U.S. patent application number 13/671483 was filed with the patent office on 2013-05-23 for multimodal detection of tissue abnormalities based on raman and background fluorescence spectroscopy.
This patent application is currently assigned to BRITISH COLUMBIA CANCER AGENCY BRANCH. The applicant listed for this patent is BRITISH COLUMBIA CANCER AGENCY BRANC. Invention is credited to Zhiwei HUANG, Harvey LUI, David I. McLEAN, Haishan ZENG.
Application Number | 20130131488 13/671483 |
Document ID | / |
Family ID | 34632967 |
Filed Date | 2013-05-23 |
United States Patent
Application |
20130131488 |
Kind Code |
A1 |
ZENG; Haishan ; et
al. |
May 23, 2013 |
MULTIMODAL DETECTION OF TISSUE ABNORMALITIES BASED ON RAMAN AND
BACKGROUND FLUORESCENCE SPECTROSCOPY
Abstract
Methods and apparatus for classifying tissue use features of
Raman spectra and background fluorescent spectra. The spectra may
be acquired in the near-infrared wavelengths. Principal component
analysis and linear discriminant analysis of reference spectra may
be used to obtain a classification function that accepts features
of the Raman and background fluorescence spectra for test tissue
and yields an indication as to the likelihood that the test tissue
is abnormal. The methods and apparatus may be applied to screening
for skin cancers or other diseases.
Inventors: |
ZENG; Haishan; (Vancouver,
CA) ; LUI; Harvey; (Vancouver, CA) ; HUANG;
Zhiwei; (SG) ; McLEAN; David I.; (Vancouver,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
BRITISH COLUMBIA CANCER AGENCY BRANC; |
Vancouver |
|
CA |
|
|
Assignee: |
BRITISH COLUMBIA CANCER AGENCY
BRANCH
Vancouver
CA
|
Family ID: |
34632967 |
Appl. No.: |
13/671483 |
Filed: |
November 7, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10596072 |
May 12, 2008 |
8326404 |
|
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PCT/CA2004/002040 |
Nov 26, 2004 |
|
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13671483 |
|
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60525139 |
Nov 28, 2003 |
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Current U.S.
Class: |
600/408 ;
600/407; 600/473; 600/475 |
Current CPC
Class: |
G01N 21/6486 20130101;
A61B 5/0075 20130101; A61B 5/7264 20130101; A61B 5/0071 20130101;
A61B 5/444 20130101; A61B 5/0035 20130101; A61B 5/0086 20130101;
G01N 21/65 20130101 |
Class at
Publication: |
600/408 ;
600/473; 600/475; 600/407 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Claims
1. A method for characterizing a tissue, the method comprising:
obtaining a raw spectrum of the tissue by illuminating the tissue
with infrared light that is substantially monochromatic and
detecting light backscattered from the tissue at a plurality of
infrared wavelengths in a first wavelength range, processing the
raw spectrum to obtain both a Raman spectrum of the tissue and a
background fluorescence spectrum of the tissue that is a background
to the Raman spectrum in the first wavelength range; processing the
Raman spectrum to extract one or more features of the Raman
spectrum and processing the background fluorescence spectrum to
extract one or more features of the background fluorescence
spectrum; and, characterizing the tissue based upon at least the
one or more features of the Raman spectrum and the one or more
features of the background fluorescence spectrum.
2. A method according to claim 1 wherein processing the raw
spectrum to obtain the Raman spectrum comprises fitting a
background fitting function to the raw spectrum to yield a fitted
background function and subtracting the fitted background function
from the raw spectrum.
3. A method according to claim 2 wherein processing the background
fluorescence to extract one or more features of the background
fluorescence spectrum comprises processing the fitted background
function.
4. A method according to claim 3 wherein the first wavelength range
includes wavelengths from about 800 nm to about 1000 nm.
5. A method according to claim 3 wherein the infrared light has a
wavelength of 785 nm.
6. A method according to claim 1 wherein characterizing the tissue
based upon at least the Raman spectrum features and the background
fluorescence spectrum features comprises applying to one or more
datasets a classification function derived from principal
components analysis, the one or more datasets collectively
including the one or more Raman spectrum features and the one or
more background fluorescence spectrum features.
7. A method according to claim 6 wherein the one or more datasets
include one or more Raman spectrum principal components scores and
one or more background fluorescence spectrum principal components
scores.
8. A method according to claim 6 wherein applying the
classification function comprises applying a predetermined
principal component to data of the one or more datasets.
9. A method according to claim 1 wherein the tissue is skin of a
part of a subject's body and the method comprises selecting the
classification function corresponding to the part of the subject's
body from a plurality of classification functions each
corresponding to a different body region.
10. A method according to claim 9 wherein the plurality of
classification functions includes classification functions
corresponding to two or more of the following body parts: head,
torso, hand, and arm or thigh.
11. A method according to claim 1 wherein characterizing the tissue
comprises applying the Raman spectrum features and the background
fluorescence spectrum features as inputs to a neural network.
12. A method according to claim 1 where the section of tissue is a
section of a tissue selected from the group consisting of: skin,
lung tissue, and epithelial tissue.
13. A method according to claim 12 wherein the tissue comprises
epithelial tissue and the epithelial tissue comprises tissue lining
the subject's gastrointestinal tract, ear, nose or throat.
14. A method according to claim 1 applied to screening for skin
cancer.
15. A method according to claim 1 applied to screening for one or
more conditions selected from the group consisting of: basal cell
carcinoma, squamous cell carcinoma, melanoma, actinic keratosis,
seborrheic keratosis, sebaceous hyperplasia, keratoacanthoma,
lentigo, melanocytic nevi, dysplastic nevi, and blue nevi.
16. A method according to claim 1 wherein the first wavelength
range includes wavelengths from about 800 nm to about 1000 nm.
17. A method according to claim 2 wherein the one or more Raman
features include an intensity of the Raman spectrum at a Raman
shift of 1445 cm.sup.-1 relative to a wavelength of the incident
light.
18. A method according to claim 17 wherein the one or more Raman
features include an intensity of the Raman spectrum at a Raman
shift of 1269 cm.sup.-1 relative to a wavelength of the incident
light.
19. A method according to claim 18 wherein characterizing the
tissue comprises one or both of indicating whether the tissue is
likely affected by melanoma and indicating whether the tissue is
likely compound nevus tissue.
20. A method according to claim 2 wherein the one or more Raman
features include an intensity of the Raman spectrum at a Raman
shift of 1269 cm.sup.-1 relative to a wavelength of the incident
light.
21. A method according to claim 2 wherein the one or more Raman
features include features within a band having a Raman shift in the
range of about 1200 cm.sup.-1 to about 1400 cm.sup.-1 relative to a
wavelength of the incident light.
22. A method according to claim 2 wherein the Raman features
include features within a band having a Raman shift in the range of
about 1500 cm.sup.-1 to about 1650 cm.sup.-1 relative to a
wavelength of the incident light.
23. A method according to claim 1 wherein the Raman spectrum
includes first and second peaks at Raman shifts of approximately
1368 cm.sup.-1 and 1572 cm.sup.-1 relative to a wavelength of the
incident light, the method further comprising determining a melanin
content of the tissue by performing steps comprising: computing the
melanin content of the tissue based upon intensities of the first
and second peaks in the Raman spectrum.
24. Apparatus for characterizing tissues in vivo, the apparatus
comprising: a light source for illuminating a tissue; an optical
system configured to collect and direct backscattered light from
the tissue into a spectrometer; a data processor connected to
receive raw spectrum information for the backscattered light from
the spectrometer; at least one classification function accessible
to the data processor, the classification function producing a
classification result in response to an input, the input including
information about at least one Raman feature and at least one
background autofluorescence feature of a tissue spectrum; wherein
the data processor is configured to: process the raw spectrum to
obtain both a Raman spectrum of the tissue and a background
autofluorescence spectrum of the tissue that is a background to the
Raman spectrum; process the Raman spectrum to extract one or more
features of the Raman spectrum and process the background
fluorescence spectrum to extract one or more features of the
background fluorescence spectrum; and, characterize the tissue
based by applying the one or more features of the Raman spectrum
and the one or more features of the background fluorescence
spectrum as inputs to the classification function.
25. Apparatus according to claim 24 comprising a plurality of
classification functions and a mechanism for permitting a user to
select one of the plurality of classification functions to be used
to characterize the tissues.
