Cancer diagnosis by refractive index multifractality

Abstract

A label-free optical device for near real time quantification of the multifractal micro-optical properties of a sample includes a source of broadband light; a tunable filter that receives at least a portion of the broadband light and then transmits narrowband light, whereby a specific band of light is selected to avoid unwanted absorption of light by the sample; where the narrowband light is configured to illuminate a selected area of the sample, and in response elastically-scattered light is dispersed from the sample; a light collection device configured to collect at least some of the elastically-scattered light; where at least some of the collected elastically-scattered light is configured to be transmitted to a detector by the light collection device, and the detector is configured to record a light scattering signal; and where the detector is configured to perform light scattering signal measurements at multiple angles or wavelengths to determine a refractive index multifractality of the sample.

Claims

1. A label-free optical device for near real time quantification of the multifractal micro-optical properties of a sample comprising: a source of broadband light; a tunable filter configured to receive at least a portion of the broadband light and then transmit narrowband light; whereby a specific band of light is selected to avoid unwanted absorption of light by the sample; wherein the narrowband light is configured to illuminate a selected area of the sample, and in response elastically-scattered light is dispersed from the sample; a light collection device configured to collect at least some of the elastically-scattered light; wherein at least some of the collected elastically-scattered light is configured to be transmitted to a detector by the light collection device, and the detector is configured to record a light scattering signal; and wherein the detector is configured to perform light scattering signal measurements at multiple angles or wavelengths to determine a refractive index multifractality of the sample.

2. The optical device of claim 1, wherein the multifractality determination includes Fourier domain preprocessing of a light scattering spectrum carried out to derive the spatial distribution of the refractive index multifractality of the sample.

3. The optical device of claim 1, wherein the multifractality determination includes Multifractal Detrended Fluctuation Analysis applied on Fourier domain pre-processed light scattering data to finally yield the multifractal micro-optical properties that include at least one of a generalized Hurst exponent, and width of the singularity spectrum.

4. The optical device of claim 1, wherein the multifractality determination includes analysis of measurements of at least one of angular dependence of the elastically-scattered light and wavelength dependence of the elastically-scattered light.

5. The optical device of claim 1, wherein the detector is a camera.

6. The optical device of claim 1, wherein the tunable filter is at least one of the items selected from the group consisting of a spectrometer, a filter wheel, a liquid crystal tunable filter, and an acousto-optic tunable filter.

7. The optical device of claim 1, further comprising a light delivery device configured to collect at least a potion of the scattered light from the sample and transmit that scattered light to the detector, wherein the light delivery device is at least one of the items selected from the group consisting of optical fiber and free-space optics.

8. The optical device of claim 1, wherein the light collection device is at least one of the items selected from the group consisting of an optical fiber and an objective lens.

Description

DESCRIPTION OF THE DRAWINGS

(1) The novel features believed characteristic of the application are set forth in the appended claims. However, the application itself, as well as a preferred mode of use, and further objectives and advantages thereof, will best be understood by reference to the following detailed description when read in conjunction with the accompanying drawings, wherein:

(2) FIG. 1 shows aspects of a flow chart outlining various steps used with an optical device of the present application used in proposed inverse analysis of refractive index multifractality from a tissue light scattering signal.

(3) FIG. 2 illustrates a schematic of the optic device of FIG. 1 for quantifying refractive index multifractality for cancer screening.

(4) FIG. 3A is a graph of the scattering spectra (intensity as a function of wavelength) from colon tissue acquired using the optical device of the present application.

(5) FIG. 3B is a graph of the Lamp normalized scattering spectra (intensity as a function of frequency) from the tissue.

(6) FIG. 4A is a graph of the discrimination of colon cancers from their normal counterpart based on the multifractal tissue optical properties, derived from white light scattering spectra, acquired and processed using the presently disclosed optic device and method.

(7) FIG. 4B shows the mapping of multifractality (width of singularity spectrum: Δα) in normal (left) and cancer (right) tissue sections, derived from white light scattering spectra, acquired and processed using the presently disclosed optic device and method.

(8) FIG. 5 is a chart of the optic device of the present application for cancer screening.

(9) FIG. 6 is a sample screen shot from developed software for the differentiation of different pathology grades using the multifractal tissue optical parameters derived from light scattering spectra.

(10) FIG. 7 is a chart showing an example of prediction analysis carried out by training the refractive index multifractal data using the optic device and method of the present application.

(11) FIG. 8 illustrates a software application for use with the optic device and method of the present application for cancer diagnosis.

(12) FIGS. 9A and 9B illustrate discrimination of different human (pre)cancers based on the light scattering-derived multifractal tissue optical properties determined using NanoSpectro Technology.

(13) FIG. 10A shows a NanoSpectro Vivo prototype for painless in-situ cancer diagnosis.

