Lung cancer immune marker analysis method, device, electronic device and storage medium

Abstract

The present invention relates to the field of biological data processing, and discloses a lung cancer immune marker analysis method, device, electronic device and storage medium, wherein the method comprises: using fluorescent signal amplification technology to perform a multi-biomarker staining operation on lung tumor tissue to obtain a tissue staining image set; performing real cell selection on the tissue staining image set according to the region of interest input by the user to obtain a circled image set; performing quantitative statistics on each biomarker in the circled image set according to a preset optimal positive threshold screening strategy to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker; and outputting the tissue marker positive rate, interstitial marker positive rate and double positive rate in a structured manner to obtain lung immune marker positive data. The main purpose of the present invention is to improve the comprehensiveness and accuracy of biomarker analysis of the lung cancer immune microenvironment.

Description

Technical Field

[0001] The present invention relates to a lung cancer immune marker analysis method, device, electronic equipment and storage medium, and belongs to the field of biological data processing. Background Art

[0002] With the increasing incidence of cancer and the trend of younger people getting the disease, new treatment technologies are also developing and innovating. Nowadays, immunotherapy is gradually gaining wide recognition due to its good efficacy. Immunotherapy refers to killing cancer cells by activating the body's own immune system and relying on its own immune function.

[0003] However, there are still some patients who do not respond to immunotherapy. With the advancement of technology, people have gradually come to understand the complexity and diversity of the immune environment of the tumor microenvironment and its important impact on immunotherapy. Further analysis and understanding of the tumor immune microenvironment and the biomarkers therein will help improve the responsiveness of immunotherapy. The biomarker refers to a biochemical indicator that can mark changes or possible changes in the structure or function of tissues, cells and subcellular structures, and is often used for disease diagnosis, disease staging, or to evaluate the safety and effectiveness of new drugs or new therapies in the target population.

[0004] Today's biomarker analysis is achieved by randomly selecting thresholds and other operations when quantifying indicators. There is a lack of effective biomarker quantification evaluation mechanisms. In addition, lung cancer tumor analysis only considers biomarker analysis within tissues, which is rather one-sided, resulting in low accuracy of lung cancer immune analysis data. Summary of the invention

[0005] The present invention provides a lung cancer immune marker analysis method, device, electronic device and storage medium, the main purpose of which is to improve the comprehensiveness and accuracy of biomarker analysis of the lung cancer immune microenvironment.

[0006] To achieve the above object, the present invention provides a method for analyzing lung cancer immune markers, comprising:

[0007] Obtaining pre-constructed lung tumor tissue, and performing a multi-biomarker-based staining operation on the lung tumor tissue using a fluorescent signal amplification technique to obtain a tissue staining image set;

[0008] According to the region of interest input by the user, real cell selection is performed on the tissue staining image set to obtain a selected image set;

[0009] According to the preset optimal positive threshold screening strategy, quantitative statistics are performed on each biomarker in the circled image set to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker;

[0010] The tissue marker positivity rate, interstitial marker positivity rate and double positivity rate are structured and output to obtain lung immune marker positivity data.

[0011] Optionally, the method of performing quantitative statistics on each biomarker in the circled image set according to a preset optimal positive threshold screening strategy to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker includes:

[0012] Acquire a monochrome negative control image and a monochrome bright field image for each biomarker in the circled image set;

[0013] Performing Gaussian filtering on the monochrome negative control image and the monochrome bright field image to obtain a negative noise reduction image and a bright field noise reduction image;

[0014] Performing fluorescence signal analysis on the negative noise reduction image and the bright field noise reduction image to obtain the fluorescence signal intensity of the marker in the negative noise reduction image and the bright field noise reduction image;

[0015] According to the fluorescence signal intensity corresponding to each biomarker in each of the circled images, a fluorescence intensity histogram is drawn, and the variation range of the fluorescence intensity histogram is calculated according to a preset statistical method;

[0016] Based on the mutation range as the positive standard, quantitative statistics are performed on each of the biomarkers to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between the target biomarkers corresponding to each of the biomarkers.