26. A method for determining a melanin content of tissue, the
method comprising: obtaining a spectrum of the tissue, the spectrum
including first and second peaks at Raman shifts of approximately
1368 cm.sup.-1 and 1572 cm.sup.-1; subtracting a background of the
spectrum to yield a Raman spectrum; computing the melanin content
of the tissue based upon intensities of the first and second peaks
in the Raman spectrum.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 10/596,072 filed on 12 May 2008, which is a
371 of PCT International Patent Application No. PCT/CA2004/002040
filed on 26 Nov. 2004, which claims priority from U.S. Patent
Application No. 60/525,139 filed on 28 Nov. 2003, all of which are
entitled MULTIMODAL DETECTION OF TISSUE ABNORMALITIES BASED ON
RAMAN AND BACKGROUND FLUORESCENCE SPECTROSCOPY. For purposes of the
United States, this application claims the benefit under 35 U.S.C.
.sctn.119 of U.S. Patent Application No. 60/525,139 filed on 28
Nov. 2003 and entitled MULTIMODAL DETECTION OF TISSUE ABNORMALITIES
BASED ON RAMAN AND BACKGROUND FLUORESCENCE SPECTROSCOPY which is
incorporated herein by reference for all purposes.
TECHNICAL FIELD
[0002] The invention relates to the detection of tissue
abnormalities. The invention may be applied, for example, to
screening subjects for tumors or other cancerous lesions. The
invention may be applied to screening skin tissues or other
tissues.
BACKGROUND
[0003] Skin cancer is the most common cancer in North America. Over
550,000 new cases of skin cancer are diagnosed each year. One in
seven Canadians will develop a skin cancer during their lifetime.
If detected early, skin cancer can be cured by relatively minor
surgical removal. However, if detected late, more extensive and
disfiguring surgery becomes necessary. It is especially important
to diagnose malignant melanoma early. If treatment for malignant
melanoma is commenced too late, systemic metastasis and death can
occur.
[0004] At present, skin cancers are detected primarily by visual
inspection by physicians. However, clinical accuracy of visual
diagnoses is 75% at best. Definitive diagnosis is therefore based
on histological examination of skin biopsy. Excisional biopsy
currently remains the most reliable diagnostic approach for the
early detection of skin cancer, but is invasive and impractical for
screening high-risk patients who may have multiple suspicious
lesions. Many unnecessary biopsies are done, at considerably cost
to the health care system. Moreover, some needed biopsies may not
be performed because of a failure to recognize a cancer.
[0005] During skin cancer treatment, visual assessment is also
relied upon to determine the extent of the tumor, and therefore the
amount of tissue to be either excised or irradiated. If a tumor has
margins that are poorly defined, it may be necessary to perform
repeated biopsy procedures from multiple sites in a time-consuming,
expensive, and tedious procedure known as Mohs micrographic
surgery.
[0006] Following skin cancer treatment, ongoing patient monitoring
by visual inspection and periodic microscopic examination is
required for detecting recurrent tumor or de novo skin cancer at
other sites. All stages in the management of skin cancer would be
facilitated by techniques that could provide accurate diagnostic
information without requiring multiple expensive and potentially
disfiguring skin biopsies.
[0007] A variety of approaches for noninvasive diagnosis of the
skin have been developed using either optical or non-optical
methods. Non-optical methods include ultrasound and MRI, while skin
reflectance, autofluorescence, and thermography involve measurement
of cutaneous optical properties that are altered in disease states.
Many groups in the world are working to develop reflectance skin
imaging methods (analogous to digital photography) for improving
the early detection of skin cancer using digital processing. This
approach has improved the registration, recording, and
documentation of skin lesions, but has not yet significantly
improved the accuracy of non-invasive diagnoses.
[0008] Raman spectroscopy and fluorescence spectroscopy have both
been suggested as tools for the diagnosis of cancers. Raman
spectroscopy measures the wavelength and intensity of light which
has been scattered inelastically from molecular systems. Raman
scattered light has wavelengths that are shifted from that of the
incident light by amounts corresponding to the energies of
excitations of the molecular systems. The excitations are typically
vibrations.
[0009] Raman scattered light is typically relatively faint. When
monochromatic light strikes a sample, almost all the observed light
is scattered elastically (Rayleigh scattering) with no change in
energy (or wavelength). Only a very small portion of the scattered
light, typically approximately 1 part in 10.sup.8, is inelastically
scattered (Raman scattering). Raman peaks are typically narrow and
in many cases can be attributed to the vibration of specific
chemical bonds (or normal modes dominated by the vibration of a
functional group) in a molecule. As such, a Raman spectrum provides
a "fingerprint" for the presence of various molecular species.
Raman spectroscopy can be used for both qualitative identification
and quantitative determination of molecular species.
[0010] Raman spectra have been observed from various biological
tissues including skin. Identified Raman scatterers in tissues
include elastin, collagen, blood, lipid, tryptophan, tyrosine,
carotenoid, myoglobin, nucleic acids etc. Raman spectroscopy has
also been used to monitor cutaneous drug delivery and
pharmacokinetics during skin disease treatment. It has been used to
monitor blood analytes, e.g. glucose, lactic acid, and urea, in
blood samples.
[0011] Most studies which have investigated the Raman spectra of
tissues have investigated ex vivo tissue samples using
Fourier-Transform (FT) Raman spectrometers. FT-Raman systems take
up to 1/2 hour to acquire a spectrum and are bulky and not
portable, and therefore are of limited clinical utility. Recently
developed dispersive type Raman systems based on fiber optic light
delivery and collection, compact diode lasers, and high efficiency
spectrograph-detector combinations, have shortened the time
required to obtain a Raman spectrum to minutes or sub-minutes.
[0012] In addition to scattering and reflecting light, tissues can
also absorb light and emit the absorbed energy in the form of
fluorescent light that is of a longer wavelength than the incident
light. Such "autofluorescence" signals are weak but can be
detected. Fluorescence excitation and emission studies of tissues
are usually performed in the ultraviolet and visible wavelength
ranges.
[0013] Recently, some tissue autofluorescence studies have been
conducted at longer red to near infrared (NIR) wavelengths. Some
examples are Zhang G, et al., Far-red and NIR Spectral Wing
Emission from Tissues under 532 and 632 nm Photo-excitation Lasers
in Life Science 9:1-16, 1999 and Demos S G, et al. Tissue imaging
for cancer detection using NIR autofluorescence, Proceedings SPIE
4613:31-34, 2002.
[0014] A problem with the evaluation of pigmented lesions,
including melanoma and its precursors, by reflectance or visible
fluorescence techniques is that melanin is a strong light absorber
throughout the ultraviolet and visible spectrum. Both incident and
reflected or re-emitted (fluorescent) photons in this wavelength
range are largely absorbed by melanin. This results in weak spectra
and "black hole" images that provide little clinically useful
information.
[0015] Richards-Kortum et al., U.S. Pat. No. 6,095,982; discloses
the use of a combination of fluorescence and Raman spectroscopy in
detecting pre-cancers and other abnormalities in tissue. The
fluorescence measurements are made in the ultraviolet while the
Raman spectroscopy measurements are made in the infrared.
Richards-Kortum et al, U.S. Pat. Nos. 5,991,653; 5,697,373;
5,612,540 and 6,258,576 disclose similar methods.
[0016] Verma U.S. Pat. No. 4,832,483 discloses a method for using
Raman spectroscopy for the detection of cancers. Georgakoudi et al.
U.S. Pat. No. 6,697,652 disclose a method for evaluating tissue
using multiple spectroscopic techniques including fluorescence,
reflectance and light scattering spectra. Nordstrom et al. U.S.
Pat. No. 6,385,484 discloses the use of fluorescence spectra and
reflectance spectra for classifying tissue specimens. Tumer et al.
U.S. Pat. No. 6,135,965 discloses the use of neural networks to
identify spectra corresponding to abnormal tissues.
[0017] Alfano et al. U.S. Pat. No. 5,293,872 relates to methods
which include the use of Raman spectroscopy for distinguishing
between calcified atherosclerotic tissue and fibrous
atherosclerotic tissue. Alfano et al., U.S. Pat. No. 5,131,398
discloses a method which uses native fluorescence for
distinguishing cancerous tissue from benign tumour tissue. Alfano
et al., U.S. Pat. No. 5,261,410 discloses a method for using Raman
spectroscopy for determining whether a tissue is a malignant tumour
tissue, a benign tumour tissue or a normal tissue. Alfano et al.,
U.S. Pat. No. 5,369,496 discloses the use of back-scattered light
for evaluating tissue samples.