(14) FIG. 10B shows a Fiber probe with angled probe. FIG. 10C shows a Nanospectro device in action on human skin multifractality measurement. FIG. 10D shows Inter-subject and inner-subject variation of RI-MF parameters of skin measured by NanoSpectro-Vivo. N=10 different skin sites/healthy subject. Average±Standard Deviation. No statistical significant difference between the two healthy subjects.

(15) FIG. 11 is a chart showing measured optical biomarker for cancer detection matching well with pathological results obtained from patient's biopsy samples.

(16) FIG. 12 is a flow chart for multifractal parameter extraction of recorded elastically-scattered spectrum.

(17) FIGS. 13A-13D are graphs showing an example of extraction of tissue multifractality through inverse analysis.

(18) FIG. 14 is a graph of an example of the linear SVM based classification on the multifractal parameters extracted from elastic scattering spectra from wet colon tissue slices.

(19) FIG. 15 is a schematic of an Integrated Spectral-Spatial Multifractal Imaging System (ISSMIS) to achieve multifractal mapping of a sample using the optic device and method of the present application.

(20) While the device and method of the present application is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and are herein described in detail. It should be understood, however, that the description herein of specific embodiments is not intended to limit the application to the particular embodiment disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the process of the present application as defined by the appended claims.

DETAILED DESCRIPTION OF THE INVENTION

(21) Illustrative embodiments of the preferred embodiment are described below. In the interest of clarity, not all features of an actual implementation are described in this specification. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions must be made to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.

(22) In the specification, reference may be made to the spatial relationships between various components and to the spatial orientation of various aspects of components as the devices are depicted in the attached drawings. However, as will be recognized by those skilled in the art after a complete reading of the present application, the devices, members, apparatuses, etc. described herein may be positioned in any desired orientation. Thus, the use of terms to describe a spatial relationship between various components or to describe the spatial orientation of aspects of such components should be understood to describe a relative relationship between the components or a spatial orientation of aspects of such components, respectively, as the device described herein may be oriented in any desired direction.

(23) The device and method in accordance with the present application overcomes one or more of the above-discussed problems commonly associated with traditional approaches discussed above. In particular, the present invention provides a new clinical approach for in-situ, near real time quantification of the multifractal micro-optical properties (from the recorded tissue light scattering signal) for their use in the classification of different pathological grades of cancers/precancers. These and other unique features of the device are discussed below and illustrated in the accompanying drawings.

(24) The device and method will be understood, both as to its structure and operation, from the accompanying drawings, taken in conjunction with the accompanying description. Several embodiments of the device may be presented herein. It should be understood that various components, parts, and features of the different embodiments may be combined together and/or interchanged with one another, all of which are within the scope of the present application, even though not all variations and particular embodiments are shown in the drawings. It should also be understood that the mixing and matching of features, elements, and/or functions between various embodiments is expressly contemplated herein so that one of ordinary skill in the art would appreciate from this disclosure that the features, elements, and/or functions of one embodiment may be incorporated into another embodiment as appropriate, unless otherwise described.

(25) It is understood that terms such as “a” and “an” are defined as one or more unless this disclosure explicitly requires otherwise. The term “substantially” is defined as largely but not necessarily wholly what is specified (and includes what is specified; e.g., substantially 90 degrees includes 90 degrees and substantially parallel includes parallel), as understood by a person of ordinary skill in the art. In any disclosed embodiment, the terms “substantially,” “approximately,” and “about” may be substituted with “within [a percentage] of” what is specified, where the percentage includes 0.1, 1, 5, and 10 percent.

(26) Furthermore, the terms “comprise” (and any form of comprise, such as “comprises” and “comprising”), “have” (and any form of have, such as “has” and “having”), “include” (and any form of include, such as “includes” and “including”) and “contain” (and any form of contain, such as “contains” and “containing”) are open-ended linking verbs. As a result, an apparatus that “comprises,” “has,” “includes” or “contains” one or more elements possesses those one or more elements, but is not limited to possessing only those elements. Likewise, a method that “comprises,” “has,” “includes” or “contains” one or more steps possesses those one or more steps, but is not limited to possessing only those one or more steps.

(27) Any embodiment of any of the devices, systems, and methods can consist of or consist essentially of—rather than comprise/include/contain/have—any of the described steps, elements, and/or features. Thus, in any of the claims, the term “consisting of” or “consisting essentially of” can be substituted for any of the open-ended linking verbs recited above, in order to change the scope of a given claim from what it would otherwise be using the open-ended linking verb.

(28) The feature or features of one embodiment may be applied to other embodiments, even though not described or illustrated, unless expressly prohibited by this disclosure or the nature of the embodiments.

