# Patent

## Title
Multimodal spectroscopic systems and methods for classifying biological tissue

## Abstract

Multimodal optical spectroscopy systems and methods produce a spectroscopic event to obtain spectroscopic response data from biological tissue and compare the response data with preset criteria configured to correlate the measured response data and the most probable attributes of the tissue, thus facilitating classification of the tissue based on those attributes for subsequent biopsy or remedial measures as necessary.

## Background
CROSS-REFERENCE TO RELATED APPLICATION
The instant application claims priority to U.S. Provisional Patent Application Ser. No. 61/058,966, filed Jun. 5, 2008, the entire specification of which is expressly incorporated herein by reference.


STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
The invention was made with U.S. Government support, in whole or in part, from NIH grant no. CA-114542. The U.S. Government has certain rights in the invention.


FIELD OF THE INVENTION
The present invention generally relates to medical imaging systems and in particular, to employing multimodal spectroscopy in the diagnosis of biological tissue.
BACKGROUND OF THE INVENTION
The following description of the background of the invention is provided to aid in understanding the invention, but is not admitted to describe or constitute prior art to the invention. The contents of the articles, patents, and patent applications, and all other documents and electronically available information mentioned or cited in this application, are hereby incorporated by reference in their entirety to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference. Applicants reserve the right to physically incorporate into this application any and all materials and information from any such articles, patents, patent applications, or other documents.
Pancreatic adenocarcinoma has a five-year survival rate of only 5%, making it the fourth-leading cause of cancer death in the United States. “Cancer Statistics 2008,” (website: cancer.org). Current diagnostic procedures are unable to diagnose the disease in its early stages. T. P. Yeo, et al., “Pancreatic cancer,” Current Problems in Cancer 26, 176-275 (2002). In addition, diagnosis is compromised due to an overlap of symptoms with pancreatitis (inflammation of the pancreas). As a result, endoscopic ultrasound-guided fine needle aspiration (EUS-FNA), an established method for the diagnosis of pancreatic adenocarcinoma, has only 54% sensitivity for cancer in the setting of pancreatitis. A. Fritscher-Ravens et al., “Endoscopic ultrasound-guided fine-needle aspiration in focal pancreatic lesions: a prospective intraindividual comparison of two needle assemblies,” Endoscopy 33, 484-490 (2001). As many as 9% of patients undergo complicated Whipple surgery to remove a significant portion of their pancreas, only to reveal absence of the disease during pathological examination of the resected specimen. S. C. Abraham et al., “Pancreaticoduodenectomy (Whipple Resections) in Patients Without Malignancy: Are They All ‘Chronic Pancreatitis’?,” The American Journal of Surgical Pathology 27, 110-120 (2003).
Clearly, the detection of the disease in its early stages and its distinction from pancreatitis would greatly reduce the instances of unnecessary surgery, and more importantly, improve the chances of patient survival.
Multiple studies over the years have employed optical techniques as a means for minimally invasive detection of breast, cervical, colon, and esophageal cancer, among other things. Z. Volynskaya et al., “Diagnosing breast cancer using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy,” J Biomed Opt 13, 024012 (2008); G. Zonios et al., “Diffuse reflectance spectroscopy of human adenomatous colon polyps in vivo,” Applied Optics 38, 6628-6637 (1999); S. K. Chang et al., “Model-based analysis of clinical fluorescence spectroscopy for in vivo detection of cervical intraepithelial dysplasia,” J Biomed Opt 11,—(2006); and I. Georgakoudi and M. S. Feld, “The combined use of fluorescence, reflectance, and light-scattering spectroscopy for evaluating dysplasia in Barrett's esophagus,” Gastrointestinal Endoscopy Clinics of North America 14, 519-537 (2004).
However, there is little support for applying optical methods for pancreatic cancer detection, possibly owing to the relatively inaccessibility of the pancreas.
Recently, it is understood that Optical Coherence Tomography (OCT) has been applied to both in vivo and ex vivo detection of pancreatic cancer. P. A. Testoni et al., “Intraductal optical coherence tomography for investigating main pancreatic duct strictures,” Am J Gastroenterol 102, 269-274 (2007); P. A. Testoni et al., “Optical coherence tomography to detect epithelial lesions of the main pancreatic duct: an Ex Vivo study,” Am J Gastroenterol 100, 2777-2783 (2005).
Furthermore, Near-Infrared Spectroscopy and Partial-wave microscopic spectroscopy have also been applied in ex vivo studies. V. R. Kondepati et al., “Near-infrared fiber optic spectroscopy as a novel diagnostic tool for the detection of pancreatic cancer,” J Biomed Opt 10,—(2005); H. Subramanian et al., “Partial-wave microscopic spectroscopy detects subwavelength refractive index fluctuations: an application to cancer diagnosis,” Opt Lett 34, 518-520 (2009). In the latter, pancreatic cancer cells on microscopic slides were studied. Four-dimensional elastic light-scattering spectroscopy, and low-coherence enhanced backscattering spectroscopy have been employed for the ex vivo study of duodenal tissue based on a field effect hypothesis that predicts changes in the duodenum owing to the presence of cancer in the pancreas. V. Turzhitsky et al., “Investigating population risk factors of pancreatic cancer by evaluation of optical markers in the duodenal mucosa,” Dis Markers 25, 313-321 (2008); Y. Liu et al., “Optical markers in duodenal mucosa predict the presence of pancreatic cancer,” Clin Cancer Res 13, 4392-4399 (2007).
A number of chemometric and statistical techniques have been used in the literature to develop tissue classification algorithms employing optical spectroscopy data. These include, multiple linear regression analysis, linear discriminant analysis, backpropagating neural network analysis, principal component analysis, logistic discrimination, partial least squares, multivariate linear regression, and support vector machine. N. Ramanujam et al., “Development of a multivariate statistical algorithm to analyze human cervical tissue fluorescence spectra acquired in vivo,” Lasers in Surgery and Medicine 19, 46-62 (1996); Z. F. Ge et al., “Identification of colonic dysplasia and neoplasia by diffuse reflectance spectroscopy and pattern recognition techniques,” Applied Spectroscopy 52, 833-839 (1998); G. M. Palmer et al., “Comparison of Multiexcitation Fluroescence and Diffuse Reflectance Spectroscopy for the Diagnosis of Breast Cancer,” Ieee T Bio-Med Eng 50, 1233-1242 (2003); S. K. Chang et al., “Combined reflectance and fluorescence spectroscopy for in vivo detection of cervical pre-cancer,” J Biomed Opt 10, 024031 (2005); A. Dhar et al., “Elastic scattering spectroscopy for the diagnosis of colonic lesions: initial results of a novel optical biopsy technique,” Gastrointest Endosc 63, 257-261 (2006); S. C. Chu et al., “Comparison of the performance of linear multivariate analysis methods for normal and dyplasia tissues differentiation using autofluorescence spectroscopy,” Ieee T Bio-Med Eng 53, 2265-2273 (2006); and G. Salomon et al., “The Feasibility of Prostate Cancer Detection by Triple Spectroscopy,” Eur Urol, (2008). Additionally, quantitative photon-tissue interaction models of reflectance and fluorescence have been utilized in optical methods for detecting breast cancer [4], colon cancer [5], cervical cancer [6], and Barrett's esophagus [7]. Recently, photon-tissue interaction modeling was incorporated into an optical study of murine tumors consisting of human pancreatic cancer cells, in order to quantitatively distinguish different tumor regions [8]. Z. Volynskaya, et al., “Diagnosing breast cancer using diffuse reflectance and intrinsic fluorescence spectroscopy,” J. Biomed. Opt. 13, 024012 (2008); G. Zonios, et al., “Diffuse reflectance spectroscopy of adenomatous colon polyps in vivo,” Appl. Opt. 38, 6628-6637 (1999); S. K. Chang, et al., “Model-based analysis of clinical fluorescence spectroscopy for in vivo detection of cervical intraepithelial dysplasia,” J. Biomed. Opt. 11, 024008 (2006); I. Georgakoudi and M. S. Feld, “The combined use of fluorescence, reflectance, and light-scattering spectroscopy for evaluating dysplasia in Barrett's esophagus,” Gastroint. Endosc. Clin. N. Am. 14, 519-537 (2004); and V. Krishnaswamy, et al., “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt. 14, 014004 (2009).
SUMMARY OF THE INVENTION
The invention is generally directed to systems and methods configured for, among other things, directing electromagnetic radiation or light of a plurality of wavelengths onto biological tissue to produce a measurable spectroscopic event; collecting a plurality of spectroscopic response data regarding the biological tissue, which may include fluorescence and reflectance spectra, time-resolved spectroscopy, time-resolved fluorescence spectroscopy or decay measurements; comparing the response data with preset criteria that correlates the collected data with tissue attributes which facilitate a tissue classification, that is, attributes which may be indicative of a particular condition (for example, the presence of a tumor or disease) and/or probative of the relative health of the tissue (for example, normal or abnormal); determining which, if any, of the preset criteria are satisfied; and classifying the tissue based on the tissue attributes identified by the preset criteria satisfied.
The systems and methods described herein are well-suited to be used in conjunction with or as a substitute for random biopsies, since the optical systems and methods are non-invasive, do not require tissue removal, and can be performed in-vivo. Moreover, they are fast (can be applied in real time), are relatively non-expensive, are able to work on microscopic scale, and thus can find very small sites for tissue diagnosis, which may be missed by random biopsies. The systems and methods herein are also well-suited to be used in endoscopic ultrasound-guided procedures and apparatus, or delivered through an endoscope or needle by a trained professional.
In some embodiments, the invention is directed to a method of employing multimodal spectroscopy to classify tissue which includes: illuminating a biological tissue sample to produce a measurable spectroscopic event; collecting spectroscopic response data from the spectroscopic event, wherein the response data includes measurements derived from fluorescence and reflectance signals associated with the tissue sample; comparing the response data with preset criteria relating the collected response data with one or more attributes of the tissue sample, wherein each attribute is at least partially indicative of a tissue classification; determining whether preset criteria are satisfied by the collected data; and classifying the tissue sample according to the attributes identified by the preset criteria satisfied.
In some embodiments of the aforementioned method, the step of illuminating the biological tissue sample is configured to produce a plurality of measurable spectroscopic events. The illuminating step may consist of a spectroscopic event including illumination wavelengths of about 400 nm to about 750 nm to facilitate collecting reflectance signal measurements and a spectroscopic event including illumination wavelengths of about 355 nm to facilitate collecting fluorescence signal measurements.
In some embodiments, the measurements derived from fluorescence and reflectance signals include reflectance spectra, fluorescence spectra, time-resolved spectroscopic measurements, and/or time-resolved fluorescence decay.
In some embodiments, the attributes of the tissue sample include one or more histological features. In some embodiments, the step of classifying the tissue sample further includes classifying the tissue as either normal or abnormal.
In some embodiments, the preset criteria relates the deviation between the collected spectroscopic response data at a plurality of wavelengths for the tissue sample and spectroscopic response data at the plurality of wavelengths for normal tissue with one or more histological features associated with the tissue sample. In such an embodiment of the invention, the method may include steps such as measuring the extent of the deviation between the collected response data for the tissue sample and the response data for normal tissue; determining whether the extent of the deviation is sufficient for satisfying any of the preset criteria; and classifying the tissue sample according to the histological feature identified by the preset criteria satisfied. Also, the step of measuring the amount of deviation between the collected response data for the tissue sample and the response data for normal tissue may in particular involve measuring the extent of deviation in a range of wavelengths between about 360 nm to about 750 nm.
In some embodiments, the attributes of the tissue sample include the nuclear size of cells associated with the tissue sample, the collagen content associated with the tissue sample, the nicotinamide adenine dinucleotide (NADH) content associated with the tissue sample, and the flavin adenine dinucleotide (FAD) content associated with the tissue sample.
Some embodiments of the invention are directed to a system for classifying biological tissue using multimodal optical spectroscopy, which includes: a light source for generating light to illuminate a biological tissue sample; a probe configured for directing the light generated by the light source onto the tissue sample to illuminate the tissue sample and generate a measurable spectroscopic event; one or more detectors configured for collecting spectroscopic response data, wherein the spectroscopic response data includes measurements derived from fluorescence and reflectance signals associated with the tissue sample; and a data processor configured for analyzing the collected spectroscopic response data, comparing the collected spectroscopic response data with preset criteria relating the collected spectroscopic response data with one or more attributes associated with the tissue sample, wherein each attribute is at least partially indicative of a tissue classification, determining whether preset criteria are satisfied by the collected spectroscopic response data, and classifying the tissue sample according to the attributes identified by the preset criteria satisfied.
In some embodiments of the aforementioned system, the probe further includes a plurality of optical fibers. The plurality of optical fibers may be configured for directing light onto the tissue sample, receiving fluorescence and reflectance signals from the tissue sample and directing the received fluorescence and reflectance signals to the one or more detectors, among other things.
In some embodiments, the probe includes a first optical fiber configured for delivering reflectance illumination to the tissue sample, a second optical fiber configured for delivering fluorescence illumination to the tissue sample and a third optical fiber configured for detecting emitted reflectance and fluorescence from the tissue sample. The first, second and third optical fibers may be disposed in a generally triangular cross-sectional arrangement. The system may also include a device or feature which is configured for alternately covering one or more of the optical fibers while leaving others uncovered during the application of light onto the tissue sample.
The detectors in some embodiments of the invention may be configured to derive a variety of measurements from the fluorescence and reflectance signals, such as reflectance spectra, fluorescence spectra and time-resolved fluorescence spectroscopy or time-resolved fluorescence decay measurements associated with the tissue sample.
In some embodiments, the light source further includes a light source configured for generating light at wavelengths of about 400 nm to about 750 nm to facilitate collecting reflectance signal measurements and a light source configured for generating light at wavelengths of about 355 nm to facilitate collecting fluorescence signal measurements.
Some embodiments of the invention are directed to a method employing multimodal optical spectroscopy to classify pancreatic tissue, which includes the steps of: illuminating a pancreatic tissue sample to produce a measurable spectroscopic event; collecting spectroscopic response data from the spectroscopic event, wherein the response data includes measurements derived from fluorescence and reflectance signals associated with the tissue sample; comparing the response data with preset criteria relating the collected spectroscopic response data with one or more histological features associated with the pancreatic tissue sample, wherein the histological features are indicative of a tissue classification of either normal pancreatic tissue, adenocarcinoma or pancreatitis; determining if preset criteria are satisfied; and classifying the pancreatic tissue sample as either normal, adenocarcinoma or pancreatitis based on the histological features identified by the preset criteria satisfied.
The step of comparing the response data with preset criteria in some embodiments of the aforementioned method may further include comparing the response data at wavelengths ranging between about 360 nm to about 750 nm with the preset criteria. Also, in some embodiments, the measurements derived from fluorescence and reflectance signals associated with the tissue sample include fluorescence spectra, reflectance spectra and time-resolved fluorescence decay spectroscopic measurements, or any other complementary spectroscopic measurements.
These and other aspects of the invention will become more readily apparent to those of ordinary skill in the art from the following detailed description when considered in connection with the accompanying figures.