[0018] Puppels et al., WO 2004/051242 discloses the use of
high-wavenumber Raman spectroscopy for detecting abnormalities in
tissue. Haaland et al., U.S. Pat. No. 5,596,992 discloses the use
of multivariate classification techniques applied to infrared
spectra from cell and tissue samples. Gellermann et al. U.S. Pat.
No. 6,205,354 discloses the use of Raman spectroscopy for detection
of carontenoids. Lin et al., U.S. Pat. No. 6,377,841 disclose the
use of fluorescence and diffuse reflectance spectra for detecting
the boundaries of brain tumours. Garfield et al., U.S. Pat. No.
5,450,857 discloses the use of fluorescence spectra for measuring
cervical dilation. Boppart et al. U.S. Pat. No. 6,485,413 discloses
a instrument which can be used for collecting various spectra
including fluorescence spectra and Raman spectra.
[0019] Empirically determined diagnostic algorithms based on the
determined peak intensities, widths, and/or peak ratios of tissue
spectra have been described in literature for evaluating variations
in tissue spectra with tissue pathology. Some examples are
Mahadevan-Jansen A, and Richards-Kortum R. Raman spectroscopy for
the detection of cancers and precancers, J Biomed Opt 1996; 1,
31-70; Mahadevan-Jansen A, et al. Near-infrared Raman spectroscopy
for in vitro detection of cervical precancers Photochem Photobiol
1998; 68:123-132; and, Huang Z, et al., Near-infrared Raman
spectroscopy for optical diagnosis of lung cancer, Int J Cancer,
2003; 107:1047-1052.
[0020] Multivariate statistical techniques have been applied for
similar purposes. Examples include: Bakker Schut T C et al. In vivo
detection of dysplastic tissue by Raman spectroscopy Anal Chem
2000; 72:6010-6018; Mahadevan-Jansen A, et al. Near-infrared Raman
spectroscopy for in vitro detection of cervical precancers
Photochem Photobiol 1998; 68:123-132; Stone N, et al. Near-infrared
Raman spectroscopy for the classification of epithelial pre-cancers
and cancers, J Raman Spectrosc 2002; 33: 564-573; Deinum G, et al.,
Histological classification of Raman spectra of human coronary
artery atherosclerosis using principal component analysis, Appl
Spectrosc 1999; 53:938-942; and, Silveira L Jr et al., Correlation
between near-infrared Raman spectroscopy and histopathological
analysis of atherosclerosis in human coronary arteries, Lasers Surg
Med 2002; 30:290-7.
[0021] To date, none of the diagnostic methods described in the
publications listed above have been widely adopted for use in
tissue screening.
[0022] Despite the large amount of research that has been done in
the area, there remains a need for fast, accurate cost-effective
methods and apparatus capable of screening for tumours or other
cancerous lesions.
SUMMARY OF THE INVENTION
[0023] One aspect of this invention provides methods for
characterizing tissues. The methods may provide an indication as to
whether or not a section of tissue is likely abnormal. The methods
comprise obtaining features of a Raman spectrum of the tissue in a
first wavelength range; obtaining features of a background
fluorescence spectrum of the tissue in a second wavelength range
that overlaps with the first wavelength range and characterizing
the tissue based upon at least the Raman spectrum features and the
background fluorescence spectrum features. The characterization may
be performed, for example, by applying a classification function or
supplying the features of the Raman and background autofluorescence
spectra to a neural network. Suitable classification functions may
be derived, for example, by performing PCA (Principal Components
analysis) and LDA (Linear Discriminant Analysis) on reference
data.
[0024] Another aspect of the invention provides methods for
determining melanin content of tissues. The methods comprise
obtaining a NIR spectrum of the tissue, the spectrum including
first and second peaks at wavenumbers of approximately 1368
cm.sup.-1 and 1572 cm.sup.-1; subtracting a background of the
spectrum to yield a Raman spectrum; and computing the melanin
content of the tissue based upon intensities of at least one of the
first and second peaks of the Raman spectrum.
[0025] Further aspects of the invention and features of specific
embodiments of the invention are described below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] In drawings which illustrate non-limiting embodiments of the
invention:
[0027] FIG. 1 is a block diagram of an apparatus that may be used
for acquiring Raman and background fluorescence spectra;
[0028] FIG. 1A is a schematic block diagram of the probe of the
apparatus of FIG. 1;
[0029] FIG. 2A shows curved spectral lines at the output of a
spectrograph;
[0030] FIG. 2B shows lines at the output of a spectrograph which
has been modified to correct for line curvature;
[0031] FIG. 3 shows Raman spectra of human skin for several binning
modes;
[0032] FIG. 4 is a flowchart illustrating a method of the
invention;
[0033] FIG. 5 is a microphotograph of a tumour in a mouse used as a
test subject;
[0034] FIGS. 6A, 6B and 6C are respectively a raw spectrum from
mouse tumour tissue; a background autofluorescence component of the
raw spectrum of FIG. 6A and a Raman component of the raw spectrum
of FIG. 6A;
[0035] FIGS. 6D, 6E and 6F are respectively mean differences
between tumor and normal tissues among individual mice
corresponding to the raw spectrum and spectral components of FIGS.
6A, 6B and 6C respectively;
[0036] FIGS. 7A, 7B and 7C are respectively plots of principle
components for Raman, background fluorescence, and raw spectra;
[0037] FIGS. 8A, 8B and 8C are scatter plots of the two most
diagnostically significant principal components for Raman,
background fluorescence and raw spectra respectively;
[0038] FIGS. 9A, 9B, 9C and 9D are plots of posterior probabilities
of belonging to normal and tumor groups calculated respectively for
Raman, background autofluorescence, raw spectra, and combined Raman
PC scores and NIR background fluorescence PC scores;
[0039] FIG. 10 is a set of receiver operating characteristic curves
generated for Raman, background autofluorescence, raw spectra, and
combined Raman PC scores and NIR background fluorescence PC scores
at different threshold levels;
[0040] FIG. 11 shows Raman spectra of various skin areas, both
cancerous and not cancerous;
[0041] FIG. 12 is a scatter plot relating two ratios of
lipid-to-protein Raman bands showing clustering behaviour for Raman
spectra acquired at different locations on subjects' bodies;
[0042] FIG. 13 shows variation among Raman spectra of skin taken at
the same body location in a number of different subjects;
[0043] FIG. 14 shows Raman data for melanin;
[0044] FIGS. 14A, 14B and 14C are respectively raw spectrum from
tumor tissue compared to normal tissue, a principle components plot
for raw spectra, and a scatter plot of the two most diagnostically
significant principle components for raw spectra;
[0045] FIG. 15 compares NIR background fluorescence spectra for
normal and nevus tissue;
[0046] FIGS. 15A, 15B and 15C are respectively a background
autofluorescence component of a raw spectrum of tumor tissue, a
plot of principle components for background fluorescence spectra,
and a scatter plot of the two most diagnostically significant
principal components for background fluorescence spectra;
[0047] FIG. 16 compares visible fluorescence spectra for normal and
nevus tissues;
[0048] FIGS. 16A, 16B and 16C are respectively a Raman component of
the raw spectrum of tumor tissue, a plot of principle components
for Raman spectra, and a scatter plot of the two most
diagnostically significant principal components for Raman
spectra;
[0049] FIG. 17 shows NIR spectra for several melanin samples;
and,
[0050] FIG. 18 compares NIR spectra for black and white hairs.
DESCRIPTION
[0051] Throughout the following description, specific details are
set forth in order to provide a more thorough understanding of the
invention. However, the invention may be practiced without these
particulars. In other instances, well known elements have not been
shown or described in detail to avoid unnecessarily obscuring the
invention. Accordingly, the specification and drawings are to be
regarded in an illustrative, rather than a restrictive, sense.
[0052] This invention provides methods for detecting abnormalities
in tissues. Methods according to the invention illuminate a section
of tissue under investigation and acquire a spectrum in a
wavelength range which includes both Raman features and background
fluorescence. Both the Raman features and background fluorescence
are used as a basis for evaluating whether or not the section of
tissue under investigation is likely to be abnormal. Specific
embodiments of the invention may be applied to screening skin or
other tissues, such as lung tissues, epithelial tissues, such as
the lining of the digestive tract, tissues of internal organs, or
other tissues for cancers. The methods of the invention may be
applied to tissues in vivo. The methods may also be applied in
vitro.