(29) Since, elastically scattered light from any scattering object contains complete information about the spatial frequency spectrum of the object, the signature of any self-similarity in spatial scaling of refractive index (RI) inhomogeneities can potentially be probed by light scattering measurements (either angular or wavelength dependence of scattered intensity). We found that given a tissue light scattering signal (intensity as a function of wavelength, for a fixed scattering angle), it is indeed possible to extract and quantify the multifractal properties (FIGS. 4A and 4B). It is understood that Higher grades of cancers are associated with increased anti-correlations of index fluctuations (increased roughness) leading to reduction in Hurst exponent (h(q=2)); and stronger multifractality (increased width of singularity spectrum: Δα).

(30) This inverse light scattering method is based on Fourier domain pre-processing of light scattering signal via the Born approximation, followed by the Multifractal Detrended Fluctuation Analysis (MFDFA), which finally yields the multifractal parameters, namely, the generalized Hurst exponent Hurst exponent h q and width of the singularity spectrum Δα (a measure of the strength of multifractality). Unlike the bulk tissue optical properties, the derived multifractal tissue optical properties contain much finer and subtle morphological information on tissue ultra structure (even sub-micron level changes in the index inhomogeneity distribution of tissue are encoded in these parameters).

(31) In the Born approximation of light scattering (which is well valid in low refractive index scattering media such as tissue), the scattered intensity (FIGS. 3A and 3B, as a function of wavelength for a fixed angle) is related to the Fourier transformation power spectra of the spatial variation of refractive index (RI). We make use of this to extract representative spatial distribution of index (in a statistical sense) using Fourier domain pre processing of light scattering intensity (as a function of wavelength or wave vector). This Fourier domain pre-processed signal is then subjected to the multifractal analysis.

(32) We consider a weakly fluctuating scattering medium in Born approximation for normalized RI fluctuations

(33) $\Delta n\left(r\right)~\frac{n\left(r\right)-n_0}{n_0},$
the expression for scattered intensity is given by
I(β)≈k.sup.4σ.sup.2|∫η(r)e.sup.i(β.Math.r)d.sup.3r|.sup.2  (1)

(34) Here, k=2π/λ, β is the scattering vector with modulus β=2k sin(θ/2), θ=180, is the scattering angle in backscattered mode, λ is the wavelength (β=2πν=spatial frequency); σ=n.sub.0δn is the index inhomogeneities strength and η(r) is spatial distribution of index inhomogeneities. The index inhomogeneities, which encodes the information of tissue multifractality can be obtained from the scattering signal as
η/(p)≈∫k.sup.−2√{square root over (I(β=2πν))}e.sup.i(β.Math.r)d.sup.3β  (2)

(35) Here, η/(p) contains the essential multifractal features of spatial index distribution in complex systems such as tissues. The encoded multifractal features in spatial index inhomogeneities are extracted by employing multifractal analysis. For this purpose, we have quantified the multifractal tissue optical properties by employing Multifractal Detrended Fluctuation Analysis (MFDFA) on the extracted spatial index inhomogeneities η/(p), using Fourier pre-processed light scattering intensities.

(36) The Multifractal Detrended Fluctuation Analysis (MFDFA) is a state of the art statistical tool capable of characterizing complex multi-affine processes and has been successfully deployed in diverse fields. Herein, we employ this method for characterizing refractive index multifractality by two multifractal parameters—(i) generalized Hurst exponent h q and (ii) Width of the singularity spectrum Δα (a quantitative measure of multifractality).

(37) A statistically monofractal series is one whose variance follows a power law described by a single global scaling exponent, known as Hurst exponent H (0<H<1). A statistically multifractal series exhibits many fractal subsets characterized by different local Hurst exponents depends on order of moment q. Multifractal Detrended Fluctuation Analysis (MFDFA) is a state of art statistical approach to characterize such complex self-similar processes. Briefly, the fluctuation profile Y(i) of index inhomogeneities (series of length N, i=1 . . . N) is divided into Ns=int (N/s) number of non-overlapping segments with equal length s. In each m.sup.th segment of the series (y.sub.m(i)), the local trend is determined by least square polynomial fitting. These trends are then subtracted from the corresponding profile to yield the detrended fluctuations. Then the variance of the detrended fluctuation is determined as

(38) $\begin{array}{cc}{F}^2\left(m,s\right)=\frac{1}{s}\underset{i=1}{\overset{S}{.Math.}}{\left[Y\left\{\left(m-1\right)s+i\right\}-y_m\left(i\right)\right]}^2& \left(3\right)\end{array}$

(39) The generalized moment (q) dependent fluctuation function is determined by q.sup.th order averaging the variances over all the segments as

(40) $\begin{array}{cc}{F}_q\left(s\right)={\left\{\frac{1}{2N_s}\underset{m=1}{\overset{2N_s}{.Math.}}{\left[F^2\left(m,s\right)\right]}^{\frac{q}{2}}\right\}}^{1/q}& \left(4\right)\end{array}$

