a plurality of excitation wavelengths to a plurality of tissue sites in a sample population, each of the histo-pathologic tissue classifications having a prior probability of occurring in the sample population;

obtaining from the applications of the excitation wavelengths in the applying step respective sets of spectral data of fluorescence intensities at discrete emission wavelengths;

forming a first dimensionally reduced set of vectors from the sets of spectral data that shows statistically significant differences between a first one and a second one of the histo-pathologic tissue classifications and accounts for a significant amount of variation in collectively the sets of spectral data;

calculating first probability distribution functions for the tissue classifications from the first dimensionally reduced set of vectors;

forming a second dimensionally reduced set of vectors from the sets of spectral data that shows statistically significant differences between a third one and a fourth one of the histo-pathologic tissue classifications and accounts for a significant amount of variation in collectively the sets of spectral data;

calculating second probability distribution functions for the tissue classifications from the second dimensionally reduced set of vectors; and

finishing the first and second probability distribution functions and the first and second dimensionally reduced set of vectors as the index.

27. A method as in claim 26, wherein the emission wavelengths are:

about 410 nm, about 460 nm, about 510 nm and about 580 nm for an excitation wavelength of about 337 nm;

about 460 nm, about 510 nm, about 580 mn, about 600 nm and about 640 nm for an excitation wavelength of about 380 nm; and

about 510, about 580 nm, about 600 nm, about 620 nm, about 640 nm and about 660 nm for an excitation wavelength of about 460 nm.

28. The method of claim 26 further comprising the step of preprocessing the sets of spectral data prior to the step of forming a first dimensionally reduced set of vectors and prior to the step of forming a second dimensionally reduced set of vectors to reduce variations in spectral data from each organism and from different organisms of the population, wherein:

the tissue classifications comprise two histo-pathologic tissue classifications;

the plurality of excitation wavelengths comprises about 337 nm, about 380 nm, and about 460 nm;

the step of forming a first dimensionally reduced set of vectors comprises principal component analysis; and

the step of forming a second dimensionally reduced set of vectors comprises principal component analysis.

29. A method of identifying a probability of a particular tissue classification for tissue of a patient having a plurality of possible tissue classifications relating to different epithelial tissues ranging from normal to neoplastic and inflammation, comprising:

identifying the patient with a predetermined population having prior probabilities of the possible tissue classifications therein;

applying electromagnetic radiation at a plurality of wavelengths to a plurality of tissue sites of subjects in the population and to the tissue of the patient;

obtaining respective sets of subject fluorescence spectral data from the electromagnetic radiation applying step;

preprocessing the sets of subject fluorescence spectral data to reduce inter-patient and intra-patient variation therein;

forming a dimensionally reduced set of orthogonal linear combinations of emission variables, including a reduced eigenvector matrix, that shows statistically significant differences between the possible tissue classifications and that significantly accounts for variation in the preprocessed sets of subject fluorescence spectral data;

calculating subject scores of the dimensionally reduced set of orthogonal linear combinations from the preprocessed sets of subject fluorescence spectral data for the possible tissue classifications;

obtaining respective sets of patient fluorescence spectral data from the electromagnetic radiation applying step;

preprocessing the sets of patient fluorescence spectral data to reduce intra-patient variation therein;

concatenating the preprocessed patient fluorescence spectral data into vectors;

processing the vectors with the reduced eigenvector matrix to obtain patient scores; and

calculating a posterior probability of the particular tissue classification from the subject scores, from the patient scores, and from the prior probability.

30. The method of claim 29, wherein the step of forming a dimensionally reduced set of orthogonal linear combinations of emission variables comprises:

forming principal components and principle component scores from the preprocessed sets of subject fluorescence spectral data;

retaining eigenvalues from the principal components forming step that account for a significant amount of the variation in the preprocessed sets of subject fluorescence spectral data;

calculating the diagnostic contribution of each of the principle components for the retained eigenvalues; and

retaining the eigenvalues corresponding to the principle components identified in the diagnostic contribution calculating step as having a significant diagnostic contribution.

31. The method of claim 30, wherein the posterior probability calculating step comprises calculating posterior probability using logistic discrimination.

32. The method of claim 31, wherein the diagnostic contribution calculating step comprises calculating the diagnostic contribution using a Student'"'"'s T-Test.

33. The method of claim 30, wherein:

the subject fluorescence spectral data obtaining step comprises obtaining respective sets of subject fluorescence spectral data from the electromagnetic radiation applying step at respective first, second and third sets of discrete wavelengths at which component loadings for the principle components identified in the diagnostic contribution calculating step as having a significant diagnostic contribution are significant; and

the patient fluorescence spectral data obtaining step comprises obtaining respective sets of patient fluorescence spectral data from the electromagnetic radiation applying step at the first, second, and third sets of discrete wavelengths.

34. The method of claim 33, wherein:

the first wavelength is about 337 nm and the first set of discrete wavelengths is about 410 about 430 nm, about 510 nm, and about 580 nm;

the second wavelength is about 380 nm, and the second set of discrete wavelengths is about 410 nm, about 430 nm, about 510 nm, about 580 nm, and about 640 nm; and

the third wavelength is about 460 nm, and the third set of discrete wavelengths is about 580 nm, about 600 nm, about 620 nm, and about 640 nm.

35. A method as in claim 29 further comprising displaying the tissue of the patient graphically, wherein locations on the tissue of the patient having a posterior probability greater than a predetermined threshold are displayed in a different color than locations on the tissue of the patient having a posterior probability less than the predetermined threshold.