BRIEF DESCRIPTION OF THE DRAWINGS
FIGS. 1A and 1B provide graphs illustrating a representative fit of a mathematical model, formed according to some embodiments of the invention, versus average measured result for reflectance spectra of pancreatic adenocarcinoma (FIG. 1A) and pancreatitis (FIG. 1B), with residuals;
FIGS. 2A and 2B provide graphs illustrating wavelength-resolved scattering (FIG. 2A) and absorption (FIG. 2B) coefficients of normal pancreatic tissue, pancreatic adenocarcinoma, and pancreatitis, extracted from representative fits of experimental data to the reflectance model;
FIGS. 3A, 3B and 3C provide graphs illustrating intrinsic fluorescence spectra of normal pancreatic tissue (FIG. 3A), pancreatitis (FIG. 3B), and pancreatic adenocarcinoma (FIG. 3C), shown with a representative fit to a linear combination of measured and blue-shifted collagen, NADH, and FAD basis spectra;
FIGS. 4A and 4B provide bar graphs illustrating the performance of the various modeling methods employed by some embodiments of the invention, namely, chemometric, PTI model, SpARC, and hybrid tissue classification algorithms in identifying adenocarcinoma tissue;
FIG. 5 is a schematic diagram of a system constructed in accordance with some embodiments of the invention;
FIG. 6 is a cross sectional view of optical fibers at the distal end of an exemplary probe used with a system constructed accordance with some embodiments of the invention; and
FIG. 7 is a flow chart illustrating the manner in which the hybrid algorithm may be employed in some embodiments to classify a pancreatic tissue site into normal, adenocarcinoma, or pancreatitis.



DETAILED DESCRIPTION OF INVENTION
Some embodiments of the invention employ multimodal optical spectroscopic systems and methods to obtain data from biological tissue and compare the data with preset criteria configured to aid in the diagnosis of the tissue health or condition, wherein the preset criteria relates the data with the most probable attributes of the tissue. The multimodal spectroscopic systems employed may include fluorescence spectroscopy, reflectance spectroscopy and time-resolved spectroscopy, among others.
In some embodiments, data obtained through multimodal optical spectroscopy is correlated with the results of a microscopic histological examination of a normal tissue sample to develop the preset criteria by which further tissue samples are to be assessed. In particular, the preset criteria may be based on a relationship between spectral data and the histological aspects of the tissue which are most likely to be indicative of a specific attribute so as to lead to a unique classification of the tissue. For example, the preset criteria may ultimately be used to provide attributes such as the NADH content, FAD content, collagen content and/or nuclear size values associated with the tissue, or any other characteristics which are probative of tissue health, indicative of certain conditions, or otherwise provide insight into the relative health of the tissue.
Thus, systems and methods of the invention can be used to facilitate real-time (or near real-time) diagnosis of further tissue samples and may be employed with excised tissue or in vivo. Systems and methods of the invention may be used to ascertain tissue conditions and classify tissue during a surgical procedure. The systems and methods discussed herein may be utilized to guide a biopsy procedure. For example, systems and methods of the invention may be incorporated for a variety of fiber probe configurations through any kind of endoscope or needle in a clinic or other facility or with endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) procedures.
As mentioned above, the preset criteria relate measured data to tissue attributes in a manner which facilitates a tissue classification. Depending on the application, tissue classifications may include normal, abnormal, inflammation, disease or adenocarcinoma, for example. In some embodiments, the measured data may linked by the preset criteria to histological features which are hallmarks of particular tissue conditions such as adenocarcinoma. In this manner, some embodiments of the invention utilize the measured data to quantitatively distinguish between normal and abnormal tissue conditions.
Systems and methods of the invention employ multimodal optical spectroscopy, and may include exposing biological tissue to a source of electromagnetic radiation to produce a spectral event, collecting spectral response data regarding the tissue at a plurality of wavelengths, correlating the spectral response data with the histology of the tissue based on preset criteria, and classifying the tissue condition based on the preset criteria satisfied. In some embodiments, the spectral response data collected includes fluorescence spectra, reflectance spectra and time-resolved fluorescence decay information, but may include any combination of parameters derived from the spectral event or response data that are likely to provide complementary information about the biochemical, architectural and morphological state of the tissue of interest.
The preset criteria may be derived by a variety of methods, such as the empirical data collection and mathematical modeling techniques discussed herein with respect to pancreatic tissue. Although the illustrations and examples herein focus on pancreatic tissues, it should be readily apparent that the invention is not to be limited to pancreatic tissues, and mathematical models of the invention are also of potential use for optical diagnostic applications in other biological tissues. It should be understood that the embodiments of the invention may be useful for various applications and procedures throughout the medical arts. Thus, the techniques and embodiments discussed herein should not be construed as limiting, as analyzing pancreatic tissue to differentiate between normal pancreatic tissue, pancreatitis, and adenocarcinoma is merely an example of a suitable application for the embodiments of the invention.
Illustration 1
1. Introduction
Systems and methods discussed herein advantageously provide the ability to quantitatively explain prominent disease-related changes to human pancreatic tissue in terms of biologically meaningful parameters based on spectral data consisting of collected fluorescence and reflectance spectra. The systems and methods of the invention further provide the ability to classify pancreatic tissue as normal, pancreatic or adenocarcinoma. In some embodiments, the preset criteria is derived from mathematical modeling of experimentally measured spectral data used to quantitatively describe differences in the reflectance and fluorescence spectra of normal pancreatic tissue, pancreatic adenocarcinoma, and pancreatitis.
For example, it has been found that pancreatic adenocarcinoma has larger nuclei than benign pancreatic tissue, and both adenocarcinoma and chronic pancreatitis have more collagenous stroma than normal pancreatic tissue. The mathematical model of reflectance quantitatively linked increased nuclear size in adenocarcinoma to changes in the measured reflectance spectra, particularly within the range from about 455 nm to about 525 nm. The fluorescence model quantitatively linked increased collagen content in pancreatitis and adenocarcinoma to changes in the composition of the measured fluorescence spectra. Fitting the reflectance model to the experimental data also enabled the extraction of values for the optical absorption and scattering coefficients of human pancreatic tissues.
The mathematical model of some embodiments provided a quantitative link between optical spectroscopy and tissue histology as shown in Table 1, suggesting a potential clinical application of optical spectroscopy and modeling to minimally invasive early cancer diagnostics in the pancreas.







TABLE 1







Key histological features of pancreatic tissues detected


by optical spectroscopy










Key histological features




(relative to normal
Optical signature


Pancreatic tissue
pancreatic tissue)
found in





Adenocarcinoma
Increased nuclear size
Reflectance spectra



Greater stromal collagen content
Fluorescence spectra


Pancreatitis
Greater stromal collagen content
Fluorescence spectra









2. Clinical Measurements of Tissue Optical Spectra
In this embodiment, a Reflectance and Fluorescence Lifetime Spectrometer (RFLS) was used to obtain reflectance and fluorescence measurements of human pancreatic tissue within about 15 minutes of removal via Whipple resection at the University of Michigan Medical Center. Reflectance measurements were acquired by using a CW tungsten halogen lamp (e.g., HL 2000FHSA, Ocean Optics, Dunedin, Fla.) to deliver white light (about 400 to about 750 nm wavelength) to the tissue; fluorescence measurements utilized a 355 nm pulsed excitation source (e.g., PNV001525-140, JDS Uniphase, San Jose, Calif.). A spectrograph (e.g., MS 125, Oriel Instruments, Stratford, Conn.) and an intensified charge-coupled device (ICCD) camera (e.g., ICCD 2063, Andor Technology, Belfast, Northern Ireland) were used to detect tissue reflectance (about 400 to about 750 nm) and fluorescence (about 360 to about 700 nm) spectra. The light from the lamp and the laser was delivered to the tissue via two separate optical fibers with core diameters of 600 μm. The reflected or emitted fluorescence photons from the tissue were collected and transported to the detectors by a third identical fiber.
Measurements were taken at five sites on each tissue specimen. One pancreatectomy specimen was evaluated from each of two different patients. Each measured site was biopsied under the supervision of a clinical pathologist, and the biopsied samples were evaluated histologically. For the first patient, two of the sites were histologically normal and three were pancreatitis, while for the second patient, all five sites sampled were adenocarcinoma. There were noticeable differences in both the reflectance and fluorescence spectra of the three tissue types, most notably around 500 nm for the reflectance spectra and near 400 nm for the fluorescence spectra.
3. Mathematical Model of Reflectance Spectra: Theory and Results
3.1 Modeling Scattering and Absorption Coefficients of Pancreatic Tissues
The lineshapes of reflectance spectra from biological tissues are known to be primarily dependent on the absorption and scattering coefficients of the media. Absorbers such as blood will attenuate the light, while scatterers such as cell nuclei and collagen fibers will change the paths of the photons, eventually leading some of them back to the tissue surface. Mie theory was used to describe the scattering coefficient μs, as a function of wavelength, in terms of the size and density of the scatterers in the tissue. A. Sefkow, et al., “Method for Measuring Cellular Optical Absorption and Scattering Evaluated Using Dilute Cell Suspension Phantoms,” Appl. Spectrosc. 55, 1495-1501 (2001); C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles (Wiley, 1983); L. T. Perelman, et al., “Observation of Periodic Fine Structure in Reflectance from Biological Tissue: A New Technique for Measuring Nuclear Size Distribution,” Phys. Rev. Lett. 80, 627-630 (1998); and I. S. Saidi, et al. “Mie and Rayleigh modeling of visible-light scattering in neonatal skin,” Appl. Opt. 34, 7410-7418 (1995).
Two Mie theory terms were used: one for spherical scatterers (cell nuclei) and another for cylindrical scatterers (collagen fibers). For the spherical Mie scattering term, the Van de Hulst approximation was used:




  
    
      
        
          
            
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In Eq. (1), L is the scatterer diameter, Ns is the number of scatterers per unit volume, and ns (nm) is the index of refraction of the scatterer (surrounding medium). The wavelength λ is defined as λvac/nm, where λvac is the wavelength of the incident light in vacuum. For biological tissue, nm was assumed to be 1.33 (for water), while ns was set to 1.3657. The values of L and Ns were estimated from histology to be 9 μm and 7×107 cm−3, respectively. The cylindrical scattering term was modeled by a combination of Bessel functions, in which the diameter and refractive index of the collagen fibers were 3 μm and 1.35, respectively. The spherical and cylindrical Mie scattering terms were chosen over the commonly-used approximations μs=Aλ−b because they are explicit functions of scatterer size. The absorption coefficient μa was modeled as a linear combination of the extinction coefficients of oxy- and deoxy-hemoglobin, weighted according to their concentrations in the tissue:

μa(λ)=[Hb]εHb+[HbO2]εHbO2.  (2)

G. Zonios, et al., “Diffuse reflectance spectroscopy of adenomatous colon polyps in vivo,” Appl. Opt. 38, 6628-6637 (1999).

