The present disclosure relates to devices and methods for ex-vivo tissue analysis in general, and the devices and methods for detecting diseased tissue in an intraoperative procedure in particular.
For many decades the reference method for the diagnosis of cancer has been histopathological examination of tissues using conventional microscopy. This process is known as surgical pathology. In surgical pathology, tissue samples can be produced from surgical procedures (tumor resection), diagnostic biopsies or autopsies. These tissue samples are subsequently subjected to a process that includes dissection, fixation, and cutting of tissue into precisely thin slices which are stained for contrast and mounted onto glass slides. The slides are examined by a pathologist under a microscope, and their interpretations of the tissue results in the pathology “read” of the sample.
Advanced optical and electromagnetic (“EM”) imaging approaches have been reported for the determination of tumor margin: These include the use of exogenous contrast-based fluorescence imaging [1, 2], near infrared spectroscopy [3], mass spectroscopy [4], terahertz reflectivity [5], Raman spectroscopy [6-12], hyperspectral imaging [13], autofluorescence life-time imaging [14], and the like.
Of these, techniques that do not require any exogenous dye or contrast agents are particularly appealing in an in-vivo setting. Optical spectroscopy, in particular, offer significant advantages to patients by avoiding potential toxicological issues, FDA approval of the contrast agents as drugs, the cost of the contrast agents and increased surgical time associated with administering imaging agents.
The endogenous fluorescence signatures offer useful information that can be mapped to the functional, metabolic and morphological attributes of a biological specimen, and have therefore been utilized for diagnostic purposes. Biomolecular changes occurring in the cell and tissue state during pathological processes and disease progression result in alterations of the amount and distribution of endogenous fluorophores and form the basis for classification. Tissue autofluorescence has been proposed to detect various malignancies including cancer by measuring either differential intensity or lifetimes of the intrinsic fluorophores. Biomolecules such as tryptophan, collagen, elastin, nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), porphyrins, etc. present in tissue provide discernible and repeatable autofluorescence spectral patterns. While tissue autofluorescence (AF) has been proposed for cancer detection, there are three major limitations for conventional autofluorescence-based diagnosis approaches: First, traditional autofluorescence assays typically use a single excitation wavelength which obviously does not excite all the intrinsic fluorophores present in the tissue. Consequently, it does not effectively utilize the comprehensive and rich biochemical information embedded in the tissue matrix both from cells and the extracellular matrix. Second, most of the applications involving autofluorescence use a fiber probe with single-point measurement capability and are inherently slow. Third, most of the autofluorescence approaches involve simpler data analysis such as calculating redox ratio or oxygenation index ratio, and do not utilize the rich morphological information. The imaging device and methods disclosed in this application addresses these concerns and offers a potentially transformative tissue analysis tool by utilizing biomolecule/biochemical and tissue microstructural information encoded in the autofluorescence and reflectance images.
According to an aspect of the present disclosure, a system for analyzing a tissue sample is provided that includes an excitation light unit, at least one photodetector, and a system controller. The excitation light unit is configured to selectively produce a plurality of excitation lights. Each excitation light is centered on a wavelength distinct from the centered wavelength of the other excitation lights. At least one of the excitation light centered wavelengths is configured to produce an autofluorescence emission from one or more biomolecules of interest, and a diffuse reflectance signal from the tissue sample. The at least one photodetector is configured to detect the autofluorescence emission, or the diffuse reflectance signal, or both from the tissue sample as a result of the respective incident excitation light, and to produce signals representative of the detected said autofluorescence emission, or the detected said diffuse reflectance signal, or both. The system controller is in communication with the excitation light unit, the at least one photodetector, and a non-transitory memory storing instructions, which instructions when executed cause the system controller to: a) control the excitation light unit to sequentially produce the plurality of excitation lights; b) receive and process the signals from the at least one photodetector for each sequential application of the plurality of excitation lights, and produce an image representative of the signals produced by each sequential application of the plurality of excitation lights; and c) analyze the tissue sample using a plurality of the images to identify the presence of diseased tissue within the tissue sample.
