Multi-excitation diagnostic system and methods for classification of tissue
Methods and systems for in vivo classification of tissue are disclosed. The tissue is irradiated with light from multiple light sources and light scattered and fluoresced from the tissue is received. Distinct emissions of the sample are identified from the received light. An excitation-emission matrix is generated ( 1002 ). On-diagonal and off-diagonal components of the excitation-emission matrix are identified ( 1004, 1006, 1008 ). Spectroscopic measures are derived from the excitation-emission matrix ( 1014 ), and are compared to a database of known spectra ( 1016 ) permitting the tissue to be classified as benign or malignant ( 1018 ). An optical biopsy needle or an optical probe may be used to contemporaneously classify and sample tissue for pathological confirmation of diagnosis.
1. A system for real-time classifying an in vivo location of prostate tissue within a prostate of a patient, the system comprising:
an optical probe comprising:
a needle having a tip, wherein at least a portion of the needle is configured to penetrate the prostate so that the tip is directed toward the in vivo location of prostate tissue,
one or more transmitting fibers, wherein the transmitting optical fibers transmit light from a light source to the optical probe and wherein the optical probe is configured to transmit light to the in vivo location, and
one or more receiving fibers, wherein the receiving optical fibers are configured to receive light reflected from the in vivo location;
a spectrometer connected with the optical probe, wherein the spectrometer generates optical spectral data from the received light; and
a processing element that forms a relational database, wherein the relational database comprises a plurality of correlations between known digital data and tissue classifications wherein each of the plurality of correlations were generated by
a) in vivo capture of received light reflected from a test location within a prostate of a test patient, wherein the light source generates the light and the received light reflected from the test location is captured by the one or more receiving fibers,
b) generation of test optical spectral data from the received light from the test location,
c) generation of the known digital data from the test optical spectral data in the form of a matrix comprising:
a plurality of diagonal elements representative of scattering and absorption;
a plurality of off-diagonal elements of the matrix representative of fluorescence of the received light;
expanding the matrix by separating the plurality of diagonal elements into a plurality of components, wherein the expansion maintains original values of the plurality of diagonal elements; and
d) histological evaluation of an excision of the test location to determine a tissue classification corresponding to the known digital data,
wherein the processing element is in communication with the relational database and the spectrometer and compares the digital data translated from the optical spectral data to the known digital data of the relational database and provides a real time classification based on at least one of the plurality of correlations.
2. A system as in claim 1 , wherein the tissue classification comprises one of: benign, malignant, abnormal, cancerous, dysplastic, diseased or calcified.
3. A system as in claim 1 , wherein the tissue classification comprises high grade disease or low grade disease.
4. A system as in claim 1 , wherein the optical spectral data comprises fluorescence from at least one endogenous fluorophore within the in vivo location.
5. A system as in claim 4 , wherein the at least one endogenous fluorophore comprises one or more of tryptophan, tyrosine, phenylalanine, collagen, elastin, FAD, flavins, NADH, NADPH, Vitamin A, Vitamin K, Vitamin D, pyridoxine, pyridoxamone, pyridoxal, pyridoxic acid, pyridoxal 5′-phosphate, Vitamin B12, phospholipids, lipofuscin, ceroid, or porphyrins.
6. A system as in claim 4 , wherein the optical spectral data comprises elastic scattering spectra reflected from the in vivo location.
7. A system as in claim 1 , wherein the optical probe includes at least one transmitting fiber extending to the tip so as to transmit the light from at least one light source to the in vivo location and to transmit the received light from the in vivo location.
8. A system as in claim 7 , wherein the at least one transmitting fiber has a distal end which is flush with a blade surface of the tip of the optical probe.
9. A system as in claim 7 , wherein the tip of the optical probe has an aperture configured to restrict the light from the at least one transmitting fiber so that the in vivo location exposed to the light from the at least one transmitting fiber has a size of a point.
10. A system as in claim 1 , wherein the optical probe includes at least one transmitting fiber extending to the tip so as to transmit the light from at least one light source to the in vivo location and at least one optical fiber extending from the tip so as to transmit the received light from the in vivo location.
11. A system as in claim 1 , wherein the needle of the optical probe is advanceable so as to direct the tip toward a new in vivo location of prostate tissue without removing and reintroducing the optical probe to the prostate, and wherein the computational device provides a new real time classification for the new in vivo location.
12. A system as in claim 1 , wherein the optical probe includes a mechanism for delivering a therapeutic agent to the in vivo location.
13. A system as in claim 1 , wherein the optical probe is configured to deliver a therapeutic modality to the in vivo location.
14. A system as in claim 13 , wherein the therapeutic modality comprises brachytherapy or cryotherapy.
15. A system as in claim 1 , wherein the processing element comprises diagnostic spectroscopy software for analyzing the optical spectral data when translating the optical spectral data into digital data.
16. A system as in claim 1 , wherein the processing element further utilizes at least one support vector machine algorithm for analyzing the optical spectral data when translating the optical spectral data into digital data.
17. A system as in claim 1 , further comprising a light source which generates the light, wherein the light source comprises at least one bandwidth-limited incident light source in the UV or near UV spectrum.