[0017] Optionally, the quantitative statistics of each of the biomarkers are performed to obtain the positive rate of the marker in the tissue, the positive rate of the marker in the stroma, and the double positive rate between the target biomarkers corresponding to each of the biomarkers, including:

[0018] Recording the fluorescence parameters corresponding to each of the biomarkers, and performing a 16-bit to 8-bit conversion operation on the recorded results to obtain an experimental record;

[0019] Performing tissue structure recognition on the experimental record according to a pre-trained tumor tissue recognition service to obtain a tumor tissue region;

[0020] Optimizing the experimental record in the tumor tissue region according to the small object deletion operation and the hole filling operation to obtain an optimized record;

[0021] According to the mutation range, a scatter plot is set for the optimized records to obtain a single-positive ratio map, a double-positive ratio map, and a tissue ratio map. Based on the single-positive ratio map, the double-positive ratio map, and the tissue ratio map, the positive rate of the marker in the tissue corresponding to each of the biomarkers, the positive rate of the marker in the stroma, and the double-positive rate between the target biomarkers are obtained.

[0022] Optionally, performing real cell selection on the tissue staining image set according to the region of interest input by the user to obtain the selected image set includes:

[0023] grayscale the tissue staining image set to obtain a grayscale image set;

[0024] Performing edge detection on each grayscale image in the grayscale image set, and segmenting the grayscale image according to an edge detection algorithm to obtain a tissue block image set corresponding to each grayscale image;

[0025] Using the mouse interactive service, obtaining the tissue block images included in the tissue block image set by the mouse and clustering and outputting them, so as to obtain the region of interest corresponding to the grayscale image;

[0026] Each of the regions of interest in the tissue staining image set is extracted to obtain a circled image set.

[0027] Optionally, the use of fluorescent signal amplification technology to perform a multi-biomarker-based staining operation on the lung tumor tissue to obtain a tissue staining image set includes:

[0028] Using immunostaining technology to perform visual staining on the lung tumor tissue based on multiple biomarkers to obtain a basic tissue staining image set;

[0029] The signal of the basic tissue staining image set is enhanced by using branched fluorescence technology to obtain a tissue staining image set.

[0030] Optionally, the tissue marker positivity rate, interstitial marker positivity rate and double positivity rate are structured and output to obtain lung immune marker positivity data, including:

[0031] Constructing a scatter plot of the marker positive rate in the tissue, the marker positive rate in the stroma, and the double positive rate to obtain a scatter plot set;

[0032] The scatter plot set and the optimization record are uniformly output in PDF format to obtain positive data of lung immune markers.

[0033] Optionally, after obtaining the positive data of lung immune markers, the method further comprises:

[0034] The lung immune marker positive data is subjected to subsequent lung task identification to obtain a lung analysis result.

[0035] In order to solve the above problems, the present invention also provides a lung cancer immune marker analysis device, the device comprising:

[0036] A staining image acquisition module, used to acquire pre-constructed lung tumor tissue, and perform a multi-biomarker-based staining operation on the lung tumor tissue using a fluorescent signal amplification technique to obtain a tissue staining image set;

[0037] A region of interest capture module, used to perform real cell selection on the tissue staining image set according to the region of interest input by the user, to obtain a selected image set;

[0038] A multi-faceted qualitative module, for performing quantitative statistics on each biomarker in the circled image set according to a preset optimal positive threshold screening strategy, and obtaining the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker;

[0039] The data output module is used to output the tissue marker positivity rate, interstitial marker positivity rate and double positivity rate in a structured manner to obtain lung immune marker positivity data.

[0040] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0041] at least one processor; and,

[0042] a memory communicatively connected to the at least one processor; wherein,

[0043] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the above-mentioned lung cancer immune marker analysis method.

[0044] In order to solve the above problem, the present invention also provides a computer-readable storage medium, wherein at least one instruction is stored in the computer-readable storage medium, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned lung cancer immune marker analysis method.

[0045] Compared with the problems described in the background technology, the embodiment of the present invention first uses the fluorescence signal amplification technology of branch fluorescence to enhance the signal of the staining image, increase the accuracy of the subsequent biomarker analysis, and in addition, it can also ensure the stability of the staining image in temperature, pH and other aspects, laying the foundation for subsequent multiple quantitative analysis; then the present invention improves the accuracy of the lung tumor image in the area to be identified by the user manually inputting the area of ​​interest, and reduces the image order of magnitude, thereby improving the analysis efficiency; in addition, the embodiment of the present invention adopts the best positive threshold screening strategy, that is, the fluorescence signal analysis is performed by the monochrome negative control image and the monochrome bright field image, so as to obtain different positive screening thresholds for each biomarker, which has higher clarity than the traditional fixed threshold cell positive screening process and thus increases the accuracy of the analysis results, and the present invention not only analyzes the markers in the tissue, but also analyzes the markers in the stroma and the target biomarkers, so that the analysis results are more comprehensive and accurate. Therefore, the lung cancer immune marker analysis method, device, electronic device and computer-readable storage medium proposed by the present invention mainly aim to improve the comprehensiveness and accuracy of the biomarker analysis of the lung cancer immune microenvironment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a process for analyzing lung cancer immune markers provided by one embodiment of the present invention;