(41) The q.sup.th order moment dependent scaling behavior is subsequently studied considering power law scaling behavior F.sub.q(s)˜s.sup.h(q). Following this approach, any non-stationary multifractal fluctuation can be characterized via two sets of multifractal parameter (i) The generalized Hurst exponent h(q), and classical multifractal scaling exponent τ(q) (ii) The singularity spectrum ƒ(α). These are related as

(42) $\begin{array}{cc}\tau \left(q\right)=\mathrm{qh}\left(q\right)-1& \left(5\right)\end{array}$$\begin{array}{cc}\alpha =\frac{d\tau }{\mathrm{dq}},f\left(\alpha \right)=q\alpha -\tau \left(q\right)& \left(6\right)\end{array}$

(43) Where α is the singularity strength and the full width of ƒ(α); Δα (defined at ƒ(α)=0) is a measure of multifractality.

(44) The present invention provides, in one aspect, a device (FIG. 2) which records elastically (back) scattered broadband (white) light spectrum from a small volume near the tip of the fiber probe. The invention further provides a method (FIG. 1) wherein the refractive index multifractality is quantified using the Fourier domain pre-processing and MFDFA approach.

(45) The multifractal properties are found to be highly sensitive in detecting colon cancerous alterations through an increase of multifractality (FIG. 4A/4B). In the higher grades of cancers, the refractive index fluctuations are found to be more anti-correlated (characterized by lower value of h q=2), and the strength of multifractality was observed to be considerably stronger (larger Δα). Reduction in the value for h q=2 with increasing pathology is attributed to increasing tissue roughness, or effectively the predominance of index inhomogeneities having smaller dimensions.

(46) The differences in the variations of h(q) between the normal and cancer tissues are more prominent for negative values of the moment q, which implies the relative importance of the small scale index fluctuations. This follows from the fact that negative values of the moment q influence the small fluctuations, whereas positive values influence large fluctuations. This also indicates that multifractal tissue optical properties capture subtle (otherwise hidden) changes in the index inhomogeneity (spatial) distribution of tissue (contributions of sub-micron level spatial index fluctuations) as signature of cancer.

(47) Increased multifractality (larger value of Δα) at higher grades of cancer (FIG. 4A/4B) is attributed to increased heterogeneity and the different scaling behavior of the small-scale and the large-scale index fluctuations.

(48) In certain cancer or disease type, deployment of supervised statistical classifier like SVM is required to classify the overlapping of multifractal parameters exist between normal and diseased tissue. Support vector machines (SVMs) are powerful statistical classifiers under supervised learning scheme. The central idea behind SVM operation is to separate classes with a surface that maximizes margin between them by avoiding overfitting to form an optimal separating hyperplane (OSH). Hence by following structural risk minimization (SRM) of statistical learning makes prediction on a function ƒ(x) as:

(49) $f\left(x\right)=\underset{i=1}{\overset{N}{.Math.}}{w}_{i}k\left(x,x_i\right)+w_0,$
where k (x, x.sub.i) is the kernel function defined on a basis function, {w.sub.i} is the corresponding model weights and w.sub.0 is the bias weight.

(50) The training data points lie far away from the OSH, does not participate in the specification and hence receives zero weight. Data point lies close to decision boundary receives non-zero weights. These training data points are ‘Support vectors’. If we remove these points, it will change the boundary location. Unlike relevance vector machine (RVM), there are restrictions while choosing of kernels in SVM.

(51) An appropriate selection of kernel function is an important aspect as it defines the accuracy level of SVM based operation while determining training data classification. The kernel function will produce optimum results in classification as long as it obeys the Mercer's theorem. In this paper, we reported the linear SVM as an art of classification as it provided optimum sensitivity, specificity and reduced error rate than polynomial and RBF-SVM. At polynomial order d=1, the simplest kernel for a linear classifier is defined as the dot product of support vector x.sub.i and the data set x in the input space as: k(x.sub.i,x)=x.sub.i,x+1. The feature space should be as same as that of N-dimensional input space in order to form a linear OSH.

(52) For example, a non-linear kernel like quadratic kernel i.e., d=2, can be expressed mathematically as: k(x.sub.i,x)=(x.sub.i,x+1).sup.2.

(53) Mathematically, the Gaussian RBF kernel is written as:
k(x.sub.i,x)=exp(−∥x.sub.i−x∥.sup.2/2σ.sup.2),

(54) where σ is the width of Gaussian. Varying σ values optimum classification results are obtained.

(55) Since the point (spectroscopic) measurement method typically probes ˜millimeter-sized regions of tissue, the derived multifractal tissue scattering properties is a statistical representation of the index inhomogeneity distribution over the probed tissue volume. For spatial mapping of the multifractal tissue optical properties of biopsied samples or in-vivo, the fiber probe (FIG. 2) is maneuvered to enable recording of spatially resolved light scattering spectra from tissue. Thus, RI-MF values measured by elastic light scattering signal are mapped in tissue slices (FIG. 4B).