[0053] The section of tissue may be, for example: [0054] an area of
skin of a subject, [0055] a section of a piece of tissue obtained
from a biopsy or surgery, [0056] a section of lung or other tissue
from which a spectrum can be obtained using an endoscopic
instrument, or [0057] a section of tissue that has become exposed
during surgery.
[0058] In some embodiments of the invention the wavelength range
covers a portion of the spectrum in the near infrared (NIR). In
some embodiments of the invention the wavelength range spans at
least from about 800 nm to about 1000 nm.
[0059] Determining whether or not a spectrum from a section of
tissue under investigation indicates that the tissue may be
abnormal may involve statistical analysis comparing the measured
spectrum to reference data. The reference data may include or be
based upon reference spectra taken of tissues which are known to be
normal and/or abnormal. The reference data may be taken from
tissues which are known to be normal or abnormal on the basis of
reliable diagnostic techniques such as histopathological diagnosis.
The comparison of the measured spectrum to the reference data may
involve applying a principal components analysis (PCA) and linear
discriminant analysis (LDA) to the reference data as in the
examples given below. In the alternative, or additionally, features
from the Raman and background fluorescence spectra may be provided
to a neural network which has been trained to identify and/or
characterize abnormal tissue samples based at least in part on the
features of the Raman and background fluorescence spectra.
[0060] The inventors have observed that in vivo tissue NIR
autofluorescence excited by 785 nm laser light exhibits trends
different from shorter wavelength visible tissue autofluorescence
between normal and diseased tissue. For example, skin affected by
vitiligo has lower NIR fluorescence but higher visible fluorescence
than surrounding normal skin, while skin affected by compound nevus
has higher NIR fluorescence but lower visible fluorescence than
surrounding normal skin (See FIGS. 15 and 16). A major difference
between vitiligo and normal skin as well as between compound nevus
and normal skin is the amount of melanin. Skin affected by melanoma
also exhibits increased NIR autofluorescence as compared to
surrounding normal skin. The inventors have also observed increased
NIR autofluorescence emission in human skin squamous cell
carcinoma. In contrast, skin basal cell carcinoma exhibits lower
NIR autofluorescence emission than its surrounding normal skin.
These differences in the NIR spectra between normal and abnormal
tissues can be exploited in combination with features of Raman
spectra of the tissues to characterise skin and other tissues.
[0061] Other modalities may optionally be combined with features
from the Raman and background fluorescence spectra to improve the
accuracy (e.g. the specificity and/or sensitivity) of the results
obtained using Raman and background fluorescence spectra alone. For
example, a melanin content of the section of tissue may be used as
an additional feature. Raman spectroscopy may be used to measure
the melanin content of a tissue, as described below. The additional
modalities may include one or more modalities such as: [0062] UV or
visible fluorescence spectra; [0063] diffuse reflectance spectra;
[0064] light scattering spectra, which measure the scattering
properties of tissue as a function of wavelength; and, [0065]
differences between one or more Raman and/or NIR background
autofluorescence features of a spectrum of the tissue being
investigated and corresponding features of normal tissue of the
same patient.
[0066] The inventors have discovered that for Raman and
fluorescence spectra in the NIR, where the section of tissue is
skin, the spectra of normal tissues depends upon the location on
the subject's body of the section of tissue. For example, normal
skin of the hands tends to exhibit similar spectral characteristics
among different subjects. In contrast, NIR/Raman spectral
characteristics of normal skin of the hands, head, arms and trunk,
and thighs tend to be different from one another, even on the same
subject. In some embodiments of the invention, classification
functions are derived from reference spectra for the same body area
as the body area in which the section of tissue under investigation
is located. Some embodiments of the invention provide a plurality
of classification functions each derived from a different set of
reference data, each associated with a different body area. For
example, a set of reference data may be provided for each of two or
more of: the hands; the head; the arms and torso; and the
thighs.
Apparatus
[0067] Any suitable apparatus may be used to acquire Raman and
background fluorescence spectra of tissue in a desired wavelength
range. Where the methods of the invention are to be used for in
vivo screening it is generally desirable that the apparatus be
capable of acquiring the Raman and background autofluorescence
spectra reasonably quickly and that the apparatus not be unduly
bulky.
[0068] FIG. 1 is a block diagram of apparatus 10 that may be used
to acquire a Raman and background fluorescence spectrum. Apparatus
10 may be constructed as described in U.S. Pat. No. 6,486,948 and
Huang Z. et al. Rapid near-infrared Raman spectroscopy system for
real-time in vivo skin measurements, Opt Lett 2001; vol. 26: pp.
1782-1784 which are hereby incorporated herein by reference.
Apparatus 10 includes a light source 12, typically a monochromatic
light source, most typically a laser. In currently preferred
embodiments of the invention, light source 12 emits light in the
NIR (600 nm to 1200 nm). In an example embodiment, light source 12
is a laser diode that emits light having a wavelength of 785 nm. In
a prototype embodiment of the invention, light source 12 is a 300
mW laser diode emitting light at 785 nm of the type available from
SDL Inc. of San Jose, Calif.
[0069] Light from light source 12 is delivered to a probe 14
through an optical fiber 16. In the prototype, optical fiber 16 is
a 200 .mu.m core diameter fiber having a numerical aperture ("NA")
of 0.22. As shown in FIG. 1A, probe 14 includes a collimator 18 and
a bandpass filter 19 which ensures that light directed onto tissue
S is essentially monochromatic. In the prototype, bandpass filter
19 has a passband of 785 nm.+-.2.5 nm. A lens 20 focuses the
monochromatic light onto tissue S. In the prototype, lens 20
provides a spot size of 3.5 mm. A shutter (not shown) may be
mounted at the laser output port of laser 12. The shutter may be
kept closed except during the acquisition of spectral data to
ensure that the subject's skin is exposed to laser light only as
necessary to acquire data.
[0070] Light which has been backscattered from tissue S is focused
by lenses 22A, and 22B into a fiber optic bundle 24. A notch filter
28 blocks light which is outside of a wavelength range of interest.
In the prototype, filter 28 is a holographic filter having optical
density ("OD")>6.0 at 785 nm.
[0071] Fiber optic bundle 24 carries the backscattered light to a
spectrometer 26. To enhance the detection sensitivity, the fiber
optic bundle 24 used in the prototype includes as many fibers as
could be imaged onto the light sensor of spectrometer 26. In the
prototype bundle 24 has 58 100 .mu.m fibers arranged at its input
end at probe 14 in a circular shape having a diameter of 1.6 mm and
arranged in a generally linear array at its output end at the
entrance of spectrograph 26. The prototype had a 50 .mu.m
calibration fiber 27 located at the center of the output linear
array. Light of a known wavelength can be delivered to spectrometer
26 by way of calibration fiber 27 for wavelength calibration of
spectrometer 26.
[0072] In the prototype, spectrometer 26 is a HoloSpec.TM. f/2.2
NIR spectrometer equipped with a volume phase technology (VPT)
holographic grating model HSG-785-LF available from Kaiser Optical
Systems, Inc. of Ann Arbor, Mich. USA. Spectrometer 26 includes a
light detector, such as a CCD camera 30. In the prototype, camera
30 is a 1024.times.256 pixel liquid-nitrogen-cooled, NIR-optimized,
back-illuminated, deep-depletion, CCD detector model No.
LN/CCD-1024EHRB QE 75% at 900 nm, available from Princeton
Instruments, of Trenton, N.J., USA. Camera 30 provides an output to
a computer system 32.
[0073] The Raman spectra and associated autofluorescence background
may be displayed on a display 33 of computer system 32 in real time
and may be saved for further analysis. The prototype system
acquires spectra over the wavenumber range of 800-1800 cm.sup.-1 (a
wavelength range of 838-914 nm).
[0074] Raman frequencies may be calibrated using materials having
known Raman peaks in the spectral region of interest. For example
the prototype system has been calibrated using the spectra of
cyclohexane, acetone, and barium sulfate to an accuracy of 2
cm.sup.-1. The spectral resolution of the prototype system is 8
cm.sup.-1. All wavelength-calibrated spectra of the prototype
system were also corrected for the wavelength-dependent response of
the system using a standard lamp (model RS-10 available from
EG&G Gamma Scientific, San Diego, Calif., USA).