3.2 Modeling Key Features in Reflectance Spectra of Pancreatitis and Adenocarcinoma
The key diagnostic feature of the measured reflectance was increased amplitude between 455 nm and 525 nm in the adenocarcinoma spectra, relative to normal pancreatic tissue spectra. An empirical model, previously shown to be accurate in the case of small source-detector separations, was used to model this feature by describing the reflectance spectra REMPi(λ) as functions of tissue absorption and scattering:




  
    
      
        
          
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R. Reif, et al., “Analysis of changes in reflectance measurements on biological tissues subjected to different probe pressures,” J. Biomed. Opt. 13, 010502 (2008); R. Reif, et al., “Analytical model of light reflectance for extraction of the optical properties in small volumes of turbid media,” Appl. Opt. 46, 7317-7328 (2007).

In Eq. (3), μs′(λ) is the reduced scattering coefficient, equal to μs(1−g), where g is the anisotropy of the tissue (set to 0.9 for all λ). The factor Ccorr(λ) describes the confinement of oxy- and deoxy-hemoglobin to cylindrical blood vessels. R. L. P. van Veen et al., “Diffuse-reflectance spectroscopy from 500 to 1060 nm by correction of inhomogeneously distributed absorbers,” Opt. Lett. 27, 246-248 (2002). The parameters a, b, and c are fitting constants (related to probe design) whose respective values were estimated to be 0.11, 0.22, and 0.2. These values do not vary significantly when the tissue-probe refractive index mismatch is changed. The value of b is somewhat dependent on probe source-detector separation, but changing b by as much as 50% was found to have very little effect on the modeled pancreatic tissue spectra. Furthermore, ratios of RiEMP values from tissue models with several different scattering coefficients of biological relevance were found to change by only about 5% when the value of b was increased by nearly 200%. Since the reflectance model (Eq. (4)) employed in this study only utilizes ratios (and not raw values) of RiEMP for different tissue types, it was considered reasonable to approximate a, b, and c as 0.11, 0.22, and 0.2 in Eq. (3). For the remainder of the text, the subscript i in Eq. (3) will be denoted as N for normal pancreatic tissue, P for pancreatitis, or A for pancreatic adenocarcinoma.
To model the reflectance spectra of diseased pancreatic tissue, Eq. (3) was used to generate a wavelength-resolved scaling factor to transform the experimentally measured reflectance spectrum RMEASN(λ) of normal pancreatic tissue into an accurate model for the adenocarcinoma reflectance spectrum RMODELA(λ) and the pancreatitis reflectance spectrum RMODELP(λ), according to the equations:

RAMODEL(λ)=RNMEAS(λ)(RAEMP(λ)/RNEMP(λ));  (4)

RPMODEL(λ)=RNMEAS(λ)(RPEMP(λ)/RNEMP(λ)).  (5)

Representative fits of the mathematical model to experimentally measured reflectance data for adenocarcinoma and pancreatitis are shown in FIGS. 1A and 1B. The purpose of the model was not to obtain an exact fit to all points on the measured reflectance curves, but rather, to explain key features of these spectra in terms of quantitative changes to biomedically relevant tissue properties. In the diagnostically important wavelength range between about 455 and about 525 nm, where the adenocarcinoma reflectance spectra differed significantly from both the normal and pancreatitis spectra, the error in fit between the adenocarcinoma model and measured adenocarcinoma data was less than 5%.
FIGS. 1A and 1B illustrates an aspect of an embodiment of the invention and provides a representative fit of a mathematical model 12 (dotted lines) versus average measured result for reflectance spectra (solid lines) of pancreatic adenocarcinoma (FIG. 1A) 14 and pancreatitis (FIG. 1B) 16, with residuals (small dotted lines) 18. The experimentally obtained reflectance spectrum for normal pancreatic tissue (dashed lines) 20 is shown on both plots for comparison. Relative to normal pancreatic tissue, adenocarcinoma was modeled to exhibit a 1.85× increase in the diameter of cell nuclei, and a 3× increase in collagen concentration; the pancreatitis was modeled to also have a 3× increase in collagen concentration, but no change in the size of cell nuclei.
The fits shown in FIGS. 1A and 1B between the predicted and measured reflectance spectra were obtained when the cancer cell nuclei were modeled to be about 1.85 times the size of normal nuclei and the collagen fibers were modeled to be about three times as abundant in both pancreatitis and cancer. These choices of parameters were in good agreement with results from histology and previous literature. M. Chandra, et al., “Probing pancreatic disease using tissue optical spectroscopy,” J. Biomed. Opt. 12, 060501 (2007); M. Chandra, et al., “Pancreatic tissue assessment using fluorescence and diffuse reflectance spectroscopy,” Proc. SPIE 6628, 66281R (2007), 8 pgs; T. Imamura, et al., “Quantitative analysis of collagen and collagen subtypes I, III, and V in human pancreatic cancer, tumor-associated chronic pancreatitis, and alcoholic chronic pancreatitis,” Pancreas 11, 357-364 (1995). The model revealed that differences in the reflectance spectra of normal pancreatic tissue, pancreatitis, and adenocarcinoma were largely due to an increase in nuclear size for adenocarcinoma relative to pancreatitis and normal tissue.
The spectra from FIGS. 1A and 1B were also compared with a previously published reflectance spectrum taken in vivo from a pancreatic adenocarcinoma xenograft created by injecting human pancreatic cancer cells into the pancreas of a Non-Obese Diabetic/Severe Combined Immunodeficiency (NOD/SCID) mouse. Due to the suppressed immune response in SCID mice, the xenograft had a very low amount of collagen relative to cells. The xenograft also contained more blood than the ex vivo-obtained human pancreatic tissue samples. In spite of these differences, the reflectance spectrum of the xenograft was similar to that of freshly excised human adenocarcinoma from 400-475 nm, a result attributed to the increased size of the cell nuclei in both the xenograft and the ex vivo-obtained adenocarcinoma tissue samples.
3.3 Extracting Scattering and Absorption Coefficients from Reflectance Data
Model fits to experimental data were employed to extract wavelength-resolved absorption and scattering coefficients for each tissue type via Eqs. (1) and (2) and the formula for Mie scattering from cylinders. The results shown in FIGS. 2A and 2B represent a measurement of absorption and scattering coefficients of human pancreatic tissues.
As shown in FIGS. 2A and 2B, wavelength-resolved scattering (FIG. 2A) and absorption (FIG. 2B) coefficients of normal pancreatic tissue (solid lines) 120, pancreatic adenocarcinoma (dashed lines) 116, and pancreatitis (dotted lines) 114, extracted from representative fits of experimental data to the reflectance model. The difference in shape of the adenocarcinoma scattering coefficient can be attributed to the modeling of the adenocarcinoma cell nuclei as being about 1.85 times larger in diameter than those in normal pancreatic tissues and pancreatitis.
The values of the coefficients in FIGS. 2A and 2B are in the range expected for gastrointestinal tissue. K. Vishwanath and M.-A. Mycek, “Do fluorescence decays remitted from tissues accurately reflect intrinsic fluorophore lifetimes?” Opt. Lett. 29, 1512-1514 (2004). The error bars on the scattering coefficients were determined by estimating the uncertainty (±5%) in obtaining Ns from histology. In addition, an error of up to about 20% in estimation of length scales from histology slides has been shown to result from the shrinking of fixed tissue samples. C. H. Fox, et al., “Formaldehyde Fixation,” J. Histochemistry and Cytochemistry 33, 845-853 (1985). However, it is assumed that this error can be neglected because it is within the distribution of nuclear sizes observed on the histology slide from normal pancreatic tissue. The uncertainty in extracting the absorption coefficients was calculated by using the models to estimate a range of μa values that could produce accurate reflectance fits over the entire spectrum and also correct the fluorescence spectra without noticeable artifacts (see herein below), and was found to be ±12.5% for normal, ±8.3% for pancreatitis, and ±14.3% for adenocarcinoma. Since the absorption coefficient can yield quantitative information about the blood content of the tissue, the mathematical model has the potential to be useful for in vivo studies, in which the presence of blood will likely be more significant.
4. Extracting and Modeling Intrinsic Fluorescence: Theory and Results
4.1 Correcting Fluorescence Data for Scattering- and Absorption-Related Artifacts
Once the fits of the reflectance model to the adenocarcinoma and pancreatitis data were obtained, the extracted wavelength-resolved absorption and scattering coefficients μa(λ) and μs(λ) (FIGS. 2A and 2B) were then used to remove artifacts of scattering and absorption from the measured fluorescence spectra of normal, pancreatitis and adenocarcinoma tissue. To perform this task, a separate Beer-Lambert attenuation factor was constructed for each tissue type by using the extracted μa(λ) and μs(λ) values specific to that tissue type. The intrinsic fluorescence spectrum FINTRINSIC(λ) was then extracted according to the equation:

FINTRINSIC(λ)=FMEAS(λ)exp([μa(λ)+μ′s(λ)]z).  (6)

The variable z represents the average depth that photons will travel in the tissue, and it was set to 0.1 cm for all tissue types. To obtain this value, time-resolved Monte Carlo simulations were run for pancreatic tissue models whose absorption and scattering coefficients were representative of those shown in FIGS. 2A and 2B. The average depth of photon travel was determined by finding the time at which the greatest number of simulated photons exited the tissue, multiplying that by the speed of light in the medium, and dividing by two to account for the photons' travel back to the surface once they reached their point of greatest depth in the tissue.
4.2 Fitting Intrinsic Fluorescence to Endogenous Fluorophore Component Spectra
Once the intrinsic fluorescence spectra were obtained for each tissue type, their lineshapes could be decomposed into the component spectra of collagen, NADH, and FAD, three principal contributors to tissue autofluorescence in the 400-700 nm wavelength range. For each tissue type, the intrinsic fluorescence spectrum was fit to a linear combination (BasisFit(λ)) of experimentally measured basis spectra of collagen, NADH, and FAD:

BasisFit(λ)=CCOLLAGENFCOLLAGEN(λ)+CNADHFNADH(λ)+CFADFFAD(λ).  (7)

To fit the intrinsic fluorescence spectra (FIGS. 3A, 3B and 3C) to Eq. (7), each of the basis spectra (FCOLLAGEN(λ), FNADH(λ), and FFAD(λ)) was blue-shifted by about 12 nm, which accounted for the fact that the component spectra were measured in various chemical solvents and not within a biological tissue environment.
FIGS. 3A, 3B and 3C illustrate intrinsic fluorescence spectra (solid lines) of normal pancreatic tissue 220 (FIG. 3A), pancreatitis (FIG. 3B) 214, and pancreatic adenocarcinoma 216 (FIG. 3C), shown with a representative fit to a linear combination 222 (dotted lines) with residuals 218 (small dotted line) of measured and blue-shifted collagen, NADH, and FAD basis spectra.
The deviation of the basis fits to the intrinsic fluorescence spectra of normal pancreatic tissue and pancreatitis around 600 nm may be attributed to the fact that the model does not include porphyrin fluorescence, which is known to peak around 635 nm when excited with 380-440 nm light. P. Hillemanns, et al., “Lymph node metastasis detection of ovarian cancer by porphyrin fluorescence photodetection: case report,” Lasers Med. Sci. 22, 131-135 (2007).
The purpose of the fluorescence model was not to obtain an exact fit to every point on the intrinsic fluorescence spectra, but rather to interpret key features of the fluorescence from normal pancreatic tissue, pancreatitis, and pancreatic adenocarcinoma. Since the spectra were normalized to the peak, the intrinsic fluorescence between 500 and 550 nm (where intracellular NADH and FAD emit prominently) is expected to decrease in pancreatitis and adenocarcinoma, where there is increased extracellular stromal collagen content. In this diagnostically-relevant region, the error in fit between Eq. (7) and the intrinsic fluorescence was less than 4% for normal pancreatic tissue, less than 3% for pancreatitis, and less than 6% for adenocarcinoma.
The data from FIGS. 3A, 3B and 3C shows that the mathematical model of intrinsic fluorescence agreed well with histology of tissue samples from patients involved in the study and described in Table 1. The values of the collagen fit coefficients CCOLLAGEN (Table 2) correlated well with the amount of collagen incursion amidst the cells in the tissue samples examined via histology.







