Spectroscopic method and apparatus for optically detecting abnormal mammalian epithelial tissue

A method of probabilistically classifying a sample of tissue, comprising:

• illuminating the tissue sample with electromagnetic radiation having wavelengths of about 337 nm, about 380 nm, and about 460 nm to excite the tissue sample into producing first, second and third fluorescence intensity spectra, respectively;
• detecting the first, second and third fluorescence intensity spectra to obtain respective first, second and third spectral data;
• and
• calculating a probability that the tissue sample belongs to a first tissue classification from at least one of the first and third spectral data, a probability that the tissue sample belongs to the second tissue classification from the second spectral data, and a probability that the tissue sample belongs to the third tissue classification from the third spectral data.

A method of probabilistically classifying a sample of tissue, comprising:

• illuminating the tissue sample with electromagnetic radiation having wavelengths of about 380 nm and about 460 nm to excite the tissue sample into producing first, second and third fluorescence intensity spectra, respectively;
• detecting the first and second fluorescence intensity spectra to obtain respective first and second spectral data;
• and
• calculating a probability that the tissue sample belongs to a first tissue classification from the second spectral data, a probability that the tissue sample belongs to the second tissue classification from the first spectral data, and a probability that the tissue sample belongs to the third tissue classification from the second spectral data.

A method of developing an index for calculating a probability that a tissue belongs to one of a plurality of histo-pathologic tissue classifications, comprising:

• providing a plurality of tissue samples;
• illuminating the tissue samples with electromagnetic radiation having wavelengths of about 337 nm, about 380 nm, and about 460 nm to excite the tissue samples into producing fluorescence intensity spectra;
• detecting the fluorescence intensity spectra to obtain first, second and third spectral data from the tissue samples illuminated at respectively the wavelengths of about 337 nm, about 380 nm and about 460 nm in the illuminating step;
• and
• forming a first set of principle components from the first spectral data that account for a significant amount of variation in the first spectral data and show statistically significant differences between first and second histo-pathologic tissue classifications;
• calculating first probability distribution functions for the first and second histo-pathologic tissue classifications;
• forming a second set of principle components from the second spectral data that account for a significant amount of variation in the second spectral data and show statistically significant differences between third and fourth histo-pathologic tissue classifications;
• calculating second probability distribution functions for the third and fourth histo-pathologic tissue classifications;
• forming a third set of principle components from the third spectral data that account for a significant amount of variation in the third spectral data and show statistically significant differences between fifth and sixth histo-pathologic tissue classifications;
• calculating third probability distribution functions for the fifth and sixth histo-pathologic tissue classifications.

A method of assigning a probability that a tissue sample belongs to a particular tissue category, comprising:

• providing a tissue sample;
• illuminating the tissue sample with electromagnetic radiation having wavelengths of about 337 nm, about 380 nm, and about 460 nm to excite the tissue sample into producing fluorescence intensity spectra;
• detecting the fluorescence intensity spectra to obtain spectral data;
• obtaining principal components PC1, PC2, PC4, PC5, and PC7 from said spectral data, and
• calculating from principal components PC1 and PC2 from spectral data at about 337 nm a probability of squamous intraepithelial lesion as distinguished from normal squamous epithelial tissue, from principal components PC2 and PC5 from spectral data at about 380 rn a probability of squamous intraepithelial lesion as distinguished from normal columnar epithelia and inflammation, and from principal components PC4 and PC7 from spectral data at about 460 nm a probability of high grade squamous intraepithelial lesion as distinguished from low grade squamous intraepithelial lesion.

A method of assigning a probability that a tissue sample belongs to a particular tissue category, comprising:

• providing a tissue sample;
• illuminating the tissue sample with electromagnetic radiation having wavelengths of about 380 nm and about 460 nm to excite the tissue sample into producing fluorescence intensity spectra;
• detecting the fluorescence intensity spectra to obtain spectral data;
• obtaining principal components PC1, PC2, PC4, PC5, and PC7 from said spectral data;
• and
• calculating from principal components PC1 and PC2 from spectral data at about 460 nm a probability of squamous intraepithelial lesion as distinguished from normal squamous epithelial tissue, from principal components PC2 and PC5 from spectral data at about 380 nm a probability of squamous intraepithelial lesion as distinguished from normal columnar epithelia and inflammation, and from principal components PC4 and PC7 from spectral data at about 460 nm a probability of high grade squamous intraepithelial lesion as distinguished from low grade squamous intraepithelial lesion.

A method of detecting abnormal epithelial tissue in or from an anatomical feature of a mammal, comprising:

• identifying a plurality of individuals having the anatomical feature from a population of interest, the respective anatomical features of the individuals collectively having a diversity of epithelial tissue categories, including normal and abnormal epithelial tissue categories, that is representative of the diversity of the epithelial tissue categories collectively occurring in the respective anatomical features of the population of interest;
• optically applying electromagnetic energy comprising a first and second wavelengh to plural epithelial tissue regions of the respective anatomical features of the individuals, said first and second wavelengths selected to produce spectra indicative of a first and second tissue abnormality property, respectively;
• optically detecting spectra from the epithelial tissue regions of the respective anatomical features of the individuals resulting from the step of optically applying electromagnetic energy to plural epithelial tissue regions;
• processing the optically detected spectra into first spectral data;
• transforming the first spectral data into a dimensionally reduced set of variables that significantly accounts for variance in the first spectral data and that exhibits statistically significant differences between the epithelial tissue categories;
• optically applying electromagnetic energy comprising said first and second wavelength to epithelial tissue of the anatomical feature of a subject individual, said first and second wavelengths selected to produce spectra indicative of said first and second tissue abnormality properties, respectively;
• optically detecting a spectrum from the epithelial tissue of the anatomical feature of the subject individual resulting from the step of optically applying electromagnetic energy to epithelial tissue;
• processing the optically detected spectrum into second spectral data;
• and
• calculating the probability that the epithelial tissue of the anatomical feature of the subject individual belongs to each of one or more of the epithelial tissue categories from the second spectral data and the dimensionally reduced set.