[0047] Figure 2 A schematic flow chart of a detailed function in a lung cancer immune marker analysis method provided by an embodiment of the present invention;

[0048] Figure 3 A functional module diagram of a lung cancer immune marker analysis device provided by one embodiment of the present invention;

[0049] Figure 4 A schematic diagram of the structure of an electronic device for implementing the lung cancer immune marker analysis method provided by one embodiment of the present invention.

[0050] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0052] The embodiment of the present application provides a lung cancer immune marker analysis method. The execution subject of the lung cancer immune marker analysis method includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the lung cancer immune marker analysis method can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0053] Embodiment 1:

[0054] Reference Figure 1 FIG. 1 is a flow chart of a lung cancer immune marker analysis method provided by an embodiment of the present invention. In this embodiment, the lung cancer immune marker analysis method includes:

[0055] S1. Obtain pre-constructed lung tumor tissue, and use fluorescent signal amplification technology to perform a multi-biomarker-based staining operation on the lung tumor tissue to obtain a tissue staining image set.

[0056] It should be known that there are many biomarkers in the tumor microenvironment (TME) that interact with each other, so specific indicators need to be further quantified. However, there is still a lack of effective biomarker quantification evaluation mechanisms. Thresholds are randomly selected, etc., and the analysis accuracy is low. Therefore, the present invention not only analyzes the distribution of biomarkers in lung cancer tissue, but also needs to further consider the distribution of biomarkers in the interstitium.

[0057] First, the lung tumor tissue may be obtained by extraction, slicing, smearing, etc. in the embodiment of the present invention.

[0058] Furthermore, the present invention requires staining the obtained lung tumor tissue.

[0059] In detail, in an embodiment of the present invention, the fluorescent signal amplification technology is used to perform a multi-biomarker staining operation on the lung tumor tissue to obtain a tissue staining image set, including:

[0060] Using immunostaining technology to perform visual staining on the lung tumor tissue based on multiple biomarkers to obtain a basic tissue staining image set;

[0061] The signal of the basic tissue staining image set is enhanced by using branched fluorescence technology to obtain a tissue staining image set.

[0062] It should be known that immunohistochemistry staining / immunofluorescence staining (IHC / IF) is the most commonly used detection method for TME research. The present invention uses immunofluorescence staining for staining, and then uses branch fluorescence to enhance the signal of the staining result, so as to obtain a clearer set of tissue staining images. Among them, the branch fluorescence (Tyramide Signal Amplification, TSA) is a fluorescent signal amplification technology based on tyramide signal, which can amplify the signal by 10-1000 times, and the fluorescent compound has ideal light, heat, and pH stability, so that the multicolor fluorescent labeling is not limited by the species of the primary antibody.

[0063] In the present embodiment, taking the lung tumor microenvironment as an example, common analysis includes six biomarkers including PanCK, PD-L1, CD20, CD68, CD8, and FOXP3, plus DAPI as the background for cell identification. Therefore, multiple biomarker staining is required in the present embodiment.

[0064] S2. According to the region of interest input by the user, real cell selection is performed on the tissue staining image set to obtain a selected image set.

[0065] In the embodiment of the present invention, the user can capture the region of interest by using tools such as a client mouse.

[0066] In detail, in an embodiment of the present invention, the real cell selection is performed on the tissue staining image set according to the region of interest input by the user to obtain the selected image set, including:

[0067] grayscale the tissue staining image set to obtain a grayscale image set;

[0068] Performing edge detection on each grayscale image in the grayscale image set, and segmenting the grayscale image according to an edge detection algorithm to obtain a tissue block image set corresponding to each grayscale image;

[0069] Using the mouse interactive service, obtaining the tissue block images included in the tissue block image set by the mouse and clustering and outputting them, so as to obtain the region of interest corresponding to the grayscale image;

[0070] Each of the regions of interest in the tissue staining image set is extracted to obtain a circled image set.