TABLE 2







Fit coefficients Ci (percentage contributions) for collagen, NADH, and


FAD basis spectra to intrinsic fluorescence spectra of normal pancreatic


tissue, pancreatitis, and pancreatic adenocarcinoma.











Normal
Pancreatitis
Adenocarcinoma














CCOLLAGEN
0.35 (25%)
0.60 (41%)
 0.97 (87%)


CNADH
0.75 (54%)
0.50 (35%)
0.05 (4%)


CFAD
0.30 (21%)
0.35 (24%)
0.10 (9%)









The data in FIGS. 3A, 3B and 3C were also compared to the intrinsic fluorescence extracted from a fluorescence spectrum obtained in vivo from a pancreatic adenocarcinoma xenograft in a NOD/SCID mouse. M. Chandra, et al., “Probing pancreatic disease using tissue optical spectroscopy,” J. Biomed. Opt. 12, 060501 (2007). Mathematical modeling showed that the xenograft fluorescence could be mostly attributed to intracellular components, a conclusion that made sense given that the xenograft tumor was predominantly comprised of cells.
5. Discussion and Conclusions
5.1 Overview of Mathematical Models Developed
In this illustration of embodiments of the invention, mathematical models of reflectance and intrinsic fluorescence were developed and employed to quantitatively describe the effects of key histologically-observed tissue parameters on the measured optical spectra of pancreatitis and pancreatic adenocarcinoma (relative to normal pancreatic tissue). An empirical mathematical model of reflectance was able to fit the prominent feature in the adenocarcinoma spectrum (increased amplitude from about 455 to about 525 nm, relative to normal pancreatic tissue) with less than 5% error. Fitting the reflectance model to the measured optical spectra enabled the extraction of wavelength-resolved absorption and scattering coefficients of human pancreatic tissues. Obtaining values for the optical coefficients is an important result, because knowledge of these coefficients is essential for accurate computational studies of photon migration in pancreatic tissue models. For example, one such computational method is Monte Carlo simulation, which is accurate throughout optical parameter space for modeling photon transport in biological tissue. L. Wang, et al., “MCML—Monte Carlo modeling of light transport in multi-layered tissues,” Computer Methods and Programs in Biomedicine 47, 131-146 (1995); K. Vishwanath and M.-A. Mycek, “Time-resolved photon migration in bi-layered tissue models,” Opt. Expr. 13, 7466-7482 (2005).
The optical coefficients extracted from the reflectance fits were used to correct the measured fluorescence spectra by removing tissue absorption and scattering artifacts. The resulting “intrinsic” endogenous fluorescence spectra were fit to a linear combination of basis spectra from native tissue fluorophores (collagen, NADH, FAD) to obtain the relative contributions from both extracellular (collagen, about 400 to about 450 nm emission peak) and intracellular (NADH and FAD, about 500 nm to about 600 nm emission peak) autofluorescence for each tissue type. The relative contribution of collagen was found to be greater in the intrinsic fluorescence spectra of pancreatitis and adenocarcinoma. Since the spectra were normalized to the peak, the pancreatitis and adenocarcinoma spectra exhibited a decrease in amplitude in the about 500 to about 550 nm range, where NADH and FAD emission are prominent. These results were consistent with the increased collagen fibrosis seen in histology of pancreatitis and adenocarcinoma. T. Imamura, et al., “Quantitative analysis of collagen and collagen subtypes I, III, and V in human pancreatic cancer, tumor-associated chronic pancreatitis, and alcoholic chronic pancreatitis,” Pancreas 11, 357-364 (1995); J. Köninger, et al., “Overexpressed Decorin in Pancreatic Cancer: Potential Tumor Growth Inhibition and Attenuation of Chemotherapeutic Action,” Clin. Cancer Res. 10, 4776-4783 (2004).
5.2 Correlation of Optical Tissue Models With Histology
As seen in FIGS. 1A and 1B, FIGS. 3A, 3B and 3C, and Table 3, empirical models of reflectance and intrinsic fluorescence were able to quantitatively describe the differences between normal pancreatic tissue, adenocarcinoma, and pancreatitis in terms of histologically observed changes in biologically meaningful parameters. The reflectance spectra of cancerous tissue differed most noticeably from normal pancreatic tissue at around 500 nm, a change that could be quantitatively linked, via spherical Mie scattering, to larger cell nuclei in pancreatic adenocarcinoma. Subtle differences in the reflectance spectra at around 400 nm to about 425 nm and about 450 nm to about 550 nm were also found, via modeling of cylindrical Mie scattering, to correlate with the increased number of collagen fibers in both pancreatitis and cancer. These results agree with histology in that both pancreatitis and pancreatic adenocarcinoma are marked by greater collagen content than normal pancreatic tissue, but only adenocarcinoma is characterized by larger cell nuclei. R. H. Hruban, et al., “Pancreatic Intraepithelial Neoplasia: A New Nomenclature and Classification System for Pancreatic Duct Lesions,” Am. J. Surg. Path. 25, 579-86 (2001); R. H. Hruban, et al., “An Illustrated Consensus on the Classification of Pancreatic Intraepithelial Neoplasia and Intraductal Papillary Mucinous Neoplasms,” Am. J. Surg. Path. 28, 977-87 (2004).







TABLE 3







Prominent disease-related changes in histology features and measured optical spectra of pancreatic tissues,


along with corresponding changes made to mathematical models of reflectance and fluorescence.









Pancreatic tissue
Adenocarcinoma
Pancreatitis





Key histological features
Increased nuclear size
Greater stromal collagen content


(relative to normal pancreatic tissue)
Greater stromal collagen content


Optical signature
Increased amplitude of reflectance
Spectral lineshape change in intrinsic


(relative to normal pancreatic tissue)
spectrum from 455-525 nm
fluorescence spectrum from 500-550 nm



Spectral lineshape change in intrinsic



fluorescence spectrum from 500-550 nm


Mathematically modeled by
Multiplying spherical scatterer diameter L
Increasing coefficient CCOLL of collagen



by 1.85 for reflectance model
basis spectrum from 0.35 (25%) to 0.60



Increasing coefficient CCOLL of collagen basis
(41%) in fit to



spectrum from 0.35 (25%) to 0.97 (87%) in
intrinsic fluorescence



fit to intrinsic fluorescence


Error in fit of model to experimentally
Less than 5% from 455-525 nm for
Less than 6% from 500-550 nm for


measured data in diagnostically
adenocarcinoma reflectance model
normal, pancreatitis, and