In any of the aspects or embodiments described above and herein, the excitation light unit may include a plurality of excitation light sources. Each excitation light source is configured to produce one of the excitation lights centered on a wavelength distinct from a respective centered wavelength of the other respective excitation lights.
In any of the aspects or embodiments described above and herein, the system may further include a first filter arrangement that is configured to filter the light emitted or reflected from the tissue sample resulting from each said sequential application of the plurality of excitation lights from each of the plurality of excitation light sources.
In any of the aspects or embodiments described above and herein, the first filter arrangement may include a plurality of bandpass filters and at least one of the plurality of bandpass filters is configured to selectively pass a portion of the light emitted or reflected from the tissue sample associated with the one or more biomolecules of interest.
In any of the aspects or embodiments described above and herein, the first filter arrangement may include a plurality of bandpass filters and at least one of the plurality of bandpass filters is configured to selectively pass a portion of the light emitted or reflected from the tissue sample associated with cellular or microstructural morphological information relating to the tissue sample.
In any of the aspects or embodiments described above and herein, the system may further include a second first filter arrangement that is configured to filter the excitation light produced from each of the plurality of excitation light sources.
In any of the aspects or embodiments described above and herein, the one or more biomolecules of interest are associated with cancer, and the cancer may be breast cancer, liver cancer, bladder cancer, colon cancer, or other cancers.
In any of the aspects or embodiments described above and herein, the instructions when executed cause the system controller to analyze the tissue sample using each image to identify the presence of diseased tissue within the tissue sample, and the analysis may include identifying the presence of the one or more biomolecules of interest.
In any of the aspects or embodiments described above and herein, the instructions when executed cause the system controller to analyze the tissue sample using each image to identify the presence of diseased tissue within the tissue sample, and the analysis may include providing cellular or microstructural morphological information.
In any of the aspects or embodiments described above and herein, the instructions when executed cause the system controller to analyze the analyze the tissue sample using each image to identify the presence of diseased tissue within the tissue sample, and the analysis may include using stored empirical data to evaluate the plurality of the images.
In any of the aspects or embodiments described above and herein, wherein system controller includes or is in communication with a classifier and the instructions when executed may cause the system controller to analyze the analyze the tissue sample using each image to identify the presence of diseased tissue within the tissue sample, the analysis may include using the classifier to evaluate the plurality of the images.
According to another aspect of the present disclosure, a method of analyzing a tissue sample is provided. The method includes: a) sequentially interrogating the tissue sample with a plurality of excitation lights, each excitation light centered on a wavelength distinct from the centered wavelength of the other excitation lights, wherein at least one of the excitation light centered wavelengths is configured to produce autofluorescence emissions from one or more biomolecules of interest, and a diffuse reflectance signals from the tissue sample; b) using at least one photodetector to detect the autofluorescence emissions, or the diffuse reflectance signals, or both from the tissue sample, and to produce photodetector signals representative of the detected said autofluorescence emissions, or the detected said diffuse reflectance signals, or both; c) processing the photodetector signals for each sequential application of the plurality of excitation lights, including producing an image representative of the photodetector signals produced by each sequential application of the plurality of excitation lights; and d) analyzing the tissue sample using each image to identify the presence of diseased tissue within the tissue sample.
In any of the aspects or embodiments described above and herein, the method may include filtering the light emitted or reflected from the tissue sample resulting from each said sequential interrogation of the tissue sample.
In any of the aspects or embodiments described above and herein, wherein the filtering step includes filtering the light emitted or reflected from the tissue sample to selectively pass a portion of the light emitted or reflected from the tissue sample associated with the one or more biomolecules of interest.
In any of the aspects or embodiments described above and herein, wherein the filtering step includes filtering the light emitted or reflected from the tissue sample to selectively pass a portion of the light emitted or reflected from the tissue sample associated with cellular or microstructural morphological information relating to the tissue sample.
In any of the aspects or embodiments described above and herein, the method may include filtering each of the excitation lights prior to each respective said excitation light interrogating the tissue sample.