[0075] The image of a straight slit through a spectrograph that
uses a planar grating has a curved parabolic line shape. This image
aberration arises from the fact that rays from different positions
along the length of the slit are incident on the grating at varying
degrees of obliqueness. For spectrographs with short focal lengths,
this obliqueness can cause significant distortion that can affect
the measurement performance of the detector. For example, FIG. 2A
shows the image aberration of a straight 100 .mu.m slit through a
spectrograph like the one used in the prototype system when
illuminated by an Hg--Ar lamp. The curvature of the spectral lines
is apparent in FIG. 2A. In the prototype system this curvature can
be described by:
x=1.1904E-5y.sup.2+1.9455E-4y-0.98613 (1)
where x is the horizontal displacement of the line at a vertical
position, y. The coefficients in Equation (1) are specific to the
prototype system.
[0076] This image aberration presents two impediments to hardware
binning of CCD columns: (1) it decreases the spectral resolution;
and (2) it decreases the signal to noise ratio ("S/N") achievable.
It also causes problems with wavelength calibration. "Hardware
binning" is binning of intensities detected by CCD pixels performed
before signal read-out by the preamplifier. For signal levels that
are readout noise limited, such as for weak Raman signal
measurements, hardware binning can improve S/N linearly with the
number of pixels grouped together. Binning can also be done using
software after the signal is read out. However, "software binning"
improves the S/N only in proportion to the square root of the
number of pixel values added together. Hence, complete hardware
binning of an entire line is preferable to software binning for
maximizing S/N. Combinations of hardware and software binning may
also be used.
[0077] In the prototype, the image aberration discussed above was
corrected by arranging 58 100 .mu.m fibers of fiber bundle 24 along
a curved line at the entrance of spectrograph 26. The curved line
was formed by laser drilling holes in a stainless steel cylinder
piece. The shape of the curved line corresponds to the horizontal
displacement shown in Equation (1) but in the reverse direction.
FIG. 2B shows a resulting CCD image of the output of spectrograph
26 with the fiber bundle illuminated by an Hg--Ar lamp. The central
dark spots in each of the spectral lines of FIG. 2B correspond to
the calibration fiber 27 that was not illuminated. The spectral
lines are substantially straight, indicating effective image
aberration correction. This permits each entire CCD vertical line
(256 pixels in the prototype) to be hardware binned without losing
resolution or reducing S/N.
[0078] Using the prototype system, an in vivo skin Raman spectrum
can be obtained in less than 1 second. The illumination power
density is 1.56 W/cm.sup.2, which is less than the ANSI maximum
permissible skin exposure limit of 1.63 W/cm.sup.2 for 785 nm laser
light.
[0079] Lines 100, 101 and 102 of FIG. 3 each show a Raman spectrum
of the skin of a subject's palm. Line 100 is a spectrum resulting
from the use of complete software binning. Line 101 is a spectrum
acquired with combined hardware and software binning. Line 102 is a
spectrum acquired using hardware binning. For all of lines 101, 101
and 102, a CCD integration time of 0.5 second was used. The S/N of
the spectrum of line 102 can be observed to be significantly better
than that of line 101 and is much better than that of line 100. The
Raman peak at 1745 cm.sup.-1 (from the C.dbd.O stretching band of
lipid ester carbonyl) is barely visible in line 100, appears as a
noisy small peak in line 101 and appears as a smooth well defined
peak in line 102.
Overview of Method
[0080] FIG. 4 shows a method 50 according to an embodiment of the
invention. Method 50 begins in block 52 by acquiring reference
data. The reference data may be optical spectra of tissue samples.
The reference samples may include tissues which are known to be
normal and/or tissues which are know to be cancerous or otherwise
abnormal. Suitable apparatus, for example, apparatus like that
described above, is used to acquire the reference spectra. In block
54 a classification function is generated. The classification
function takes as inputs features of a test spectrum and produces
an output indicative of whether or not the tissue corresponding to
the test spectrum is likely to be normal or abnormal. In the
illustrated embodiment, block 54 includes performing Principal
components analysis (PCA) (block 54A) and performing linear
discriminant analysis (LDA) (block 54B).
[0081] Principal component analysis PCA and LDA are known data
analysis techniques. PCA and LDA are described in various reference
works including: Dillion R W, Goldstein M, Multivariate analysis:
methods and applications, John Wiley and Sons, New York, 1984; and
Devore J L, Probability and statistics for engineering and the
science, Brooks/Cole, Pacific Grove. 1992.
[0082] In block 56, a test spectrum is acquired. The test spectrum
may be acquired using suitable apparatus such as that described
above. The test spectrum is of a section of tissue. The section of
tissue may, for example, be an area of skin that has been
identified as having an appearance that could possibly indicate
cancer. In block 58 the test spectrum is compared to the reference
data. In the illustrated embodiment, this comparison involves
applying the classification function generated in block 54 to
features of the test spectrum in block 58A. The features include
both features of a Raman component of the test spectrum and
features of a background fluorescence component of the test
spectrum. The term background fluorescence is used herein to mean
fluorescence in a wavelength range that includes peaks of a Raman
spectrum.
[0083] In block 60 an output measure is provided. The output
measure indicates a likelihood that the tissue section is normal or
abnormal. The output measure may comprise any suitable indicator
including one or more of: [0084] a graphical or textual value
indicating a likelihood that the tissue section is normal or
abnormal; [0085] a warning indicator, such as a warning light;
[0086] graphical or textual information indicating a class into
which the tissue section has been classified; or [0087] other
suitable indicators.
[0088] Certain implementations of the invention comprise computer
processors which execute software instructions which cause the
processors to perform a method of the invention. For example, one
or more processors in a computer system may implement the method of
FIG. 4 by executing software instructions in a program memory
accessible to the processors. The invention may also be provided in
the form of a program product. The program product may comprise any
medium which carries a set of computer-readable signals comprising
instructions which, when executed by a data processor, cause the
data processor to execute a method of the invention. Program
products according to the invention may be in any of a wide variety
of forms. The program product may comprise, for example, physical
media such as magnetic data storage media including floppy
diskettes, hard disk drives, optical data storage media including
CD ROMs, DVDs, electronic data storage media including ROMs, flash
RAM, or the like or transmission-type media such as digital or
analog communication links. The instructions may optionally be in
an encoded, encrypted and/or compressed format.
Application Example #1
[0089] The diagnostic performance of NIR autofluorescence, Raman,
and composite Raman and NIR autofluorescence (raw spectra)
spectroscopy for in vivo tissue classification were studied using
as a model a murine Meth-A fibrosarcoma model involving syngeneic
BALB/c mice. Seven- to nine-week old female BALB/c mice each
weighing 18-28 g were implanted subcutaneously with
1.times.10.sup.6 Meth-A fibrosarcoma cells on the lower back.
Tumors thus induced grew to approximately 5-6 mm in diameter at 7
days after inoculation, and were located approximately 200 .mu.m
beneath the skin surface (FIG. 5). For spectroscopic studies, the
hair on the lower back of the mice was shaved, and the mice were
immobilized in a holder designed to expose their back skin for
spectroscopy measurements. Spectra were acquired in a pair-wise
fashion from each mouse by measuring a tumor-bearing site and the
normal-appearing skin approximately 5 cm away from the lateral
border of the tumor.
[0090] The raw spectra acquired from tissue in the 800-1800
cm.sup.-1 Raman shift range included a prominent tissue
autofluorescence component and a weaker tissue Raman scattering
component, as shown in FIG. 6A. The raw spectra were preprocessed
by adjacent 5-point smoothing to reduce noise. A fifth-order
polynomial was fit to the broad autofluorescence background in the
noise-smoothed spectrum (FIG. 6B). This polynomial, which
essentially represents NIR autofluorescence was then subtracted
from the raw spectrum to yield the tissue Raman spectrum alone
(FIG. 6C). The following three data sets were thus produced:
[0091] Raman (i.e., background-subtracted spectra),
[0092] background autofluorescence alone (i.e., the 5.sup.th order
polynomial), and,
[0093] raw spectrum (composite Raman and NIR background
autofluorescence spectra). statistical analysis was performed using
each of these data sets.
[0094] The entire spectral range (800-1800 cm.sup.-1 Raman shift)
was used for principal components analysis (PCA). Each spectrum was
represented as a set of 497 intensities (PCA variables). To
eliminate the influence of inter- and/or intra-subject spectral
variability on PCA, the entire spectrum was standardized so that
the mean of the spectrum was zero and the standard deviation of all
the spectral intensities was one. This standardization ensures that
the principal components (PCs) form an orthogonal basis.
[0095] The standardized data sets (i.e., Raman, autofluorescence,
and raw spectra) were assembled into three separate data matrices
with wavenumber (or wavelength) columns and a row for each
individual animal. PCA was performed on the three standardized
spectral data matrices to generate PCs comprising a reduced number
of orthogonal variables that accounted for most of the total
variance in the original spectra. Each PC is related to the
original spectrum by a variable called the PC score, which
represents the weight of that particular component against the
basis spectrum.