relevant region
Less than 6% from 500-550 nm for normal,
adenocarcinoma



pancreatitis, and adenocarcinoma intrinsic
intrinsic fluorescence models



fluorescence models









The intrinsic fluorescence model showed that for both pancreatitis and adenocarcinoma, there was an increased contribution from the collagen in the stroma, relative to normal pancreatic tissues. This result is consistent with the histological observation that the change from normal pancreatic tissue to both pancreatitis and adenocarcinoma is characterized by increased collagen amidst the cells. However, the intrinsic fluorescence spectra of pancreatitis and cancer were also shown to be different from each other. Whereas the reflectance model was most useful for discriminating pancreatic adenocarcinoma from pancreatitis, the intrinsic fluorescence model was more effective at distinguishing between all three tissue types. In any case, the use of the reflectance data to extract the intrinsic fluorescence lends credence to the idea that combining reflectance and fluorescence spectroscopy has a diagnostic advantage over using just one of these modalities to detect pancreatic cancer.
5.3 Comparison of Empirical Reflectance Model with Diffusion Approximation
The empirical reflectance model was compared with the diffusion approximation, which is often employed to extract tissue absorption and scattering properties from experimentally measured tissue reflectance spectra. G. Zonios, et al., “Comparative evaluation of two simple diffuse reflectance models for biological tissue applications,” Appl. Opt. 47, 4965-4973 (2008). When the values for the diameter L and number density Ns of the nuclei were used as inputs, the diffusion approximation model was noticeably less effective than the empirical model for fitting the adenocarcinoma reflectance spectrum. In the diagnostically-relevant wavelength range of 455-525 nm, the error in fit to the measured adenocarcinoma spectrum was less than 5% for the empirical model, but it rose to as high as 17% with the diffusion approximation model. Between 600 and 700 nm, the mean error in fit to the measured adenocarcinoma spectrum was less than 2% for the empirical model, but it was greater than 16% with the diffusion approximation model. Similar results were found when the values of L and Ns were changed by about 10%. These results were not surprising because the fiber-optic probe in this study had a source-detector separation of only about 660 μm. Using the scattering coefficients μs from FIGS. 1A and 1B and a value of 0.9 for the tissue anisotropy g, it can be shown that the source-detector separation of the probe was often smaller than 1/μs(1−g). This condition causes the diffusion approximation to break down [30], but the empirical model is accurate in this regime. R. Reif, et al., “Analytical model of light reflectance for extraction of the optical properties in small volumes of turbid media,” Appl. Opt. 46, 7317-7328 (2007).
5.4 Potential of Optical Spectroscopy to Fulfill Unmet Clinical Need
Current methods to detect pancreatic adenocarcinoma are highly invasive and fail to find the disease early or to distinguish it from inflammation (pancreatitis). Hence, there is great biomedical need for an endoscopic screening procedure for early detection of pancreatic adenocarcinoma. Bimodal reflectance and fluorescence spectroscopy is a potential inroad into addressing this unmet clinical need. In this study, mathematical models of measured reflectance and fluorescence spectra were employed to quantitatively describe differences between normal pancreatic tissue, pancreatic adenocarcinoma, and pancreatitis. By using biomedically relevant parameters, the model provided a link between the results of optical spectroscopy and histology. Features in the reflectance spectra were quantitatively linked to larger cell nuclei in cancer and increased collagen content in both cancer and pancreatitis. The intrinsic fluorescence spectra were fit to a linear combination of collagen, NADH, and FAD basis spectra to show quantitative differences in the contribution of collagen to the measured fluorescence from normal pancreatic tissue, pancreatic adenocarcinoma, and pancreatitis.
Translation to an in vivo setting is feasible because the model can extract the optical absorption coefficient from increased blood content in the tissues. Challenges associated with obtaining an accurate reflectance fit near 425 and 550 nm (where hemoglobin absorption is noticeable) can be resolved by fitting each individual reflectance spectrum to an empirical equation, a photon migration model, or the P3 approximation. M. Muller, et al., “Intrinsic fluorescence spectroscopy in turbid media: disentangling effects of scattering and absorption,” Appl. Opt. 40, 4633-4646 (2001); G. M. Palmer and N. Ramanujam, “Monte-Carlo-based model for the extraction of intrinsic fluorescence from turbid media,” J. Biomed. Opt. 13, 024017 (2008); J. C. Finlay and T. H. Foster, “Hemoglobin oxygen saturations in phantoms and in vivo from measurements of steady-state diffuse reflectance at a single, short source-detector separation,” Med. Phys. 31, 1949-1959 (2004). Another test of the model could involve comparing the intrinsic fluorescence extracted via a Beer-Lambert factor (Eq. (6)) with that obtained with a more detailed photon migration model.
5.5 Illustration 1 Conclusions
The mathematical models of reflectance and fluorescence as discussed herein are useful tools for pancreatic cancer diagnostics because of their ability to quantitatively link the experimental results of optical spectroscopy with those of histopathology. The mathematical model of reflectance is able to quantitatively describe the reflectance spectra of normal pancreatic tissue, pancreatitis, and pancreatic adenocarcinoma in terms of biomedically relevant parameters. The algorithm to model the reflectance was rapid, taking only several seconds to execute. Furthermore, the concept of scaling an average measured normal pancreatic tissue reflectance spectrum to obtain the pancreatitis and adenocarcinoma spectra was found to be helpful with data interpretation due to its intuitive nature. FIGS. 2A and 2B show, among other things, the capability of the reflectance model to extract absorption and scattering coefficients of the aforementioned human pancreatic tissue types. When the tissue absorption and scattering coefficients were used to correct the fluorescence for attenuation artifacts (as shown in FIGS. 3A, 3B and 3C), the resulting intrinsic fluorescence spectra revealed differences in collagen content that correlated with histology as shown in Table 2. The, rapid, intuitive, and biomedically relevant nature of these mathematical models suggests that the data analysis procedure outlined herein may be of potential use not only for pancreatic cancer detection, but also for other optical diagnostic applications involving a wider range of biological tissues.
Illustration II
1. Introduction
In other embodiments, four tissue classification algorithms were developed to employ reflectance and fluorescence spectroscopy for differentiating between human pancreatic adenocarcinoma and pancreatitis tissue.
The first approach employed the ratio of measured reflectance at 470 nm to that at 650 nm and wavelength integrated fluorescence intensity (i.e., area under the curve) for tissue classification (i.e., SpARC—Spectral areas and ratios classifier), among other things.
The second was a chemometric approach that employed Principal Component Analysis (PCA) and Linear Discriminant analysis (LDA). In some embodiments, PCA was used to identify the diagnostic features in the spectra and then LDA was employed to classify the data based on these features.
The third method employed a photon-tissue interaction (PTI) model of photon transport in pancreatic tissue, previously developed by the inventors. R. H. Wilson, et al., “Optical spectroscopy detects histological hallmarks of pancreatic cancer,” Optics Express (submitted), (2009). The PTI model has been shown to be a relatively accurate means of quantitatively describing key changes in the reflectance and fluorescence spectra of adenocarcinoma and pancreatitis (relative to normal pancreatic tissue). In some embodiments, the quantitative parameters extracted from the model were used to classify tissue using LDA.
The fourth approach was a hybrid model that employed a combination of the chemometric, PTI model and SpARC algorithms.
For purposes of illustrating embodiments of the invention, further exemplary systems and methods of the invention are discussed herein below.
2. Methods
2.1 Human Studies
Reflectance and fluorescence spectra were measured from freshly excised pancreatic tissue obtained during Whipple procedures. Multiple sites were measured on tissues obtained from 12 patients within 30 minutes of excision. A total of 90 sites were measured from all the patients and two measurements were made on each site. After data acquisition from each measurement site, a portion of tissue was removed to link optical measurements with histological analysis.
2.2 Instrumentation
As in the prior example, a clinically compatible, fiber-optic coupled Reflectance and Fluorescence Lifetime Spectrometer (RFLS) was employed for data acquisition. M. Chandra, et al., “Probing pancreatic disease using tissue optical spectroscopy,” J Biomed Opt 12, 060501 (2007); M. Chandra, et al., “Quantitative molecular sensing in biological tissues: an approach to non-invasive optical characterization,” Optics Express 14, 6157-6171 (2006).
FIG. 5 provides a schematic of a system 10 constructed in accordance with some embodiments of the invention. It should be understood that system 10 components may be separate and in wired or wireless communication with one another. Alternatively, all or most of the components of system 10 may be combined as one instrument. System 10 includes a fluorescence excitation source 26 and a reflectance source 28 which are optically coupled with a probe 30 via independent fibers 32 and 34, respectively, for the communication of light therein. Fluorescence excitation source 26 may be, for example, a pulsed solid state diode laser emitting at 355 nm (e.g., PNV001525-140, JDS Uniphase, San Jose, Calif.) or like device, and reflectance source 28 may be, for example, a tungsten halogen lamp (e.g., HL 2000FHSA, Ocean Optics, Dunedin, Fla.), with a range of 360-2000 nm emission, or like device. Light delivered by fibers 32 and 34 is directed by probe 30 onto tissue 36 to produce one or more measurable spectroscopic events, which in this embodiment includes reflectance and/or emitted fluorescence photons. Reflectance and fluorescence signals or photons from tissue 36 are delivered via a third fiber 38 disposed in probe 30 and in communication with one or more devices configured for assessing a spectroscopic event by providing measurements of the spectroscopic response associated with tissue 36, which may include a variety of measurements derived from the fluorescence and reflectance signals. In this embodiment, fiber 38 is in communication with a spectrograph 40 (e.g., MS 125, Oriel Instruments, Stratford, Conn.) or like device for measuring the properties of light and an avalanche photodiode 42 or other photodetector capable of time-resolved spectroscopic measurements, such as time-resolved fluorescence decay. Alternatively, other methods and devices may be employed for collecting spectroscopic data, such as the time-resolved fluorescence decay, as a function of time. In this embodiment, spectrograph 40 includes or is coupled with an intensified charge coupled device (ICCD) camera (e.g., ICCD 2063, Andor Technology, Belfast, Northern Ireland) or like device. In other embodiments, a spectrograph without an ICCD camera may be employed. It should be understood that additional devices for assessing the spectroscopic event and measuring the spectroscopic response may be included in some embodiments. As shown in FIG. 5, light from fiber 38 is divided so that a first portion of the detected photons is directed to spectrograph 40 and a second portion is directed to avalanche photodiode 42. In some embodiments, the division of photons from fiber 38 is facilitated by a beamsplitter or filter, such as a neutral density filter.
Fibers 32, 34 and 36 may comprise 600 μm core optical fibers, although other sized fibers may be used. Alternatively, another material capable of delivering light as described above may be employed. System 10 further includes a data processing system 44 which is in communication with spectrograph 40 and avalanche photodiode 42 for receiving and analyzing the response data in accordance with the methods of the invention, which may include a variety of measurements derived from fluorescence and reflectance signals, such as fluorescence spectra, reflectance spectra or other measurements as described above. For example, the data processing system 44 may be configured to compare the response data with the preset criteria, determine whether any preset criteria relating to a condition of tissue 36 is satisfied based on the response data, and classify tissue 36 accordingly based on the preset criteria satisfied. Data processing system 44 may also include a display for presenting the results of the analysis, which may be particularly useful when using system 10 during a biopsy or endoscopic procedure.
FIG. 6 illustrates an embodiment of probe 30 according the invention in which fibers 32, 34 and 36 are disposed adjacently in a substantially triangular cross sectional arrangement at distal end 44 of probe 30. It should be readily apparent that more or less fibers in a variety of arrangements may be employed in probe 30. For example, one fiber may be used or a ring of four or more fibers maybe fitted in probe 30. In operation, fluorescence and reflectance excitation of tissue 36 and measurements can be obtained using probe 30 by sequentially blocking light from fiber 32 and 34 using shutters (not shown) or some other apparatus for alternatively covering one fiber at a time while leaving fiber 36 exposed for the detection of tissue fluorescence and reflectance.
Those skilled in the art will readily appreciate that methods and systems of the embodiments of the invention, such as system 10, may include various other elements, such as electrical or optical components, lasers, lamps, transient digitizers, oscilloscopes, connectors, connector blocks, relays, pulse conditioners, generators, etc., computer and network related software and hardware, such as programs, operating systems, memory storage devices, input/output devices, processors, servers, data communication links, whether wireless or otherwise, and data transceiving devices. Those skilled in the art will further appreciate that it is within the scope of the invention to include such additional elements and identifying precise types of components is not vital to the full implementation of the systems and methods of the invention.
The acquired fluorescence spectra were corrected for spectral instrument response after background correction. The reflectance spectra were also background subtracted and then scaled by the lamp reflectance spectrum (Ro) to obtain the corrected reflectance spectra (R/Ro). All spectra were normalized by scaling the peak intensity value to unity.
2.3 Pathology and Inclusion Criterion
Pathology indicated that of the measured sites 17 were adenocarcinoma sites, 22 were pancreatitis sites, and 11 were normal tissue sites. The rest of the sites were either malignant breast cancer that had metastasized to the pancreas (10 sites), intraductal papillary mucinous neoplasm (IPMN—8 sites), pancreatic intraepithelial neoplasia (PanIN—6 sites), serous cyst adenoma (SCA—8 sites), scar or fat tissue or both (5 sites) or a hybrid tissue site having two or more of the above mentioned pathologies (3 sites). These sites were excluded from the data set used for algorithm development. Furthermore, those measurements that were very noisy were also excluded from the data set (4 measurements).
This left a total of 33 adenocarcinoma measurements, 40 pancreatitis measurements, and 22 normal measurements of both fluorescence and reflectance spectra. Tissue algorithm development was undertaken with this set of data of 95 total spectra (Set 1) of both fluorescence and reflectance spectra.
A second set (Set 2) of data was analyzed separately in which six of the pancreatitis sites from patient 10 were also excluded owing to some discrepancy with pathology results. Thus Set 2 comprised of 33 adenocarcinoma measurements, 31 pancreatitis measurements, and 22 normal measurements (86 total spectra) of both fluorescence and reflectance spectra each.
Table 4 shown below indicates the pathology of the measured sites for each patient. The sites with asterisks were included in algorithm development as indicated below while sites without asterisks were excluded.







TABLE 4







Patient-wise histology of the sites from which data was collected

























Scar/












fat/
P and


Patient
N
P
A
PanIN
IPMN
SCA
MBC
both
Aa
N and Ab




















1
2*
3*
—
—
—
—
—
—
—
—


2
—
—
5*
—
—
—
—
—
—
—


3
—
5*
—
1
—
—
—
1
1


4
—
3*
—
—
1
—
—
1
—
—


5
—
—
—
—
—
8
—
—
—
—


6
4*
—
—
1
—
—
—
—
—
—


7
—
—
—
—
7
—
—
1
—
—


8
—
4*
1*
1
—
—
—
—
1
1


9
5*
1*
1*
3
—
—
—
—
—
—


10
—
6**
—
—
—
—
—
2
—
—


11
—
—
10* 
—
—
—
—
—
—
—


12
—
—
—
—
—
—
10
—
—
—





N: Normal;


P: Pancreatitis;


A: Adenocarcinoma;


PanIN: Pancreatic Intraepithelial Neoplasia;


SCA: Serous Cyst Adenoma;


MBC: metastatic breast carcinoma case


aA hybrid tissue site of chronic pancreatitis and adenocarcinoma;


bA hybrid tissue site of focal adenocarcinoma at the edge of mostly normal tissue


*These sites were included in the data.


**These sites were both included and excluded from the data for algorithm development






2.4 Leave-One-Out Cross-Validation
A leave-one-out cross-validation was undertaken to test the performance of each of the proposed tissue classification algorithms. For each algorithm, the data were divided into Training and Test data where each spectrum was considered as Test data one at a time, while the remaining spectra were treated as Training data. Thus, each algorithm was implemented 95 times for Set 1 and 86 times for Set 2. Data sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated.
2.5 Spectral Areas and Ratios Classifier (SpARC) Algorithm
Preliminary examination of the fluorescence and reflectance spectra from a pilot study conducted by us had suggested the use of the ratio of measured Reflectance at 470 nm to that at 650 nm (Rratio=R470/R650) and the wavelength integrated fluorescence (area under the curve) as possible tissue classifiers.
The ratio Rratio=R470/R650 was calculated for each measured reflectance spectrum and the wavelength integrated fluorescence (Farea) was calculated for each of the fluorescence spectra. The test data was classified in a two-step procedure where Rratio was first employed to identify adenocarcinoma using LDA on the training data. If the tissue was classified as not adenocarcinoma then Rratio and Farea were both employed to classify the test data as either pancreatitis or normal using LDA. The process was repeated for each of the spectra in data Set 1 and data Set 2 (as discussed in the section above regarding “leave-one-out cross-validation”).
For purposes of illustrating some embodiments of the invention, exemplary chemometric analysis of the spectra is discussed in further detail below.
2.6 Chemometric Analysis of the Spectra
2.6.1 Principal Component Analysis (PCA)
PCA was employed to express each spectrum as a linear combination of a set of orthogonal basis vectors (or components). A. D. Joshi, et al., “Improving PET receptor binding estimates from Logan plots using principal component analysis,” J Cerebr Blood F Met 28, 852-865 (2008). Of these components, the key-features of the data are captured by only a few vectors with high eigenvalues while the vectors corresponding to lower eigenvalues represent noise in the data. However, not all the key features of the spectra are diagnostically relevant (i.e., enabling differentiation between the tissue types). By identifying the few diagnostically relevant components, the dimensionality of the problem is reduced. The diagnostically relevant components are identified by fitting the principal components to the spectra. The components whose coefficients showed greatest difference between tissue types were identified as diagnostically relevant.
The training data spectra siεRn×1 (i=1 to m) for different tissue types were grouped together and arranged row-wise in a matrix SεRm×n as shown below:




  
    
      
        
          S
          =
          
            
              [
              
                
                  
                    
                      
                        s
                        _
                      
                      1
                      T
                    
                  
                
                
                  
                    
                      
                        s
                        _
                      
                      2
                      T
                    
                  
                
                
                  
                    ⋮
                  
                
                
                  
                    
                      
                        s
                        _
                      
                      m
                      T
                    
                  
                
              
              ]
            
            =
            
              [
              
                
                  
                    
                      
                        s
                        _
                      
                      1
                      T
                    
                  
                
                
                  
                    ⋮
                  
                
                
                  
                    
                      
                        s
                        _
                      
                      p
                      T
                    
                  
                
                
                  
                    ⋮
                  
                
                
                  
                    
                      
                        s
                        _
                      
                      
                        p
                        +
                        q
                      
                      T
                    
                  
                
                
                  
                    ⋮
                  
                
                
                  
                    
                      
                        s
                        _
                      
                      
                        p
                        +
                        q
                        +
                        r
                      
                      T
                    
                  
                
              
              ]
            
          
        
        ,
      
    
    
      
        (
        8
        )
      
    
  




where, p, q and r (p+q+r=m) are the number of spectra for adenocarcinoma, pancreatitis and normal tissue type respectively. Using PCA, the above shown n dimensional training set (corresponding to the n measured wavelengths) with m total spectra can be represented as a linear combination of n basis vectors as shown below:

ST=CX,  (9)

where, T is the transpose operator, CεRn×n is the matrix of the n principal components, and X is a matrix of the fit coefficients.