In any of the aspects or embodiments described above and herein, wherein the one or more biomolecules of interest may be associated with a type of cancer.
In any of the aspects or embodiments described above and herein, wherein the analyzing step may include identifying the presence of the one or more biomolecules of interest.
In any of the aspects or embodiments described above and herein, wherein the analyzing step may include providing cellular or microstructural morphological information.
In any of the aspects or embodiments described above and herein, wherein the analyzing step may include using stored empirical data to evaluate the plurality of the images.
In any of the aspects or embodiments described above and herein, wherein the analyzing step may include using a classifier to evaluate the plurality of the images.
In any of the aspects or embodiments described above and herein, wherein the tissue sample may be a breast tissue biopsy.
In any of the aspects or embodiments described above and herein, the tissue sample may be an ex-vivo sample produced during intraoperative surgery, or the tissue sample may be a tissue biopsy, or the tissue sample may be used in conjunction with a mammogram for tissue biopsy diagnosis, or the tissue sample may be used for triaging surgical specimens in a pathological setting.
The foregoing features and elements may be combined in various combinations without exclusivity, unless expressly indicated otherwise. These features and elements as well as the operation thereof will become more apparent in light of the following description and the accompanying drawings. It should be understood, however, the following description and drawings are intended to be exemplary in nature and non-limiting.
Aspects of the present disclosure include a novel and unobvious multi-spectral imaging system and method for analyzing a tissue sample. The present disclosure may be used to analyze a tissue sample for purposes of providing information regarding whether the tissue sample is normal (e.g., “healthy”) or abnormal, and therefore potentially in a diseased state (e.g., cancerous). It can also be used to detect and differentiate different types of the malignancies and their grades/stages of a malignancy type. The present disclosure has particular utility in analyzing tissue sample for breast cancer analysis. The present disclosure is not however limited for use with breast cancer detection; e.g., other nonlimiting uses include tissue analysis for liver cancer, bladder cancer, colon cancer to name a few. In addition, the methods and systems disclosed here can be used to differentiate and detect different normal tissue types and can also be used to measure characteristics of a normal tissue such as metabolite state, density etc.
The system includes an excitation light unit, one or more optical filters, one or more photodetectors, and a system controller. In some embodiments, the system may include other components such as one or more of a filter controller, a tunable optical filtering device, a scanning device, an optical switch, an optical splitter, and the like.
The excitation light unit is configured to produce excitation light centered at a plurality of different wavelengths. As will be detail below, the term “excitation light unit” as used herein is not limited to a light source configured to produce AF emissions but is also able to produce reflectance signal. Exλmples of an acceptable excitation light source include lasers and light emitting diodes (LEDs) each centered at a different wavelength, or a tunable excitation light source configured to selectively produce light centered at respective different wavelengths, or a source of white light (e.g., flash lamps) that may be selectively filtered to produce the aforesaid excitation light centered at respective different wavelengths. This disclosure is not limited to any particular type of excitation light unit. The wavelengths produced by the excitation light unit are typically chosen based on the photometric properties associated with one or more biomolecules of interest. Excitation light incident to a biomolecule that acts as a fluorophore will cause the fluorophore to emit fluorescent light at a wavelength longer than the wavelength of the excitation light; i.e., via AF. Tissue may naturally include certain fluorophores such as tryptophan, collagen, elastin, nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), porphyrins, and the like. Biomolecular changes occurring in the cell and tissue state during pathological processes and as a result of disease progression often result in alterations of the amount and distribution of these endogenous fluorophores. Hence, diseased tissues such as cancerous tissue, due to the marked difference in cell-cycle and metabolic activity can exhibit distinct intrinsic tissue AF, or in other words an “AF signature” that is identifiable. Embodiments of the present disclosure may utilize these AF characteristics/signatures to identify regions of diseased tissue such as cancerous tissue. Different types of diseased tissue (e.g., different types of cancerous tissue) and diseases tissue of different organs for instance breast and liver cancers may have different biomolecules/biochemicals associated therewith and the present disclosure is not therefore limited to any particular biomolecule or any particular cancer type. Excitation wavelengths are also chosen that cause detectable light reflectance from tissue of interest. The detectable light reflectance is a function of light absorption of the tissue and/or light scattering associated with the tissue (this may be collectively referred to as diffuse reflectance). Certain tissue types or permutations thereof have differing and detectable light reflectance characteristics (“signatures”) at certain wavelengths. Significantly, these reflectance characteristics can provide information beyond intensity; e.g., information relating to cellular or microcellular structure such as cell nucleus and extracellular components. The morphology of a healthy tissue cell may be different from that of an abnormal or diseased tissue cell. Hence, the ability to gather cellular or microstructural morphological information (sometimes referred to as “texture”) provides another tool for determining tissue types and the state and characteristics of such tissue. The excitation light source may be configured to produce light at wavelengths in the ultraviolet (UV) region (e.g., 100-400 nm) and in some applications may include light in the visible region (e.g., 400-700 nm). The excitation lights are chosen based on the absorption characteristics of the biomolecules of interest.