(56) In another aspect, the present invention provides a graphical-user-interface software that allows near real-time determination of the multifractal parameters (h q=2, and Δα) from the recorded tissue light scattering signal.

(57) In another embodiment, the invention (software) includes classifiers (e.g. Hidden Markov model) for diagnostic classification of the different grades of precancers/cancers based on the multifractal parameters (FIGS. 6-8).

(58) Further, the invention includes a smartphone App for near real-time determination of the multifractal parameters. It provides a simple and easy-to-use interface with a single click “run” tab to start the diagnosis process once the probe is in contact with the biological sample.

(59) The entire system comprising of the experimental light scattering set-up, multifractal inverse analysis tool and the diagnostic algorithms is integrated in a user-friendly manner for invivo biomedical deployment. Rapid processing, portability and real time data analysis enable our device and method to be used in point-of-care (POC) settings.

(60) Below, the presently disclosed invention will be further described by way of examples, which are provided for illustrative purposes only and accordingly are not to be construed as limiting the scope of the invention.

EXAMPLES

Example 1

(61) In FIGS. 3A and 3B a representative spectrum acquired using the presently disclosed invention (device) is shown. FIG. 3A shows the Scattering spectra (intensity as a function of wavelength) from colon tissue acquired using the presently disclosed invention (device) while FIG. 3B shows the Lamp normalized scattering spectra (intensity as a function of frequency) from the tissue. Using the presently disclosed invention elastic scattering spectrum (ESS) is recorded from multiple sites in the tissue (FIG. 3A). The spectra are collected in the 400-700 nm spectral range. Based on the spectrum of the light source, the recorded spectrum is normalized (FIG. 3B). In one aspect, the present invention includes a flow chart outlining the various steps of the proposed inverse analysis of multifractality from ESS signal from tissues (FIG. 1). FIGS. 4A and 4B show illustrative examples of such multifractal analysis on Fourier domain preprocessed light scattering signal from a cancerous human colon tissue, confirmed by pathological examination. Refractive index multifractality (RI-MF) database for different cancer samples is now being classified based on the pathological result. This will allow establishment of RI-MF database.

Example 2

(62) The prototype to measure elastic scattering spectroscopy (ESS) is attachable/mountable on smart-phone and may utilize the in-built light source, camera, processing power, visual interface, data transportation and battery power of the smart-phone. FIG. 5 shows the schematic and picture of the smart-phone mounted device. With internet connectivity, the smart-phone based device allows transmission of patient information and diagnostic scores via local internet provider. In greater detail, the left side is a schematic where S is a White light source in smart phone; L1&2 are Lenses; T is a Tissue (biopsy or in-vivo); G is a Holographic grating; and C is a CMOS camera in smart phone. The right side shows the Smart-phone mounted device with lead wires to the tissue.

(63) FIG. 6 is a sample page from developed software for differentiation of different pathology grades using the multifractal tissue optical parameters derived from light scattering spectra. The mean values and standard deviations of the Generalized Hurst Exponent, Width of Singularity Spectra of samples (from the database) for different grades are shown in this column. The Calculate button calculates the value of the Generalized Hurst exponent, Width of Singularity spectrum and of the sample under investigation. It also displays the predicted grade of the sample based on binary classification.

Example 3

(64) For evaluating prediction accuracy of classifying the abnormalities in tissue (e.g. cancer) based on refractive index multifractality parameters (e.g. Hurst exponent, width of singularity spectrum and multifractal scaling exponent) obtained from elastic scattering spectra, we first trained a 7 model state in hidden Markov model for each of the categories. The training set for each category includes time series data obtained from experimentation. The model trained by the training data is defined as θ={π, A, B} and a sequence of seventeen states S={s1, s2, . . . s17}. π denotes the prior probabilities, A is the transition probabilities and B denotes the emission probabilities. Prior probabilities are first selected as a random function. A and B are modeled as Gaussian densities with mean 0 and variance 1. Then the data is trained on the model iteratively to fit and modify the model using EM (Expectation maximization) algorithm. The model is optimized using Lagrange multipliers. We use forward and backward algorithm to compute a set of sufficient statistics for our EM step tractably. Once the Model is sufficiently trained for a given sequence of data we calculate the likelihood of sequence with model for each category. i.e, we calculate P(X/θi) which is the sum of the joint likelihoods of the sequence over all possible state sequences Q allowed by the model for each category. The Maximum likelihood gives the prediction for the sequence data. FIG. 7 shows an example of the prediction analysis carried out by training the refractive index multifractal data (obtained from human colon cancer tissues using the device and method of the invention) in 7 model state of hidden Markov model. The parameters in width-axis shows predicted value for the parameter shown in the depth-axis. The height-axis displays the percentage of prediction accuracy. As can be clearly seen from the graph the parameters clearly create distinction between multifractal scaling exponent (TAU), width of singularity spectrum (WSS) and Hurst exponent (HurstExp). Data are correctly and accurately predicted as TAU all the time thus creates a zero chance of false prediction for the tested data set.