  
    
      
        
          X
          =
          
            [
            
              
                
                  
                    x
                    11
                  
                
                
                  
                    x
                    12
                  
                
                
                  ...
                
                
                  
                    
                  
                
                
                  
                    x
                    
                      1
                      ⁢
                      m
                    
                  
                
              
              
                
                  
                    x
                    21
                  
                
                
                  ⋱
                
                
                  
                    
                  
                
                
                  
                    
                  
                
                
                  ⋮
                
              
              
                
                  ⋮
                
                
                  
                    
                  
                
                
                  ⋱
                
                
                  
                    
                  
                
                
                  
                    
                  
                
              
              
                
                  
                    x
                    
                      n
                      ⁢
                      
                        
                      
                      ⁢
                      1
                    
                  
                
                
                  ...
                
                
                  ...
                
                
                  ...
                
                
                  
                    x
                    
                      n
                      ⁢
                      
                        
                      
                      ⁢
                      m
                    
                  
                
              
            
            ]
          
        
        ,
      
    
    
      
        (
        10
        )
      
    
  




Where, an element xji is the fit-coefficient of the jth component for the ith spectrum.

The principal component matrix C was obtained from S by employing the princomp function in MATLAB. The princomp function first calculates matrix S0 by subtracting the column mean vector from each row of S. Then singular value decomposition is used to calculate the principal components as the eigenvectors of the sample covariance matrix




  
    (
    
      
        1
        
          m
          -
          1
        
      
      ⁢
      
        S
        0
        T
      
      ⁢
      
        S
        0
      
    
    )
  
  .




The columns of X were estimated by fitting the principal components to the spectra using ordinary least squares. The estimated coefficients vectors for each spectrum (i.e. the columns of X) were then separated into three groups based on the tissue type. The principal components for which the coefficients were significantly different between the tissue types were determined based on pair-wise student's T-test (p<0.05). Six such t-tests were performed for the coefficients of each principal component j: The hypothesis tested was that the mean fit-coefficients were significantly different for the jth principal component of (1) Adenocarcinoma and the rest of the tissue types, (2) Normal and the rest of the tissue types, (3) Pancreatitis and the rest of the tissue types, (4) adenocarcinoma and Pancreatitis, (5) Adenocarcinoma and Normal, (6) Pancreatitis and Normal. These principal components were the diagnostically relevant components.
The above analysis was done for both fluorescence (n=492) and reflectance spectra (n=521) separately to determine the components whose coefficients would be used for classification of tissue types in the test data.
Thus, in some embodiments, the steps involved in the algorithm development include: obtaining fluorescence or reflectance spectra at n or a plurality of wavelengths; apply principal component analysis to calculate n or a plurality of components; T-test compares the fit coefficients of each of the n components for each tissue type, that is, tissue with adenocarcinoma, normal tissue or pancreatitis tissue and identifies which components are significant for classifying the tissue types.
2.6.2 Classification of Test Data Using PCA and LDA
Each fluorescence and reflectance spectrum of the test data were then fit to the principal components obtained above (C) and the coefficients of the components that were identified as being diagnostically relevant were used for classifying the test data by employing Linear discriminant analysis (LDA). Z. F. Ge, et al., “Identification of colonic dysplasia and neoplasia by diffuse reflectance spectroscopy and pattern recognition techniques,” Applied Spectroscopy 52, 833-839 (1998). The analysis was done by employing the coefficients of a varying number of diagnostically relevant components.
This analysis was repeated for each measured spectrum for leave-one-out cross-validation as described above.
2.7 Photon-Tissue Interaction (PTI) Model of Measured Reflectance and Fluorescence Spectra
As referenced above, a previously developed PTI model of photon transport in pancreatic tissue has been shown to be an accurate means of quantitatively describing key changes in the reflectance and fluorescence spectra of adenocarcinoma and pancreatitis (relative to normal pancreatic tissue). The PTI model acts as a link between optical spectra and microscopic examination of pancreatic tissues. When a measured reflectance spectrum is used as an input, the model extracts tissue absorption coefficients that are functions of tissue hemoglobin concentration and blood-oxygen saturation, as well as scattering coefficients that are explicit functions of the diameters of the cell nuclei in the tissue. Then, the model corrects the measured fluorescence spectra for attenuation due to tissue absorption and scattering and fits the resulting “intrinsic” fluorescence spectra to a linear combination of collagen, NADH, and FAD basis spectra.
The PTI model was found to accurately reproduce the key features of the adenocarcinoma reflectance spectrum relative to normal; the predictions of the model matched the average measured adenocarcinoma reflectance spectrum to better than 5% in the diagnostically-relevant wavelength range of 455-525 nm. This fit was obtained when the diameter of the cancer cell nuclei (L) was modeled to be 1.85 times that of the normal pancreatic cell nuclei, a figure that was consistent with the observations of histology. The fit of the linear combination of collagen (Ccoll), NADH (CNADH), and FAD (CFAD) basis spectra to the intrinsic fluorescence extracted from measured normal, pancreatitis, and adenocarcinoma fluorescence spectra was shown to have less than 6% error for all three tissue types in the diagnostically-relevant wavelength region of 500-550 nm, where NADH and FAD play a major role in fluorescence emission. In this region, the amplitude of the peak-normalized spectra is expected to decrease in adenocarcinoma and pancreatitis due to greater collagen content, and the extracted fit coefficient of the collagen basis spectra increased for pancreatitis and cancer, relative to normal tissue.
2.7.1 Protocol for Tissue Classification Based on the PTI Model
First, the reflectance fitting algorithm was executed such that the predicted spectrum fit the measured reflectance spectrum as accurately as possible in the diagnostically-relevant wavelength region of 455-525 nm. This fitting procedure was primarily carried out by changing the size L of the cell nuclei until an appropriate fit to the data was obtained. Then, the tissue hemoglobin concentration was adjusted in attempt to better reproduce the features of the measured spectrum from 400-425 nm and 525-600 nm. Once the reflectance fit was completed, the extracted absorption and scattering coefficients of the tissue were utilized to correct the measured fluorescence spectrum for attenuation-related artifacts. The resulting “intrinsic” fluorescence spectrum was then fit to a linear combination of collagen, NADH, and FAD (Ccoll, CNADH, and CFAD) basis spectra until good agreement with the measured intrinsic fluorescence spectrum over was obtained. If there were clear artifacts of over-correction or under-correction for attenuation in the intrinsic fluorescence spectrum, the reflectance fit was modified to extract a new set of tissue absorption and scattering coefficients that corrected the measured fluorescence spectrum more accurately.
Once the fits were completed, four diagnostically-relevant parameters were extracted: the nuclear size L and the fit coefficients Ccoll, CNADH, and CFAD for the fluorescence basis spectra of collagen, NADH, and FAD. Linear Discriminant Analysis was then performed on the data sets (Set 1 and Set 2) using leave-one-out cross-validation. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were calculated.
2.8 Hybrid Algorithm: Combination of the Chemometric, PTI Model, and SpARC Algorithms
It has been previously shown that a “hybrid” between a pure chemometric model (e.g. PCA) and a physical tissue model can produce increased diagnostic accuracy. Z. Volynskaya, et al., “Diagnosing breast cancer using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy,” J Biomed Opt 13, 024012 (2008). A hybrid algorithm employing the parameters extracted from the SpARC algorithm, the chemometric approach, and the physical photon-tissue interaction (PTI) model, was used to classify tissue spectra. LDA was employed to this multi-dimensional classification criterion to achieve tissue classification using a leave-one-out cross-validation. The data was divided into Training and Test data using a leave-one-out method. The classifiers corresponding to SpARC, Chemometric and PTI model algorithms were calculated for each of the Training set data and the Test data.
The SpARC algorithm as discussed above was employed to calculate the ratio of reflectance intensities at 470 nm to that at 650 nm (Rratio=R470/R650) for each reflectance spectrum and the wavelength integrated fluorescence intensity (Farea) for each fluorescence spectrum.
The Chemometric algorithm as discussed above was employed to identify the diagnostically relevant principal components (PC) of reflectance and fluorescence spectra in the training set for identifying adenocarcinoma from the rest (RPCA and FPCA) and for identifying between pancreatitis and normal tissue (RPCP-N and FPCP-N).
For each set of measurements of reflectance and fluorescence spectra, the PTI model algorithm as discussed above was employed to extract the parameters L/Lo, Ccoll, CNADH, and CFAD.
Once these parameters were extracted, Linear Discriminant Analysis was first employed to classify the Test data as either adenocarcinoma or not adenocarcinoma using all or a subset of L/Lo, Ccoll, CNADH+CFAD, Rratio, Farea, and fit-coefficients of RPCA and FPCA.
Then, if the data was classified as not cancer, LDA employed all or a subset of L/Lo, Ccoll, CNADH+CFAD, Rratio, Farea, and fit-coefficients of RPCP-N and FPCP-N to classify the Test data as either pancreatitis or normal tissue type.
This process was repeated in a leave-one-out cross-validation scheme to calculate sensitivity, specificity, PPV and NPV.
2.9 Principal Component Analysis (PCA) of Time-Resolved Fluorescence Data
PCA was employed as described above on the time-resolved fluorescence data acquired from adenocarcinoma, pancreatitis, and normal tissue. The acquired decay traces were smoothed and normalized and the principal components were calculated for the training data. The t-test on the fit-coefficients of each principal component indicated that pancreatitis and normal tissue can be discriminated by using the PCA of time-resolved data.
The analysis was done for distinguishing Pancreatitis from Normal tissue using a leave-one-out cross-validation on data Set 1 and data Set 2 excluding patient 11 data. Data from patient 1 to 10 were collected by measuring the entire spectrum. The data from patient 11 onwards was excluded as it was obtained with a long-pass filter (>500 nm) in front of the avalanche photodiode (thus capturing only a portion of the spectrum). The results are discussed herein below.
3. Results
3.1 SpARC Algorithm for Tissue Classification
Classification of data Set 1 and Set 2 was achieved by using either only Rratio as a classifier (Table 5, Table 6), or only Farea as a classifier (Table 7, and Table 8) or a combination of the two (Table 9 and Table 10). The tables list the sensitivity, specificity, PPV, and NPV of the SpARC algorithm in classifying (a) adenocarcinoma from pancreatitis and normal tissue, (b) adenocarcinoma from pancreatitis tissue, (c) adenocarcinoma from normal tissue, (d) pancreatitis from normal tissue and adenocarcinoma tissue (e) normal from adenocarcinoma and pancreatitis tissue. The classification in Tables 9 and 10 was undertaken by first employing Rratio to identify adenocarcinoma and then employing Rratio and Farea to distinguish between pancreatitis and normal tissue types. Farea was not employed for adenocarcinoma classification as it decreased the classification performance of the algorithm.