The present disclosure may utilize a variety of different photodetector types configured to sense light and provide signals that may be used to measure the same. Non-limiting examples of an acceptable photodetector include those that convert light energy into an electrical signal such as photodiodes, avalanche photodiodes, a CCD array, an ICCD, a CMOS, or the like. The photodetector may take the form of a camera. As will be described below, the photodetector(s) are configured to detect AF emissions from the interrogated tissue and/or diffuse reflectance from the interrogated tissue and produce signals representative of the detected light and communicate the signals to the system controller.
The system controller is in communication with other components within the system, such as the excitation light source and one or more photodetectors. In some system embodiments, the system may also be in communication with one or more of a: filter controller, a tunable optical filtering device, an optical switch, an optical splitter, and the like as will be described below. The system controller may be in communication with these components to control and/or receive signals therefrom to perform the functions described herein. The system controller may include any type of computing device, computational circuit, processor(s), CPU, computer, or the like capable of executing a series of instructions that are stored in memory. The instructions may include an operating system, and/or executable software modules such as program files, system data, buffers, drivers, utilities, and the like. The executable instructions may apply to any functionality described herein to enable the system to accomplish the same algorithmically and/or coordination of system components. The system controller includes or is in communication with one or more memory devices. The present disclosure is not limited to any particular type of memory device, and the memory device may store instructions and/or data in a non-transitory manner. Exλmples of memory devices that may be used include read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. The system controller may include, or may be in communication with, an input device that enables a user to enter data and/or instructions, and may include, or be in communication with, an output device configured, for example to display information (e.g., a visual display or a printer), or to transfer data, etc. Communications between the system controller and other system components may be via a hardwire connection or via a wireless connection.
Embodiments of the present disclosure may include optical filtering elements configured to filter excitation light, or optical filtering elements configured to filter emitted light (including reflected light), or both. Each optical filtering element is configured to pass a defined bandpass of wavelengths associated with an excitation light source or emitted/reflected light (e.g., fluorescence or reflectance), and may take the form of a bandpass filter. In regard to filtering excitation light, the system may include an independent filtering element associated with each independent excitation light source or may include a plurality of filtering elements disposed in a movable form (e.g., a wheel or a linear array configuration) or may include a single filtering element that is operable to filter excitation light at a plurality of different wavelengths (e.g., see
An exemplary embodiment of a present disclosure system 20 is diagrammatically illustrated in
In the operation of the system 30 embodiment diagrammatically shown in
In the operation of the system 20 embodiment diagrammatically shown in
It should be noted that the present disclosure system embodiments diagrammatically illustrated in
In some system 20 embodiments, a tunable excitation light source configured to selectively produce light centered at a plurality of different wavelengths as an alternative to the plurality of AF excitation light sources. The tunable excitation light source may be operated to sequentially produce each of the respective excitation wavelengths.