Example 4

(65) FIG. 8 shows application software (i.e. Android compatible) for the in-built ESS sensor. Android is one of the widely used open source mobile platforms. Android offers new possibilities for mobile applications by offering an open development environment built on an open source Linux kernel. Hardware access is available to all applications through a series of API libraries, and application interaction, while carefully controlled, is fully supported. It is definitely free and open platform that differ hardware from software that runs on it. The Android platform is a device-independent platform, which means that our App can work for various devices. Our Android based application software (FIG. 8) provides a platform for user interaction, allowing the user to control the ESS sensor, obtain measurements, and view results. In the background, the software analyzes and converts the ESS spectra captured by the camera into meaningful clinical data set to construct diagnostic rules for the detection of cancer.

(66) When the app is initiated, it prompts the user with a list of patient information parameters which can be selected by touching its labeled button. After selection of a particular patient parameter, the app then guides the user through the acquisition and measurement process, which consist of three user-initiated steps: a first step to ensure that the sensor is attached to the phone and a second step in which the sample probe is inserted into the sensor head, and third, the probe is in contact with the tissue, when the measurement is performed. Once the measurement is performed internal algorithms compute measurements of the desired parameters. Once refractive index multifractality (RI-MF) database for different cancer grades and/or types are collected, the app also performs grading of cancer (by correlating the measure RI-MF parameters with and display the result both numerically and on an animated scale with an indicator arrow. After the measurement result has been displayed, the user can immediately initiate another measurement using the button labeled “MEASURE” located above the displayed result. The navigation bar at the top of the app also allows the user to quickly navigate back to the title screen containing the list of parameters to perform a measurement of another site or patient. At any point during the measurement procedure, the user can press the home button on the app icon to exit the app completely. FIG. 8 illustrates the functional sequences of the described software operations.

Example 5

(67) FIGS. 9A and 9B illustrates discrimination of different human skin (pre)cancers based on the light scattering-derived multifractal tissue optical properties determined using NanoSpectro Technology. The multifractal parameters, Hurst exponent h(q=2), and width of singularity spectrum (WSS) form the two axes. In case of skin cancer types evaluated using the invention (device and analysis), it was found that the Hurst exponent (multifractal parameter) is lowest in case of Melanoma, followed by Squamous Cell Carcinoma (SCC) and significantly higher in case of Squamous In-Situ (precancer) as shown in FIG. 9A. Higher grades of cancers are found to be associated with increased anti-correlations of RI fluctuations (reduction in the Hurst exponent). FIG. 9(B) shows the software interface for detailed analysis and cancer classification.

Example 6

(68) FIG. 10A shows a NanoSpectro Vivo prototype for painless in-situ cancer diagnosis. Different probe shapes such as straight and angled are integrated to the illumination and collection fiber for analysis of tissue in-vivo. The prototype of FIG. 10A can be used in action on human skin. FIG. 10B shows the comparison of measured RI-MF parameters (Hurst exponent and WSS) at 10 different skin locations (i.e., hand, neck, back) between two healthy human subjects, measured by NanoSpectro-Vivo. No statistical significant difference between the two healthy subjects.

Example 7

(69) NanoSpectro-Vivo (optic device of the present application) can be used for cancer tissue analysis study during awake brain surgery. The optical biomarker used for classification is the multifractal parameter, Hurst exponent, Hq=2. FIG. 11 shows the measured optical biomarker (Hurst exponent, Hq=2) for disease (Lymphoma) detection in brain. The dotted line is drawn at Hurst exponent value of 0.8 to classify cancer from normal tissue. The measured optical biomarker (Hurst exponent) values were compared with pathological results from patient's biopsy samples, collected from same sites. The comparison between measured optical biomarker (Hurst exponent) values and pathological analysis of biopsy samples is shown in FIG. 11. Results indicate that our novel approach for brain cancer detection agrees well with the pathological classification of normal and cancer. However, the distribution of the Hurst exponent for another type of brain cancer (Glioblastoma Multiforme) was found to be different from that of the Lymphoma. Therefore, database and classification algorithms need to be developed and integrated with MFDFA analysis for disease diagnosis with high specificity and sensitivity.

Example 8

(70) Through a careful pathological staging and validation of tissue with light scattering based inverse analysis approach, a training data set of multifractal parameters needs to be formed and classified. A flow chart outlining the various steps of the inverse analysis of multifractality from the recorded elastic scattering signal from biopsied tissues and classification steps of multifractality parameter to increase accuracy of cancer detection is shown in FIG. 12. This is the whole analysis process and contains FIG. 1 flow chart. FIG. 1 may be replaced by this figure.