TABLE 5







Employing only Rratio for data Set 1 classification














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
84.8
85.5
75.7
91.4



P vs. A and N
35.0
87.3
66.7
64.9



N vs. A and P
81.8
74.0
48.6
93.1



A vs. P
84.8
85.0
82.4
87.2



A vs. N
87.9
95.5
96.7
84.0



P vs. N
57.5
81.8
85.2
51.4







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 6







Employing only Rratio for data Set 2 classification














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
87.9
88.7
82.9
92.2



P vs. A and N
35.5
87.3
61.1
70.6



N vs. A and P
72.7
73.4
48.5
88.7



A vs. P
84.8
83.9
84.8
83.9



A vs. N
87.9
95.5
96.7
84.0



P vs. N
51.6
72.7
72.7
51.6







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 7







Employing only Farea for data Set 1 classification














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
66.7
62.9
48.9
78.0



P vs. A and N
57.5
69.1
57.5
69.1



N vs. A and P
18.2
91.8
40.0
78.8



A vs. P
66.7
62.5
59.5
69.4



A vs. N
51.5
59.1
65.4
44.8



P vs. N
57.5
63.6
74.2
45.2







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 8







Employing only Farea for data Set 2 classification














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
66.7
60.4
51.2
74.4



P vs. A and N
54.8
67.3
48.6
72.5



N vs. A and P
13.6
92.2
37.5
75.6



A vs. P
66.7
58.1
62.9
62.1



A vs. N
51.5
59.1
65.4
44.8



P vs. N
54.8
59.1
65.4
48.1







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 9







Performance of the SpARC algorithm for data Set 1














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
84.8
85.5
75.7
91.4



P vs. A and N
50.0
83.6
69.0
69.7



N vs. A and P
72.7
82.2
55.2
90.9



A vs. P
84.8
85.0
82.4
87.2



A vs. N
87.9
95.5
96.7
84.0



P vs. N
70.0
72.7
82.4
57.1







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 10







Performance of the SpARC algorithm for data Set 2














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
87.9
88.7
82.9
92.2



P vs. A and N
51.6
87.3
69.6
76.2



N vs. A and P
72.7
81.3
57.1
89.7



A vs. P
84.8
83.9
84.8
83.9



A vs. N
87.9
95.5
96.7
84.0



P vs. N
64.5
72.7
76.9
59.3







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma






3.2 Chemometric Classification
The t-tests performed as described above indicated that the principal components of the reflectance and fluorescence spectra that were diagnostically relevant for classifying Adenocarcinoma from Pancreatitis and Normal (RPCA and FPCA) were not the same as for classifying Pancreatitis from Normal. This indicated that it may be necessary to separate the classification algorithm into a two-step process where first Adenocarcinoma sites are identified in the Test data and then the rest of the data is classified into Normal and Pancreatitis.
3.2.1 Classifying Adenocarcinoma vs. Pancreatitis and Normal Tissue
Four diagnostically relevant principal components, two each from reflectance and fluorescence were identified by finding those components, in the first ten principal components, for which the t-test between adenocarcinoma and the remaining tissue types' fit-coefficents gave a p-value<0.05. LDA was then used to classify the test data into adenocarcinoma or not adenocarcinoma based on the fit-coefficient values for all or a subset of these four principal component values (RPCA1, RPCA2, FPCA1, FPCA2).
If a tissue type was classified as not adenocarcinoma in this part of the algorithm then it was classified as either normal or pancreatitis as described below.
3.2.2 Classifying Pancreatitis vs. Normal Tissue
Four diagnostically relevant principal components, two each from reflectance and fluorescence were identified by finding those components, in the first ten principal components, for which the t-test between normal and pancreatitis fit-coefficents gave a p-value<0.05. LDA was then used to classify test data into normal and pancreatitis based on the fit-coefficient values for all or a subset of these four principal component values (RPCP-N1, RPCP-N2, FPCP-N1, FPCP-N2). The chemometric algorithm was validated by employing the leave-one-out technique.
Table 11 and Table 12 list the classification performance of the chemometric algorithm if only the fit-coefficients of diagnostically relevant reflectance principal components are employed for tissue classification (RPCA1, RPCA2, RPCP-N1, RPCP-N2).
Table 13 and Table 14 list the classification performance of the chemometric algorithm if only the fit-coefficients of diagnostically relevant principal components of the fluorescence spectra are employed for tissue classification (FPCA1, FPCA2, FPCP-N1, FPCP-N2). In Table 14 values are missing due to the absence of any diagnostically relevant, FPCP-N1, FPCP-N2 for distinguishing between normal and pancreatitis for certain Test data. This shows that chemometric analysis of fluorescence spectra could not be used alone for tissue classification.
Table 15 and Table 16 list the classification performance of the chemometric algorithm if the fit-coefficients of all diagnostically relevant principal components of reflectance and fluorescence spectra are employed for tissue classification (RPCA1, RPCA2, FPCA1, FPCA2, RPCP-N1, RPCP-N2, FPCP-N1, and FPCP-N2).







TABLE 11







Performance of the chemometric algorithm using Reflectance spectra


only for data Set 1 (RPCA1, RPCA2, RPCP-N1, RPCP-N2)














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
93.9
83.9
75.6
96.3



P vs. A and N
52.5
90.9
80.8
72.5



N vs. A and P
77.3
84.9
60.7
92.5



A vs. P
90.9
77.5
76.9
91.2



A vs. N
93.9
86.4
91.2
90.5



P vs. N
70.0
77.3
84.8
58.6







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 12







Performance of the chemometric algorithm using Reflectance spectra


only for data Set 2 (RPCA1, RPCA2, RPCP-N1, RPCP-N2)














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
87.9
86.8
80.6
92.0



P vs. A and N
54.8
87.3
70.8
77.4



N vs. A and P
68.2
82.8
57.7
88.3



A vs. P
87.9
80.6
82.9
86.2



A vs. N
93.9
86.4
91.2
90.5



P vs. N
74.2
68.2
76.7
65.2







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 13







Performance of the chemometric algorithm using Fluorescence spectra


only for data Set 1 (FPCA1, FPCA2, FPCP-N1, FPCP-N2)














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
78.8
82.3
70.3
87.9



P vs. A and N
32.5
78.2
52.0
61.4



N vs. A and P
50.0
69.9
33.3
82.3



A vs. P
78.8
80.0
76.5
82.1



A vs. N
81.8
81.8
87.1
75.0



P vs. N
42.5
54.5
63.0
34.3







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 14







Performance of the chemometric algorithm using Fluorescence spectra


only for data Set 2 (FPCA1, FPCA2, FPCP-N1, FPCP-N2)














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
69.7
81.1
69.7
81.1



P vs. A and N
*
*
*
*



N vs. A and P
*
*
*
*



A vs. P
69.7
77.4
76.7
70.6



A vs. N
72.7
77.3
82.8
65.4



P vs. N
*
*
*
*







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma;



* No significant PC for













TABLE 15







Performance of the chemometric algorithm for data Set 1


Employing RPCA1, RPCA2, FPCA1, FPCA2, RPCP-N1,


RPCP-N2, FPCP-N1, and FPCP-N2














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
90.9
83.9
75.0
94.5



P vs. A and N
52.5
87.3
75.0
71.6



N vs. A and P
63.6
82.2
51.9
88.2



A vs. P
87.9
82.5
80.6
89.2



A vs. N
93.9
85.7
91.2
90.0



P vs. N
67.5
63.6
77.1
51.9







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 16







Performance of the chemometric algorithm for data Set 2


Employing RPCA1, RPCA2, FPCA1, FPCA2, RPCP-N1,


RPCP-N2, FPCP-N1, and FPCP-N2














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
87.9
88.7
82.9
92.2



P vs. A and N
58.1
85.5
69.2
78.3



N vs. A and P
63.6
82.8
56.0
86.9



A vs. P
87.9
87.1
87.9
87.1



A vs. N
93.9
86.4
91.2
90.5



P vs. N
71.0
63.6
73.3
60.9







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma






Table 17 and Table 18 list the classification performance of the chemometric algorithm if the fit-coefficients of the following diagnostically relevant principal components of reflectance and fluorescence spectra are employed for tissue classification (RPCA1, RPCA2, FPCA1, RPCP-N1, RPCP-N2, FPCP-N1, and FPCP-N2) i.e. FPCA2 was not employed for adenocarcinoma classification. This algorithm showed the best adenocarcinoma classification.







TABLE 17







Performance of the chemometric algorithm for data Set 1


Employing RPCA1, RPCA2, FPCA1,


RPCP-N1, RPCP-N2, FPCP-N1, and FPCP-N2














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
93.9
85.5
77.5
96.4



P vs. A and N
52.5
85.5
72.4
71.2



N vs. A and P
63.6
83.6
53.8
88.4



A vs. P
93.9
80.0
79.5
94.1



A vs. N
93.9
86.4
91.2
90.5



P vs. N
67.5
63.6
77.1
51.9







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 18







Performance of the chemometric algorithm for data Set 2


Employing RPCA1, RPCA2, FPCA1,


RPCP-N1, RPCP-N2, FPCP-N1, and FPCP-N2














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
93.9
88.7
83.8
95.9



P vs. A and N
54.8
87.3
70.8
77.4



N vs. A and P
63.6
82.8
56.0
86.9



A vs. P
90.9
87.1
88.2
90.0



A vs. N
93.9
86.4
91.2
90.5



P vs. N
71.0
63.6
73.3
60.9







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma






3.3 Classification Based on Parameters Extracted from the PTI Model
Classification of data Set 1 and Set 2 was achieved by using either only the parameter extracted from reflectance spectra (L/L0) as a classifier (Table 19 and Table 20), or only the parameters extracted from fluorescence spectra (Ccoll, CNADH+CFAD) as a classifier (Table 21 and Table 22) or a combination of these (Table 23 and Table 24). The tables list the sensitivity, specificity, PPV, and NPV of the SpARC algorithm in classifying (a) adenocarcinoma from pancreatitis and normal tissue, (b) adenocarcinoma from pancreatitis tissue, (c) adenocarcinoma from normal tissue, (d) pancreatitis from normal tissue and adenocarcinoma tissue (e) normal from adenocarcinoma and pancreatitis tissue.







TABLE 19







Performance of the PTI model algorithm for data Set 1


CLASSIFIER SET: L/Lo only














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
93.9
67.7
60.8
95.5



P vs. A and N
57.5
63.6
53.5
67.3



N vs. A and P
81.8
65.8
41.9
92.3



A vs. P
93.9
57.5
64.6
92.0



A vs. N
93.9
81.8
88.6
90.0



N vs. P
42.5
81.8
81.0
43.9







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 20







Performance of the PTI model algorithm for data Set 2


CLASSIFIER SET: L/Lo only














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
93.9
71.7
67.4
95.0



P vs. A and N
64.5
63.6
50.0
76.1



N vs. A and P
81.8
65.6
45.0
91.3



A vs. P
93.9
64.5
73.8
90.9



A vs. N
93.9
81.8
88.6
90.0



N vs. P
35.5
81.8
73.3
47.4







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 21







Performance of the PTI model algorithm for data Set 1


CLASSIFIER SET: Ccoll, CNADH + CFAD only














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)

















A vs. P and N
78.8
91.9
83.9
89.1



P vs. A and N
25.0
81.8
50.0
60.0



N vs. A and P
72.7
61.6
36.4
88.2



A vs. P
81.8
87.5
84.4
85.4



A vs. N
78.8
100.0
100.0
75.9



N vs. P
37.5
68.2
68.2
37.5







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 22







Performance of the PTI model algorithm for data Set 2


CLASSIFIER SET: Ccoll, CNADH + CFAD only














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)

















A vs. P and N
78.8
92.5
86.7
87.5



P vs. A and N
19.4
80.0
35.3
63.8



N vs. A and P
72.7
64.1
41.0
87.2



A vs. P
78.8
87.1
86.7
79.4



A vs. N
78.8
100.0
100.0
75.9



N vs. P
35.5
59.1
55.0
39.4







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 23







Performance of the PTI model algorithm for data Set 1


CLASSIFIER SET 5: L/Lo, Ccoll, CNADH + CFAD














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
84.8
90.3
82.4
91.8



P vs. A and N
70.0
52.7
51.9
70.7



N vs. A and P
81.8
67.1
42.9
92.5



A vs. P
84.8
87.5
84.8
87.5



A vs. N
87.9
86.4
90.6
82.6



N vs. P
52.5
72.7
77.8
45.7







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 24







Performance of the PTI model algorithm for data Set 2


CLASSIFIER SET 5: L/Lo, Ccoll, CNADH + CFAD














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
87.9
86.8
80.6
92.0



P vs. A and N
71.0
54.5
46.8
76.9



N vs. A and P
77.3
67.2
44.7
89.6



A vs. P
87.9
80.6
82.9
86.2



A vs. N
87.9
86.4
90.6
82.6



N vs. P
54.8
63.6
68.0
50.0







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma






3.4 Hybrid Algorithm for Tissue Classification: Combination of Chemometric, PTI Model and SpARC Algorithms
The Hybrid algorithm was also a two-step algorithm that first identified a Test data as adenocarcinoma or not. If not, the data was then classified as either pancreatitis or normal.
FIG. 7 provides a flow chart 70 which illustrates, among other things, an example of the manner in which the hybrid algorithm described herein can be employed to classify a pancreatic tissue site into normal, adenocarcinoma, or pancreatitis. In this example, a fiber optic probe, such as the probe discussed previously, delivers excitation light from the fluorescence and reflectance sources to the tissue site to produce a spectroscopic event in step 72. The fluorescence and reflectance signals are collected by the probe and delivered to the detectors in step 74 to determine the emitted reflectance spectrum (R(λ)) in step 76, along with the fluorescence spectrum (F(λ)) and time-resolved fluorescence decay (F(t)) in step 78. A data processing device receives the measurements derived from the reflectance and fluorescence signals and uses these measurements to obtain further information regarding the tissue site. In this embodiment, the SpARC algorithm is employed to calculate the Rratio (=R470/R650) for the reflectance spectrum in step 80 and the wavelength integrated fluorescence intensity (Farea) for the fluorescence spectrum in step 82. The Chemometric algorithm is employed to calculate the fit-coefficients (a) for the diagnostically significant principal components (PC) of the reflectance in step 84 and fluorescence spectra and time-resolved fluorescence decay in step 86. The PTI model algorithm is employed to use the reflectance to derive nuclear size (L/Lo) in step 88, whereas the fluorescence is used to derive Ccoll, CNADH, and CFAD in step 90. Linear Discriminant Analysis (LDA) is applied in step 92 to use the various derived values relating to the tissue site of interest to determine the most likely tissue classification using a leave-one-out cross-validation. In this embodiment, the possible tissue classifications which may be determined based on the overall analysis of the derived values include adenocarcinoma, pancreatitis or normal as shown in steps 94a, 94b and 94c, respectively.
Table 25 and Table 26 list the sensitivity, specificity, PPV, and NPV of the Hybrid algorithm in classifying pancreatic tissue (for data Set 1 and Set2). In this case all the classifiers from SpARC (Rratio and Farea) and Chemometric (RPCA1, RPCA2, FPCA1, FPCA2, RPCP-N1, RPCP-N2, FPCP-N1, and FPCP-N2), and PTI model (L/Lo, Ccoll, CNADH+CFAD) algorithms were employed for tissue classification. However, a sub-set of these classifiers could also be used.