A method of identifying a probable tissue category for epithelial tissue of a mammalian patient from among a plurality of epithelial tissue categories, comprising:

• identifying the patient with a predetermined population having prior probabilities of the tissue categories therein;
• applying electromagnetic radiation comprising a first and second wavelength to a plurality of tissue sites of subjects in the population and to the tissue of the patient, the first and second wavelengths selected to excite respectively a first autofluorescence intensity spectra indicative of first and second tissue categories and second autofluorescence intensity spectra indicative of third and fourth tissue categories;
• obtaining first and second sets of subject autofluorescence spectral data from the first and second autofluorescence intensity spectra from the electromagnetic radiation applying step;
• preprocessing the first and second sets of subject autofluorescence spectral data to reduce inter-subject and intra-subject variation therein;
• forming a first dimensionally reduced set of emission variables from the preprocessed set of first subject autofluorescence spectral data, including a first reduced eigenvector matrix, that shows statistically significant differences between the first and second tissue categories and that significantly accounts for variation in the preprocessed set of first subject autofluorescence spectral data;
• calculating first subject scores from the first dimensionally reduced set of emission variables for the first and second tissue categories;
• forming a second dimensionally reduced set of emission variables from the preprocessed set of second subject autofluorescence spectral data, including a second reduced eigenvector matrix, that shows statistically significant differences between the third and fourth tissue categories and that significantly accounts for variation in the preprocessed set of second subject autofluorescence spectral data,
• calculating second subject scores from the second dimensionally reduced set of emission variables for the third and fourth tissue categories;
• obtaining respective sets of patient autofluorescence spectral data from the electromagnetic radiation applying step;
• preprocessing the sets of patient autofluorescence spectral data to reduce intra-patient variation therein;
• concatenating the preprocessed patient autofluorescence spectral data into vectors;
• processing the vectors with the first reduced eigenvector matrix to obtain first patient scores;
• calculating posterior probabilities for the first and second tissue categories from the first subject scores, from the first patient scores, and from prior probabilities of the first and second tissue categories;
• processing the vectors with the second reduced eigenvector matrix to obtain second patient scores;
• and
• calculating posterior probabilities for the third and fourth tissue categories from the second subject scores, from the second patient scores, and from prior probabilities of the third and fourth tissue categories.

A method of obtaining a probability that an epithelial tissue site of a mammalian patient contains tissue belonging to a particular tissue category, comprising:

• identifying a training set of epithelial tissue sites identified with a plurality of individuals, the training set of epithelial tissue sites having tissues belonging to a plurality of tissue categories and occurring with known prior probabilities;
• obtaining optical response training data from the epithelial tissue sites in the training set by illuminating said tissue sites with electromagnetic radiation comprising a first wavelength selected to produce first spectra indicative of first and second tissue categories, and a second wavelength selected to produce second spectra indicative of third and fourth tissue categories;
• calculating a first dimensionally reduced set of variables from the optical response training data that shows statistically significant differences between the first and second tissue categories and that significantly accounts for variation in the first spectra;
• calculating a second dimensionally reduced set of variables from the optical response training data that shows statistically significant differences between the third and fourth tissue categories and that significantly accounts for variation in the preprocessed optical response training data;
• obtaining optical response patient data from the epithelial tissue site of the patient;
• and
• calculating the probability that the epithelial tissue site of the patient contains the particular tissue type using the optical response patient data, the prior probabilities, and the first and second dimensionally reduced set.

An index embodied on a computer-readable medium for calculating a probability that epithelial tissue in or from an anatomical feature of a mammal belongs to each of a plurality of epithelial tissue categories, the index being formed by the steps of:

• identifying a plurality of individuals having the anatomical feature from a population of interest, the respective anatomical features of the individuals collectively having a diversity of the epithelial tissue categories, including normal and abnormal epithelial tissue categories, that is representative of the diversity of the epithelial tissue categories collectively occurring in the respective anatomical features of the population of interest;
• optically applying electromagnetic energy comprising a first wavelength and a second wavelength to plural epithelial tissue regions of the respective anatomical features of the individuals, said first and second wavelengths selected to produce first fluorescence intensity spectra indicative of a first tissue abnormality and second fluorescence intensity spectra indicative of a second tissue abnormality, respectively;
• optically detecting the first and second fluorescence intensity spectra from the epithelial tissue regions of the respective anatomical features of the individuals resulting from the step of optically applying electromagnetic energy;
• processing the first and second fluorescence intensity spectra into first and second spectral data;
• transforming the first spectral data into a first dimensionally reduced set of variables that significantly accounts for variance in the first spectral data and that exhibits statistically significant differences between the first tissue abnormality and at least one of the other epithelial tissue categories;
• transforming the second spectral data into a second dimensionally reduced set of variables that significantly accounts for variance in the second spectral data and that exhibits statistically significant differences between the second tissue abnormality and at least one of the other epithelial tissue categories;
• and
• deriving the index from the first and second dimensionally reduced sets.