In the system 20 embodiments described above and others, the signals (i.e., image) representative of the emitted light (AF and/or reflectance) captured by the photodetector arrangement 28 (e.g., camera or plurality of photodetectors) for each excitation light wavelength collectively provide a mosaic of information relating to the tissue sample. The chart shown in
The integrated information provided by the aforesaid emitted light images provide distinct benefits in the process of identifying biomolecule/tissue types of interest with a desirable degree of specificity and sensitivity. As can be seen from
In some embodiments, the stored instructions within the system controller 30 may include an artificial intelligence/machine learning (AI/ML) algorithm trained classifier 52 (e.g., see
While the principles of the disclosure have been described above in connection with specific apparatuses and methods, it is to be clearly understood that this description is made only by way of example and not as limitation on the scope of the disclosure. Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it is understood that the embodiments may be practiced without these specific details. For example, the present disclosure has been described above in terms of analyzing tissue samples suspected to include cancerous tissue associated with, for example, breast cancer, liver cancer, bladder cancer, colon cancer, and the like. The present disclosure also provides considerable utility with procedures associated with detecting and treating the same. For example, the tissue sample may be an ex-vivo sample produced during intraoperative surgery, or the tissue sample may be a tissue biopsy, or the tissue sample may be produced and analyzed in conjunction with mammogram for a tissue biopsy diagnosis, or the tissue sample may be used for triaging surgical specimens in a pathological setting, or the like. The aforesaid are non-limiting examples of applications of the present disclosure.
It is noted that the embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a block diagram, etc. Although any one of these structures may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
The singular forms “a,” “an,” and “the” refer to one or more than one, unless the context clearly dictates otherwise. For example, the term “comprising a specimen” includes single or plural specimens and is considered equivalent to the phrase “comprising at least one specimen.” The term “or” refers to a single element of stated alternative elements or a combination of two or more elements unless the context clearly indicates otherwise. As used herein, “comprises” means “includes.” Thus, “comprising A or B,” means “including A or B, or A and B,” without excluding additional elements.
It is noted that various connections are set forth between elements in the present description and drawings (the contents of which are included in this disclosure by way of reference). It is noted that these connections are general and, unless specified otherwise, may be direct or indirect and that this specification is not intended to be limiting in this respect. Any reference to attached, fixed, connected or the like may include permanent, removable, temporary, partial, full and/or any other possible attachment option.
No element, component, or method step in the present disclosure is intended to be dedicated to the public regardless of whether the element, component, or method step is explicitly recited in the claims. No claim element herein is to be construed under the provisions of 35 U.S.C. 112(f) unless the element is expressly recited using the phrase “means for.” As used herein, the terms “comprise”, “comprising”, or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
While various inventive aspects, concepts and features of the disclosures may be described and illustrated herein as embodied in combination in the exemplary embodiments, these various aspects, concepts, and features may be used in many alternative embodiments, either individually or in various combinations and sub-combinations thereof. Unless expressly excluded herein all such combinations and sub-combinations are intended to be within the scope of the present application. Still further, while various alternative embodiments as to the various aspects, concepts, and features of the disclosures—such as alternative materials, structures, configurations, methods, devices, and components, and so on—may be described herein, such descriptions are not intended to be a complete or exhaustive list of available alternative embodiments, whether presently known or later developed. Those skilled in the art may readily adopt one or more of the inventive aspects, concepts, or features into additional embodiments and uses within the scope of the present application even if such embodiments are not expressly disclosed herein. For example, in the exemplary embodiments described above within the Detailed Description portion of the present specification, elements may be described as individual units and shown as independent of one another to facilitate the description. In alternative embodiments, such elements may be configured as combined elements.
The following references are hereby incorporated by reference in their respective entireties:
This application claims priority to U.S. Patent Appln. No. 63/079,783 filed Sep. 17, 2020, which is hereby incorporated by reference in its entirety.
Filing Document | Filing Date | Country | Kind |
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PCT/US2021/050991 | 9/17/2021 | WO |
Number | Date | Country | |
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63079783 | Sep 2020 | US |