TABLE 25







Performance of the Hybrid algorithm for data Set 1


All classifiers from Chemometric, PTI model


and SpARC algorithm included














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
84.8
95.2
90.3
92.2



P vs. A and N
65.0
85.5
76.5
77.0



N vs. A and P
72.7
80.8
53.3
90.8



A vs. P
84.8
92.5
90.3
88.1



A vs. N
90.9
86.4
90.9
86.4



N vs. P
72.5
72.7
82.9
59.3







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma













TABLE 26







Performance of the Hybrid algorithm for data Set 2


All classifiers from Chemometric, PTI model


and SpARC algorithm included














Sensitivity
Specificity
PPV
NPV



Tissue Type
(%)
(%)
(%)
(%)







A vs. P and N
87.9
92.5
87.9
92.5



P vs. A and N
48.4
87.3
68.2
75.0



N vs. A and P
68.2
75.0
48.4
87.3



A vs. P
84.8
87.1
87.5
84.4



A vs. N
87.9
81.8
87.9
81.8



N vs. P
58.1
68.2
72.0
53.6







N: Normal;



P: Pancreatitis;



A: Adenocarcinoma






The bar plots in FIGS. 4A and 4B summarize the performance of each of the developed algorithms in identifying adenocarcinoma tissue for data Set 1 and Set 2. The chemometric algorithm represented here as the first column 50 of each grouping of columns employed RPCA1, RPCA2, FPCA1, RPCP-N1, RPCP-N2, FPCP-N1, and FPCP-N2 for classification (as shown in Table 17 and Table 18). The PTI model algorithm represented as the second column 52 of each grouping employed L/Lo, Ccoll, and CNADH+CFAD for classification (Table 23 and Table 24). The SpARC algorithm represented as the third column 54 of each grouping employed Rratio for adenocarcinoma identification and Rratio and Farea for classification of Test data as either pancreatitis or normal tissue (see Table 9 and Table 10). The hybrid algorithm is represented as the fourth column 56 of each grouping of columns. The performance of a second hybrid algorithm employing only a subset of the classifiers is also shown as the fifth column 58 of each grouping. In this case only Rratio, RPCA1, RPCA2, FPCA1, L/Lo, Ccoll, and CNADH+CFAD were employed for adenocarcinoma classification. For classification of the Test data as either pancreatitis or normal all the classifiers were employed (Rratio, Farea, RPCP-N1, RPCP—N2, FPCP-N1, FPCP—N2, L/Lo, Ccoll, and CNADH+CFAD). The plot shows that a combination of the developed algorithms shows the most promise for pancreatic tissue classification.
3.5 Classifying Pancreatitis vs. Normal Tissue Using PCA and LDA on Time-Resolved Fluorescence Data
The fit-coefficients of the 1st and 7th principal components were identified as diagnostically relevant for classifying pancreatitis and normal tissue. LDA was then employed to classify test data into normal and pancreatitis based on these fit-coefficient values for these diagnostically relevant principal component values.
The chemometric algorithm was validated by employing the leave-one-out technique as described above. Table 27 lists sensitivity, specificity, PPV, and NPV of the chemometric algorithm in classifying pancreatitis from normal tissue using time-resolved fluorescence data (Set 1 and Set 2). This algorithm appears to have performed the best amongst the developed algorithm for classifying between normal and pancreatitis tissue but may need further study and/or refinement.







TABLE 27







Performance of the time-resolved fluorescence data chemometric


algorithm in classifying pancreatitis and normal tissue












Sensitivity
Specificity
PPV
NPV


Data Set*
(%)
(%)
(%)
(%)





Set 1
75.0
81.8
88.2
64.3


Set 2
71.0
81.8
84.6
66.7





*Patient 11 data from both data set 1 and data set 2 were excluded due to difference in data acquisition parameters






Time-resolved data will be collected with a long-pass filter (>500 nm) in front of the detector (patient 11 onwards). This will enable us to capture only a portion of the emitted fluorescence spectrum and assess its diagnostic information.
The chemometric analysis of time-resolved fluorescence data shows promise as a possible method of classifying pancreatic tissue data. It may be used in the hybrid algorithm in conjunction with the other developed algorithms for pancreatic tissue classification.
4. Illustration 2 Discussion and Conclusion
In all the algorithms the combination of classifiers extracted from both reflectance and fluorescence performed better than using information from either just reflectance or just fluorescence. The performance of the four developed tissue classification algorithms (Chemometric, PTI model, SpARC, and Hybrid) all show promise for pancreatic tissue classification using optical spectroscopy. The sensitivity and specificity of the algorithms for adenocarcinoma identification are comparable and in some cases better than reported literature performance of EUS-FNA which is generally considered the diagnostic standard.
Obviously, many modifications and variations of the embodiments of the invention are possible in light of the above teachings. The invention may be practiced otherwise than as specifically described within the scope of the appended claims.

## Claims

1. A method of employing multimodal spectroscopy to classify tissue comprising the steps of:
illuminating a biological tissue sample to produce a measurable spectroscopic event;
collecting spectroscopic response data from the spectroscopic event, wherein the response data includes measurements derived from fluorescence and reflectance signals, including reflectance spectra, fluorescence spectra and time-resolved fluorescence decay measurements associated with the tissue sample;
comparing the response data with preset criteria relating the collected response data with one or more attributes of the tissue sample, wherein each attribute is at least partially indicative of a tissue classification;
determining whether preset criteria are satisfied by the collected data; and
classifying the tissue sample according to the one or more attributes identified by the preset criteria satisfied.

2. A method according to claim 1, wherein the step of illuminating the biological tissue sample is configured to produce a plurality of measurable spectroscopic events.

3. A method according to claim 2, wherein the illuminating step includes an event including illumination wavelengths of about 400 nm to about 750 nm to facilitate collecting reflectance signal measurements and an event including illumination wavelengths of about 355 nm to facilitate collecting fluorescence signal measurements.

4. A method according to claim 1, further comprising the steps of: determining the reflectance intensities and wavelength integrated fluorescence intensity using a SpARC algorithm; determining the diagnostically relevant principal components using a Chemometric algorithm; determining the nuclear size, the content of collagen, the content of NADH and the content of FAD in the tissue sample using a PTI model algorithm; and selecting the classification by applying a linear discriminant analysis to the determined values, wherein the determined values are derived from the fluorescence spectra, reflectance spectra and time-resolved fluorescence decay measurements.

5. A method according to claim 1, wherein the one or more attributes of the tissue sample comprise one or more histological features.

6. A method according to claim 1, wherein the step of classifying the tissue sample further includes classifying the tissue as normal or abnormal.

7. A method according to claim 1, wherein the preset criteria relates the deviation between the collected spectroscopic response data at a plurality of wavelengths for the tissue sample and spectroscopic response data at the plurality of wavelengths for normal tissue with one or more histological features associated with the tissue sample.

8. A method according to claim 7, further comprising the steps of:
measuring the extent of the deviation between the collected response data for the tissue sample and the response data for normal tissue;
determining whether the extent of the deviation is sufficient for satisfying any of the preset criteria; and
classifying the tissue sample according to the histological feature identified by the preset criteria satisfied.

9. A method according to claim 8, wherein the step of measuring the amount of deviation between the collected response data for the tissue sample and the response data for normal tissue further comprises measuring the extent of deviation in a range of wavelengths between about 360 nm to about 750 nm.

10. A method according to claim 1, wherein attributes of the tissue sample include the nuclear size of cells associated with the tissue sample, the collagen content associated with the tissue sample, the NADH content associated with the tissue sample, and the FAD content associated with the tissue sample.

11. A method according to claim 1, wherein the tissue sample is pancreatic tissue and the tissue sample classifications include normal, adenocarcinoma and pancreatitis.

12. A system for classifying biological tissue using multimodal optical spectroscopy, comprising:
(a) a light source for generating light to illuminate a biological tissue sample;
(b) a probe configured for directing the light generated by the light source onto the tissue sample to illuminate the tissue sample and generate a measurable spectroscopic event;
(c) one or more detectors configured for collecting spectroscopic response data, wherein the spectroscopic response data includes measurements derived from fluorescence and reflectance signals associated with the tissue sample, including reflectance spectra, fluorescence spectra and time-resolved fluorescence decay measurements; and
(d) a data processor configured for:
(i) analyzing the collected spectroscopic response data;
(ii) comparing the collected spectroscopic response data with preset criteria relating the collected spectroscopic response data with one or more attributes associated with the tissue sample, wherein each attribute is at least partially indicative of a tissue classification;
(iii) determining whether preset criteria are satisfied by the collected spectroscopic response data; and iv. classifying the tissue sample according to the one or more attributes identified by the preset criteria satisfied.

13. A system as recited in claim 12, wherein the probe further comprises a plurality of optical fibers for directing light onto the tissue sample, receiving fluorescence and reflectance signals from the tissue sample and directing the received fluorescence and reflectance signals to the one or more detectors.

14. A system as recited in claim 12, wherein the probe further comprises a first optical fiber configured for delivering reflectance illumination to the tissue sample, a second optical fiber configured for delivering fluorescence illumination to the tissue sample and a third optical fiber configured for detecting emitted reflectance and fluorescence from the tissue sample.

15. A system as recited in claim 14, wherein the first, second and third optical fibers are disposed in a generally triangular cross-sectional arrangement.

16. A system as recited in claim 12, wherein the light source further comprises a light source configured for generating light at wavelengths of about 400 nm to about 750 nm to facilitate collecting reflectance signal measurements and a light source configured for generating light at wavelengths of about 355 nm to facilitate collecting fluorescence signal measurements.

17. A method employing multimodal optical spectroscopy to classify pancreatic tissue, comprising:
illuminating a pancreatic tissue sample to produce a measurable spectroscopic event;
collecting spectroscopic response data from the spectroscopic event, wherein the response data includes measurements derived from fluorescence and reflectance signals associated with the tissue sample, including reflectance spectra, fluorescence spectra and time-resolved fluorescence decay measurements;
comparing the response data with a first preset criteria for identifying adenocarcinoma and a second preset criteria for identifying pancreatitis or normal tissue;
relating the collected spectroscopic response data with one or more histological features associated with the pancreatic tissue sample, wherein the histological features are indicative of a tissue classification of either normal pancreatic tissue, adenocarcinoma or pancreatitis;
determining if either the first or second preset criteria are satisfied; and
classifying the pancreatic tissue sample as either normal, adenocarcinoma or pancreatitis based on the one or more histological features identified by the preset criteria satisfied.

18. A method according to claim 17, wherein the step of comparing the response data with preset criteria relating the collected spectroscopic response data with one or more histological features associated with the pancreatic tissue sample further comprises comparing the response data at wavelengths ranging between about 360 nm to about 750 nm with the preset criteria.

19. A method according to claim 17, further comprising the steps of: determining the reflectance intensities and wavelength integrated fluorescence intensity using a SpARC algorithm; determining the diagnostically relevant principal components using a Chemometric algorithm; determining the nuclear size, the content of collagen, the content of NADH and the content of FAD in the tissue sample using a PTI model algorithm; and selecting the classification by applying a linear discriminant analysis to the determined values, wherein the determined values are derived from the fluorescence spectra, reflectance spectra and time-resolved fluorescence decay measurements.