[0096] Paired two-sided student t-tests as described, for example,
in Devore J L, Probability and statistics for engineering and the
sciences, Brooks/Cole, Pacific Grove, 1992. were used to identify
diagnostically significant PC scores for each case using an alpha
of 5%. All statistically significant PC scores were retained and
then input into a LDA model for tissue classification.
[0097] LDA determines the discriminant function line that maximizes
the variance in the data between groups while minimizing the
variance between members of the same group. The performance of the
classification functions resulting by the LDA models was estimated
in an unbiased manner using the leave-one-out, cross-validation
method as described, for example, in Dillion R W and Goldstein M,
Multivariate analysis: methods and applications, John Wiley and
Sons, New York, 1984 and Lachenbruch P and Mickey R M, Estimation
of error rates in discriminant analysis, Technometrics 1968;
10:1-11. In this method, one spectrum was removed from the data set
and the entire algorithm including PCA and LDA was performed using
the remaining tissue spectra to produce a new classification
function. The new classification function was then used to classify
the withheld spectrum. This process was repeated until all withheld
spectra were classified. The results of this analysis indicated the
relative ability to correctly predict the status (i.e., tumor vs.
normal) based upon each of the model spectra.
[0098] To compare the performance of the PCA-LDA model for tissue
classification using the three spectroscopic data sets (Raman,
autofluorescence, and raw spectra), receiver operating
characteristic (ROC) curves were generated by successively changing
the thresholds to determine correct and incorrect classifications
for all subjects.
[0099] All multivariate statistical analyses were performed using
Factor Analysis and Stepwise Discriminant Analysis modules within
the BMDP statistical software package (Version 7.0, BMDP
Statistical Software, Inc., Los Angeles, Calif.).
[0100] On average, the raw spectra and the background
autofluorescence spectra showed higher signal intensities for tumor
than for normal skin, whereas the converse was true for Raman
scattering (see FIGS. 6A, 6B, and 6C). Compared to normal
surrounding tissue, tumor tissue was significantly associated with
an increased overall intensity of autofluorescence background
spectra (p<0.0001; paired student t-test on the mean differences
of spectral intensities point by point in the range 800-1800
cm.sup.-1 between normal and tumor tissue) (FIGS. 6A, 6B), whereas
normal skin exhibited higher Raman intensity than tumor tissue
(P<0.0001; paired student t-test) (FIG. 6C). Nevertheless, there
was significant variability for the spectrum differences across
separate animals as reflected in the mean difference spectra SD
(FIGS. 6D, 6E and 6F. These differences made it impractical to
differentiate between normal and tumor tissue using overall signal
intensities alone.
[0101] It can be seen that the raw spectrum of FIG. 6A is composed
of a small contribution of tissue Raman scattering superimposed on
a relatively intense autofluorescence background. The mean in vivo
Raman spectra for tumor and normal skin (FIG. 6C) showed similar
vibrational bands that were dominated by several prominent Raman
peaks. For instance, the Raman bands observed in both tumor and
normal skin at Raman shifts of 1655 cm.sup.-1, 1445 cm.sup.-1, 1300
cm.sup.-1, 1265 cm.sup.-1, and 1004 cm.sup.-1 are presumably
attributed to the protein amide I, CH.sub.2 bending modes, CH.sub.2
twisting modes, protein amide III, and phenyl ring breathing mode,
respectively. Tentative assignments of some Raman bands observed in
tumor and normal skin are summarized in Table 1.
TABLE-US-00001 TABLE I ASSIGNMENT OF RAMAN BANDS Peak position
(cm.sup.-1) Protein assignments Lipid assignments Others 1745w
.nu.(C.dbd.O) 1655vs .nu.(C.dbd.O) amide I (a-helix conformation,
collagen) 1620w .nu. (C.dbd.C) porphyrin 1585vw .nu.(C.dbd.C)
olefinic 1558vw .nu.(CN) and .delta.(NH) amide II .nu. (C.dbd.C)
porphyrin 1514 .nu. (C.dbd.C) carotenoid 1445vs .delta.(CH.sub.2),
.delta.(CH.sub.3) .delta.(CH.sub.2) scissoring 1379vw
.delta.(CH.sub.3) symmetric 1336mw (sh) .delta. (CH.sub.2), .delta.
(CH.sub.3), twisting, collagen 1302vs .delta.(CH.sub.2) twisting,
wagging, .delta.(CH.sub.2) twisting, collagen wagging 1265s
.nu.(CN) and .delta.(NH) amide III (a-helix conformation, collagen)
1208vw .nu.(C--C.sub.6H.sub.5) phenylalanine 1168vw .nu.(C.dbd.C),
.delta.(COH) .nu. (C--C), carotenoid 1122mw (sh) .nu..sub.s(CC)
skeletal 1078ms .nu.(CC) skeletal .nu.(CC),
.nu..sub.s(PO.sub.2.sup.-) nucleic acids 1030mw (sh) .nu.(CC)
skeletal, keratin 1004mw .nu.(CC) phenylalanine ring 973mw (sh)
r(CH.sub.3), .delta.(CCH) olefinic 935mw r(CH.sub.3) terminal,
proline, valine; .nu.(CC) a-helix keratin 883mw r(CH.sub.2) 855mw
.delta.(CCH) phenylalanine, polysaccharide olefinic .nu.,
stretching mode; .nu.s, symmetric stretch; .nu.as, asymmetric
stretch; .delta., bending mode; r, rocking mode; v, very; s,
strong; m, medium; w, weak; sh, shoulder
[0102] The shape of the background autofluorescence spectrum in the
range of 800-1800 cm.sup.-1 (i.e., 838-914 nm) can be seen to
differ between tumor and normal skin (FIG. 6B). The ratio of the
curves for normal and tumor tissue is not a flat horizontal line
but decreases from 800 cm.sup.-1 to 1350 cm.sup.-1 and then
increases until close to 1800 cm.sup.-1 (data not shown). Although
no distinctive differences in Raman peak positions were observed
between normal and tumor tissue, subtle differences in spectral
lineshapes were noted, especially at 1200-1400 cm.sup.-1 and
1500-1650 cm.sup.-1. PCA/LDA is one way to exploit such lineshape
differences for tissue classification.
[0103] FIGS. 7A through 7C show respectively the first five
principal components (PCs) loadings calculated from principal
component analysis (PCA) for:
[0104] Raman spectra;
[0105] background autofluorescence spectra; and,
[0106] raw spectra.
Overall, the PC features for each of the three spectral data sets
differ from those of the other spectral data sets. Some PC features
(FIGS. 7A, 7C) roughly correspond to Raman spectra, with peaks at
positions similar to those at which Raman peaks occur in skin
tissue. The first PC accounts for the largest variance within the
spectral data sets (e.g., 74.6% for Raman; 79.5% for
autofluorescence; 69.9% for raw spectra), whereas successive PCs
describe features that contribute progressively smaller
variances.
[0107] Paired two-sided student t-tests on the first five PC scores
comparing normal and tumor-bearing skin showed that there were only
three PCs (PC1, PC2, PC3 in FIG. 7A), two PCs (PC1, PC4 in FIG.
7B), and three PCs (PC1, PC3, PC4 in FIG. 7C) that were
diagnostically significant (p<0.0001) for discriminating normal
and malignant tissues. FIGS. 8A, 8B, and 8C show examples of
scatter plots of the most diagnostically significant PC scores for
normal and tumor tissue derived respectively from:
[0108] Raman (PC1 vs. PC2);
[0109] background autofluorescence (PC1 vs. PC4); and,
[0110] raw spectra (PC1 vs. PC3).
These Figures show that the spectra can be clustered into normal
and tumor groups using dotted lines that represent potential
diagnostic algorithms. In this case the dotted lines represent a
set of linear combinations of two PC scores that could be used as a
classification function.
[0111] LDA was used to generate classification functions using all
significant PCs for each of the 3 different spectral data sets.
Based on the statistically significant spectral features in each
data set, classification functions using PCA-LDA-based spectral
classification with leave-one-out, cross-validation method were
developed. Posterior probabilities were determined by calculating
the percentage of each group in the data set by LDA. The cost of
misclassifying normal skin as tumor was chosen to be 0.50 for the
maximal number of correctly classified tissue groups.