(71) FIG. 12 shows a flow chart for multifractal parameter extraction of recorded elastically-scattered spectrum, the MFDFA analysis and support vector machine (SVM) based classification of tissue. (i) Broad band light sends through optical fiber and elastic light scattering recorded from tissue slices using same fiber probe in backscattering mode. (ii) Born approximation based Fourier domain preprocessing was applied to extract tissue index inhomogeneity. (iii) Extracted index inhomogeneity subjected to Multifractal Detrended Fluctuation Analysis (MFDFA) to extract multifractal parameter, namely Hurst exponent (h(q=2)), represents index correlation and the width of singularity spectrum, represent the strength of multifractality in tissue index distribution. (iv) Extracted multifractal parameter from numbers of tissue samples are used to train by support vector machine (SVM), a state of art classification method to increase accuracy of colon cancer detection.

Example 9

(72) FIGS. 13A-13D shows an example of extraction of tissue multifractality through inverse analysis. The recorded normalized backscattering spectra through optical fiber probe of a tissue sample is shown in FIG. 13A. FIG. 13B displays detrended index fluctuation (scale length=8˜20 μm) extracted through Fourier domain inverse analysis from normalized scattering spectra. Scale vs. fluctuation function plot (Eqn. 4) in FIG. 13C shows different slope for different order of moment indicates existence multifractality. FIG. 13D displays order of moment, q vs. generalized Hurst exponent, h(q) plot for healthy (green color) and cancerous (black color) colon tissue. The extracted Hurst exponent, h(q=2)=0.80 for a healthy colon and gets reduced to h(q=2)=0.64 for cancer colon indicates a reduction of index correlation as cancer progress. Inset of FIG. 13D displays singularity strength, a vs. singularity spectrum, f(α) plot (green circle for a healthy colon and black square for cancerous colon), and corresponding width of singularity spectrum or the strength of multifractality Δα=0.52 for healthy colon and increased to Δα=0.81 in cancerous colon indicates increase of roughness or heterogeneity as cancer progress. Increased multifractality (larger value of Δα) at higher grades of cancer, is expected due to the increased heterogeneity and the different scaling behavior of the small-scale and the large-scale index fluctuations in the domain of different order of moments q.

Example 10

(73) FIG. 14 shows an example the linear support vector machine (SVM) based classification on the multifractal parameters extracted from elastic scattering spectra from wet colon tissue slices. The effect of tissue preparation on sensitivity, specificity and error rate were observed while classifying the test data sets. The support vector machine (SVM) was deployed as a classifier of healthy and cancerous colon tissues. Hence the training data sets of wet tissues were prepared for tissue classifications on the basis of nonlinear parameters like Hurst exponent (h(q=2)), strength of multifractality (Δα) and the power law coefficients (slope). The SVM was deployed as a classifier of healthy and cancerous colon tissues. Here, 90 training data points (40 normal and 50 cancerous) and 29 test data points have been taken from wet colon tissues for this SVM based classification. Optimum sensitivity, specificity and the error rates were achieved for colon tissue slices by the linear SVM based approach with polynomial order d=1. The data is spuriously distributed and the classifier plane best suited for this data is by forming multiple nonlinear regions The 3D curved layers represent the decision boundary. In initial SVM based exploration over colon tissues, SVM was deployed over two non-linear multifractal parameters Hurst exponent (h(q=2)), strength of multifractality (Δα), which did not led to required high specificity values. Hence three non-linear parameters (Hurst exponent (h(q=2)), strength of multifractality (Δα), power law coefficients (slope)) were explored, which led to improved results.

Example 7

(74) FIG. 15 shows schematic of an Integrated Spectral-Spatial Multifractal Imaging System (ISSMIS) to achieve multifractal mapping of sample. A supercontinuum (white, broadband) laser source and scanning spectrometer is used to select center wavelength with narrow bandwidth (2 nm). The light source can be replaced by any other broadband light source. The tunable filter is a spectrometer, however, a filter wheel, a liquid crystal tunable filter or Acousto-Optic tunable filter can be used instead to select center wavelength with narrow bandwidth. The rotating diffuser is used to minimize speckles during use of the laser source. The narrow band light is coupled to the condensing lens via a folding mirror to illuminate a sample area>1 mm2. The transmitted narrowband images at different wavelengths are collected by an objective lens and acquired using a digital camera. For high throughput and automated image acquisition and multifractal analysis, the system is interfaced with Multispectral Image Acquisition and Multifractal Analysis Software (MIAMAS).

Example 8

(75) The optic device and method of the present application is operable with Multispectral Image Acquisition and Multifractal Analysis Software (MIAMAS). The software allows connecting to the light source and camera; switching ON and OFF the light source; and selection of start central wavelength, bandwidth, and end wavelength. The interface control of the light source and camera as well as the control of start wavelength and bandwidth of light through the tunable filter are adjustable features and can be displayed. Further, it allows selection of wavelength interval at which images are to be acquired and time interval to acquire images using the camera, as well as file destination to save multispectral image sequences. The control of wavelength interval, end wavelength and imaging time interval are also optionally provided. Images of colon tissue slices at 2 nm intervals spanning from 450 to 700 nm range can be collected using the system and control software. Other intervals are also possible.