[0112] FIGS. 9A, 9B, 9C and 9D show the posterior probabilities of
belonging to the normal and tumor groups as calculated respectively
for: [0113] Raman; [0114] background autofluorescence; [0115] raw
spectra; and, [0116] a combination of the Raman spectrum PC scores
and background autofluorescence spectrum PC scores. The
classification results showed that 81.3% (13/16), 93.8% (15/16),
93.8% (15/16) and 93.8% (15/16) of tumor tissue are correctly
classified (diagnostic sensitivity) with a posterior probability
less than 0.50 using the four types of data (i.e., Raman; NIR
autofluorescence; raw spectra; and Raman spectra PC scores and NIR
autofluorescence spectra PC scores combined), respectively. The
diagnostic specificities are 100%, 87.5%, 100%, and 100%. Overall
diagnostic accuracies are 90.6%, 90.6% and 96.9% and 96.9% for the
Raman spectra, NIR background autofluorescence spectra, raw spectra
and Raman spectra PC scores and NIR Autofluorescence spectra PC
scores combined respectively. It is noteworthy that the raw
spectra, which includes both Raman and background fluorescence
components, has a better overall diagnostic accuracy than either
the Raman spectra or background autofluorescence spectra taken
alone.
[0117] To further evaluate and compare the performance of the
PCA/LDA-based classification functions derived from the four types
of data for in vivo tissue classification, receiver operating
characteristic (ROC) curves (FIG. 10) were generated from the
scatter plots in FIGS. 9A to 9D at different threshold levels. FIG.
10 shows the discrimination results using Raman, NIR
autofluorescence, raw spectra, and a Raman spectra PC scores and
NIR Background autofluorescence PC scores combined. A comparative
evaluation of the ROC curves indicates that either NIR
autofluorescence or Raman alone can be used for in vivo tissue
diagnosis with high diagnostic sensitivity and specificity.
However, of the classification functions derived from the four
types of data, it appears that the classification function derived
from the raw spectra (which is a composite of Raman and NIR
fluorescence spectra components) or the classification function
derived from combined Raman spectra PC scores and NIR background
autofluorescence PC scores can give the most effective diagnostic
capability for in vivo tissue classification. this is illustrated
by the improvement in the specificity and sensitivity. The
integration areas under the ROC curves are 0.951, 0.963, 0.994 and
1.0. for classification functions derived respectively from: NIR
background autofluorescence; Raman spectra; raw spectra; and Raman
spectra PC scores and NIR background autofluorescence PC scores
combined. The results suggest that the raw spectra, which contains
both Raman signatures and NIR autofluorescence signatures may
generate better diagnostic accuracy than either the Raman or NIR
background autofluorescence modalities taken alone.
[0118] Multivariate statistical analysis allows objective diagnosis
by retaining only those principal components that describes
inter-group differences. The information most useful for tissue
diagnosis is distributed only over a few PCs. For LDA models, the
discriminative information may be contained in the first 3-4 PCs.
PCA plots of significant PC scores (See FIGS. 8A to 8C) show that
the combination of tissue NIR autofluorescence and Raman spectra
correlate well with pathologic grouping.
[0119] PCs that describe most of the variance in the spectroscopic
data do not necessarily provide the most diagnostic utility. For
instance, for the background autofluorescence data set, one of the
most significant PCs (PC4) describes only 0.33% of the total
variance. While the inventors do not wish to be bound by any
specific theory of operation, this suggests that subtle
modifications in histochemistry precede and accompany significant
pathological changes to the tissue. Other PCs that explain only
very small amounts of the total variance but are diagnostically
significant were also found in the Raman and raw spectral data
sets.
[0120] The combination of PCA and LDA is a statistically powerful
tool for providing diagnostic tissue classification algorithms
having high diagnostic sensitivity and specificity based on
features of background autofluorescence and Raman spectra.
[0121] While the inventors do not wish to be bound by any
particular theory of operation, the favorable discriminant results
obtained by employing the raw spectra, which contain both
autofluorescence and Raman signatures might be explained as
follows: NIR autofluorescence has previously been treated as
useless background signals in the measured raw spectra but, as the
inventors have learned, the NIR autofluorescence enhances the
ability to differentiate tumor from normal tissue, and may be
useful for establishing the chemical identity of the NIR
fluorophores in tissue. The combination of Raman spectra, which
respond to vibrational modes in materials within tissues, with the
autofluorescence signals using PCA/LDA can be a powerful tool for
elucidating the biochemical structure and composition of tissue,
and thus may provide useful diagnostic capabilities for tissue
diagnosis.
[0122] The use of NIR Raman and NIR Background fluorescence spectra
as a diagnostic tool has advantages over diagnostic tools which
require a subject to be irradiated with UV light. Unlike UV
excitation light, NIR light is non-carcinogenic, and it is safe for
use in tissue diagnosis. Further, where both the incident light
used and the measured tissue autofluorescence and Raman light are
at NIR wavelengths, the light can penetrate deeper into the tissue
(e.g. up to about 1 mm) than light at other wavelengths. Therefore,
NIR autofluorescence and Raman spectroscopy are potentially useful
for the noninvasive in vivo detection of lesions located below the
surface of tissue. For example, lesions could be detected by NIR
autofluorescence imaging, and then characterized by Raman
spectroscopy.
Application Example #2
[0123] As shown in FIG. 11, some features of Raman spectra are
different between normal to benign (compound nevus) and malignant
(melanoma) skin diseases. Curve 104 is a Raman spectrum of the
volar forearm normal skin of a subject of African descent. Curve
105 is a Raman spectrum of a benign compound pigmented nevus. Curve
106 is a Raman spectrum of a malignant melanoma. Curve 107 is a
Raman spectrum of normal skin adjacent the melanoma of curve 106.
One can see significant differences between these curves. The 1445
cm.sup.-1 peak is not visible in the malignant melanoma spectrum
106 but can be seen in both the normal black skin spectrum 104 and
the benign compound nevus spectrum 105. The 1269 cm.sup.-1 peak is
present in the malignant melanoma spectrum 106 and in the normal
black skin spectrum 104 but not in the benign compound nevus
spectrum 105. Features of these curves may be used together with
features of NIR autofluorescence which forms a background to these
curves in the raw spectra from which these curves are extracted in
a classification method according to this invention.
Application Example #3
[0124] Some methods of the invention provide a plurality of
classification functions. Such methods may involve selecting the
one of the classification functions most appropriate for
classifying the tissue section involved. For example, the
classification functions may include classification functions for
any one or more of: [0125] a number of different pathologies (such
as, for example, two or more of basal cell carcinoma (BCC),
squamous cell carcinoma (SCC), melanoma, actinic keratosis,
seborrheic keratosis, sebaceous hyperplasia, keratoacanthoma,
lentigo, melanocytic nevi, dysplastic nevi, and blue nevi); [0126]
a number of different tissue types (such as, for example, two or
more of skin, lung tissue, other epithelial tissues, such as the
bronchial tree, the ears nose and throat, the gastrointestinal
tract, the cervix, and the like); [0127] a number of different skin
types (for example, one classification function may be provided for
use with subjects having lightly pigmented skin and another
classification function may be provided for use with subjects
having more darkly pigmented skin; and, [0128] a number of
different locations on the body of the same general tissue type
(for example, as described below, different classification
functions may be provided for classifying skin for different areas
of a subject's body.
[0129] Where a plurality of classification functions are provided,
each of the classification functions may be derived from a set of
reference data for the tissue type/medical condition/tissue
location for which the classification function is intended to be
used. Apparatus according to the invention may include a user
interface which permits a user to select an appropriate one of a
plurality of classification functions.
[0130] The inventors have learned that, within the same subject,
the Raman spectrum of skin is typically significantly different for
different body sites. In some embodiments of the invention a
classification function is selected based on a body site in which a
tissue being studied is located. Raman spectroscopy measurements
were taken at each of 25 body sites for each on 50 normal
volunteers. FIG. 12 shows ratios of lipid-to-protein Raman bands
for the Raman spectroscopy measurements. It can be seen that these
ratios are clustered according to body sites. As shown in FIG. 13,
the inter-subject differences of skin Raman signals for a given
body site are relatively small, at least for subjects having
lightly pigmented skin.
[0131] This observation may be applied in the practice of this
invention by providing a plurality of different classification
functions each corresponding to a different body location. The
different classification functions may each be developed using
reference Raman and background fluorescence spectra obtained at the
corresponding body locations. The appropriate classification
function may be selected based upon the body location from which a
test spectrum is obtained. By way of example, different
classification functions may be provided for a plurality of
different body regions which may include, for example, the hands;
the head; the arms and torso; and the thighs. Classification
functions developed from reference data from other skin regions,
such as the feet, legs and nails may also be provided.
Application Example #4
[0132] A patient has a condition such as dysplastic nevus. The
condition causes many nevi at various locations on the patient's
body. The patient visits his physician who needs to decide whether
it is necessary to take a biopsy of any of the nevi and, if so,
which ones. There are enough nevi that it is not desirable or
practical to take biopsies of all of the nevi.
[0133] The physician obtains a NIR spectrum for each of the nevi to
be investigated. The NIR spectra include both Raman features and
NIR fluorescence features. The spectra may be obtained, for
example, with apparatus as described above and shown in FIG. 1. The
physician can place the probe of the apparatus on each nevus in
turn and then trigger the acquisition of a spectrum by activating a
control. For example, the physician may press a button when the
probe is over a nevus and then hold the probe over the nevus until
a spectrum has been acquired. The apparatus may generate a signal,
such as an audible beep, when the spectrum has been acquired. If
the apparatus is configured to take into account differences
between one or more Raman and/or NIR background autofluorescence
features of a spectrum of the nevus being investigated and
corresponding features of normal tissue of the patient then the
physician also obtains a spectrum from a portion of the patient's
skin which appears to be normal.
[0134] The apparatus either includes or is connected to a computer
system capable of applying classification functions to the spectra
acquired from various sites on the patient. Prior to applying the
classification function to the acquired spectrum, the physician may
use an interface provided by the computer system to select a
classification function appropriate for classifying dysplastic nevi
on the patient. The interface may prompt the physician to answer
questions to follow a decision tree resulting in selection of the
appropriate classification function. In the alternative, the
interface may permit the physician to directly select a
classification function or to input data on the basis of which the
computer system can select the most appropriate classification
function.
[0135] The computer system applies the classification function to
each of the acquired spectra. This may be done immediately after
acquiring one spectrum and before acquiring the next spectrum or in
a batch mode after a number of spectra have been acquired. For each
spectrum, the computer system provides an output signal indicative
of whether the classification function indicates that the
corresponding nevus is likely to be normal or is likely to be
abnormal. The output may comprise a visible or audible signal. The
output may be a simple output which simply indicates whether the
spectrum is indicative of normal tissue or suggests that the tissue
may be abnormal. In the alternative, the output may comprise
numeric and/or graphical information which indicates a likelihood
that the tissue from which the spectrum was taken is normal or
abnormal.
[0136] The physician can use the output to decide which of the
patient's nevi, if any, should be more thoroughly studied by way of
a biopsy or other procedure.
Application Example #5
[0137] A patient suspected of having lung cancer undergoes
bronchoscopy. A bronchoscope is equipped with an endoscopic probe
capable of acquiring a spectrum having Raman and background
fluorescence features. Suitable probes are described, for example,
in Zeng, U.S. Ser. No. 10/761,703 and PCT CA/04/00062. The
physician positions the tip of the bronchoscope adjacent a tissue
section of interest and triggers the apparatus to obtain a
spectrum. The apparatus applies a classification function to the
spectrum. The classification function is appropriate for lung
tissue. The classification function may have been developed from a
set of reference spectra including normal lung tissue and lung
tissue known to be cancerous. The apparatus provides an output. The
physician can use the output together with images acquired by the
bronchoscope to select locations for taking biopsy samples.
Application Example #6
[0138] In some cases, it is useful to measure the melanin content
of a tissue. The inventors have determined that melanin has broad
Raman peaks at Raman shifts of approximately 1380-1400 cm.sup.-1
and 1580-90 cm.sup.-1. These peaks can be detected in the Raman
spectra of human hair, which contains melanin (see FIG. 18). These
peaks may be used to measure melanin content of tissues.
[0139] Curves 104, 105, and 106 of FIG. 11 are Raman spectra of
tissues taken in vivo. These curves exhibit these peaks. FIG. 14
shows Raman data for melanin.
[0140] The spectra of cutaneous melanin-rich normal skin (curve
104) and pigmented lesions (curves 105 and 106) include two intense
and broad bands peaking near 1368 cm.sup.-1 and 1572 cm.sup.-1 that
are very similar to the Raman patterns observed in the melanin
samples of FIG. 14. In addition, the in vivo skin Raman spectra
exhibit vibrational bands for proteins and lipids that are
different in various skin that appeared dark due to melanin. For
instance, the weaker vibrational mode at 1742 cm.sup.-1 which
likely corresponds to C.dbd.O stretching of a lipid head group was
present in highly pigmented skin lesions, while other bands were
significantly reduced: e.g., the .nu. (C.dbd.O) amide I band at
1654 cm.sup.-1, the .delta. (CH.sub.3) and .delta. (CH.sub.2) at
scissoring mode at 1445 cm.sup.-1, the CH.sub.2 deformation at 1301
cm.sup.-1, and the .nu. (CN) and .delta. (NH) amide III bands at
1269 cm.sup.-1. The 1445 cm.sup.-1 peak disappeared in the
malignant melanoma spectrum but was observed in the benign compound
nevus spectrum, whereas the converse was true for the 1269
cm.sup.-1 peak. These differences as well as the peak positions and
bandwidths of the two melanin Raman bands may be included as
features and used for non-invasive melanoma detection in
embodiments of the invention.
[0141] In some embodiments of the invention, the 1368 cm.sup.-1
and/or 1572 cm.sup.-1 melanin peaks are used directly or indirectly
as an indicator of melanin concentration in a tissue specimen of
interest. The melanin concentration may be used as a feature for
tissue classification in addition to other features of the Raman
and background fluorescence spectra. The magnitude of these peaks
may be determined by subtracting the background. This may be
achieved by fitting a function to the background. The fitting
function should be a low order function such as a second-order
polynomial since these peaks are so broad that a higher order
fitting function will fit the peaks themselves.
[0142] There is a relationship between melanin concentration and
NIR background fluorescence. Although melanin fluoresces only
weakly in the visible band, melanin fluoresces more strongly in the
NIR wavelength range. Measurements on synthesized and extracted
melanin products from Sigma confirm strong NIR fluorescence
emission (see FIG. 17).
[0143] The 1368 cm.sup.-1 and 1572 cm.sup.-1 Raman bands may also
be used independently in methods for measuring the melanin content
of tissues. In such methods the intensities of one or both of these
peaks is determined. This may be done, for example, by subtracting
the background from the peaks as described above.
[0144] Where a component (e.g. a software module, processor,
assembly, device, circuit, etc.) is referred to above, unless
otherwise indicated, reference to that component (including a
reference to a "means") should be interpreted as including as
equivalents of that component any component which performs the
function of the described component (i.e., that is functionally
equivalent), including components which are not structurally
equivalent to the disclosed structure which performs the function
in the illustrated exemplary embodiments of the invention.
[0145] As will be apparent to those skilled in the art in the light
of the foregoing disclosure, many alterations and modifications are
possible in the practice of this invention without departing from
the spirit or scope thereof. For example: [0146] methods according
to the invention may optionally take into account features in
addition to features of Raman spectra and background fluorescence
spectra. For example, diffuse reflectance properties, ultraviolet
or visible fluorescence properties could also be included in the
analysis. [0147] Any suitable mathematical techniques may be used
to derive appropriate classification functions from reference data.
Such techniques may include discriminant function analysis,
logistic regression, multiple regression, or other suitable
statistical analysis techniques. [0148] While some of the examples
given above relate to classifying skin, the invention is not
limited to skin tissues but can equally be applied to other tissues
including epithelial tissues of internal surface organs, such as
the bronchial tree, the earns nose and throat, the gastrointestinal
tract, the cervix, and the like. A fiber probe may be used with an
endoscope to most easily obtain Raman and NIR background
fluorescence spectra for internal tissues. A compact fiber probe
suitable for obtaining Raman and NIR background fluorescence
spectra through an endoscope is described in international patent
application No. PCT/CA04/00062 entitled In vivo Raman endoscopic
probe and methods of use and in corresponding U.S. patent
application Ser. No. 10/761,703. [0149] Instead of acquiring an
essentially continuous spectrum containing Raman and background
fluorescence features, the invention could be practiced by
acquiring spectral information for a plurality of discrete
wavelengths or for a plurality of wavelength ranges. Accordingly,
the scope of the invention is to be construed in accordance with
the substance defined by the following claims.
* * * * *