Example 9

(76) The optic device and method of the present application is able to show spectral and multifractal analysis using the Multispectral Image Acquisition and Multifractal Analysis Software (MIAMAS). The option for selecting a point on the image to carry out spectral—multifractal analysis using the image series collected at different wavelengths is possible. Using analysis function, the intensity distribution at different wavelengths (i.e., spectrum) at selected pixel(s) of the image is displayed after loading the acquired image files. Display of spectrum at a selected point, which was saved for further analysis by MFDFA using the flowchart detailed in FIG. 1.

(77) Because the presently disclosed invention (device and method) is essentially breaking new ground via the multifractal properties, work is currently under progress to explore entire relevant parameter space. The work-in-progress include rigorous evaluation of the specificity, sensitivity of the multifractal parameters towards cancerous changes in tissue morphology (for different types of cancers, mapping out the spatial accuracy of the approach, technological development of amenable experimental system, development of necessary algorithms/software for in-situ, near real time determination of the multifractal parameters for their subsequent use in classification of different pathological grades of cancers.

(78) In the presently disclosed invention (device and method), the optical fiber can be easily adapted with endoscopic methods for diagnosis of cancer in cervix, colon and stomach. The field of cancer diagnostics is rapidly expanding; however, despite the best laboratory practices the rate of conclusive diagnosis by histological analysis for a range of cancers, including cervical, prostate, bladder, skin and oral cancer, is only 65-75%. Our non-invasive technology has potential to provide highly-sensitive diagnostics for many cancer types.

(79) For obtaining high sensitivity/specificity (dependent on extraction of RI-MF parameters), spectral resolution better than 1 nm is used. However, higher spectral resolution corresponds to signature of larger structures in the Fourier domain. In fact, preliminary studies have been carried out to choose the limit of the maximum scale (size) in the MFDFA analysis. Since differences are always more prominent in smaller scales, the results of the analysis was not significantly affected by the upper limit of the scale size (corresponding to maximum resolution). If sensitivity/specificity is observed to be compromised, we obtain significant number of spectral data points by interpolation and then in the MFDFA the scale will be chosen to cover the actual spectral resolution. If still the sensitivity/specificity is measured to decrease in the tissue samples, higher resolution grating is used.

(80) The presently disclosed invention (device and method) is modular (FIG. 2), i.e. the optical fiber probe is detachable from the device. FC-PC/SMA connector is used to connect the fiber patch cord with the spectrometer. This will ensure that in case the optical fiber breaks, it can be replaced.

(81) Though spatial mapping of the multifractal tissue optical (RI-MF) properties of colon cancer samples is currently obtained (FIG. 4B) by scanning the sample stage or maneuvering the fiber using the presently disclosed invention (device and method), other scanning methods (e.g. scanning mirrors) and/or multiple fibers (forming a bundle) can be used to increase the throughput of the mapping process.

(82) During cancer screening, discrepancy between RI-MF parameter based diagnosis using the presently disclosed invention and pathology may occur for early cancer patients, where ultra-structural cellular changes are not discernible in pathology. In those cases, longitudinal studies on measurement of RI-MF parameters and correlation with pathological results are to be-carried out.

(83) The specification and examples herein provide a complete description of the structure and use of illustrative embodiments. Although certain embodiments have been described with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the scope of this invention. As such, the various illustrative embodiments of the devices are not intended to be limited to the particular forms disclosed. Rather, they include all modifications and alternatives falling within the scope of the claims, and embodiments other than the one shown may include some or all of the features of the depicted embodiment. For example, components may be omitted or combined as a unitary structure, and/or connections may be substituted. Further, where appropriate, aspects of any of the examples described above may be combined with aspects of any of the other examples described to form further examples having comparable or different properties and addressing the same or different problems. Similarly, it will be understood that the benefits and advantages described above may relate to one embodiment or may relate to several embodiments.

(84) Furthermore, the claims are not intended to include, and should not be interpreted to include, means-plus- or step-plus-function limitations, unless such a limitation is explicitly recited in a given claim using the phrase(s) “means for” or “step for,” respectively.

(85) The particular embodiments disclosed above are illustrative only, as the application may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. It is therefore evident that the particular embodiments disclosed above may be altered or modified, and all such variations are considered within the scope and spirit of the application. Accordingly, the protection sought herein is as set forth in the description. It is apparent that an application with significant advantages has been described and illustrated. Although the present application is shown in a limited number of forms, it is not limited to just these forms, but is amenable to various changes and modifications without departing from the spirit thereof.