[0071] In order to ensure that the tumor tissue is completely cut when the user's region of interest is intercepted, the present invention adopts edge detection technology in advance to perform edge segmentation on each object in the tissue staining image. When all tissue block images involved in the area encompassed by the user's mouse starting point and the final point are clustered and extracted, the region of interest with a complete object is obtained. The mouse interaction service refers to a service that is operated and controlled through the interaction between the mouse and the computer, and is used to select the region of interest by delimiting the range.

[0072] Finally, the embodiment of the present invention intercepts each region of interest in the tissue staining image set to obtain a circled image set.

[0073] S3. According to the preset optimal positive threshold screening strategy, quantitative statistics are performed on each biomarker in the circled image set to obtain the tissue marker positivity rate, interstitial marker positivity rate and double positivity rate between target biomarkers corresponding to each biomarker.

[0074] Among them, the optimal positive threshold screening strategy refers to adaptively selecting the positive standard threshold according to the different fluorescence intensities of each biomarker under different environments, rather than a qualitative analysis method using a fixed threshold, to improve the clarity of the qualitative analysis of each biomarker.

[0075] For more details, please refer to Figure 2 As shown, in the embodiment of the present invention, the preset optimal positive threshold screening strategy is used to perform quantitative statistics on each biomarker in the circled image set to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker, including:

[0076] S31, acquiring a monochrome negative control image and a monochrome bright field image for each biomarker in the circled image set;

[0077] S32, performing Gaussian filtering on the monochrome negative control image and the monochrome bright field image to obtain a negative noise reduction image and a bright field noise reduction image;

[0078] S33, performing fluorescence signal analysis on the negative noise reduction image and the bright field noise reduction image to obtain the fluorescence signal intensity of the marker in the negative noise reduction image and the bright field noise reduction image;

[0079] S34, drawing a fluorescence intensity histogram according to the fluorescence signal intensity corresponding to each biomarker in each of the circled images, and calculating the variation range of the fluorescence intensity histogram according to a preset statistical method;

[0080] S35. Based on the mutation range as the positive standard, quantitative statistics are performed on each of the biomarkers to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between the target biomarkers corresponding to each of the biomarkers.

[0081] The monochrome negative control image is an image stained with an antibody not labeled with a fluorescent substance or a nonspecific antibody to evaluate nonspecific binding and background fluorescence, providing a negative control for determining the level of background fluorescence; the monochrome bright field is an image of the specimen stained with the same fluorescently labeled antibody as the target antibody to evaluate the fluorescence intensity range of positive cells, providing a positive control for determining the fluorescence intensity range of positive cells.

[0082] The embodiment of the present invention uses machine learning software to perform tissue recognition. The machine learning software comes with default parameters of background, stroma, objects, and tissue recognition and optimization functions: remove small objects, fill holes. The background usually refers to an area that is irrelevant to the tissue or target object, such as a blank area or a noise area. The recognition of the background area can help separate the tissue or target object from the surrounding environment, so as to better perform subsequent analysis and processing; the stroma refers to the extracellular matrix, including intercellular substances and extracellular matrix molecules, which can help separate cells and extracellular structures in the tissue, so as to better understand the structure and function of the tissue; the object usually refers to a cell or other target with a specific morphology and structure, which can help separate and locate different cells or targets in the tissue, so as to perform further quantitative analysis and feature extraction.

[0083] The deletion of small objects refers to the possibility that there may be some small cells or targets in the tissue image, which may be noise or insignificant structures. By deleting small objects, these interference factors can be removed; the filling of holes refers to the possibility that there may be some holes or gaps in the tissue image, which may be caused by noise or missing during image acquisition or processing. By filling holes, discontinuous areas in the image can be connected, making the image more complete and continuous, which is convenient for subsequent analysis and processing.

[0084] In an embodiment of the present invention, by performing fluorescence signal analysis on a monochrome negative control image and a monochrome bright field image, the intensity or pixel value of the fluorescence signal can be measured, and then a fluorescence intensity distribution curve or histogram can be drawn based on the intensity or pixel value of the fluorescence signal, so that the distribution of the fluorescence signal can be observed.

[0085] In the embodiment of the present invention, a suitable threshold is selected to divide positive and negative cells according to the fluorescence intensity histogram. The selection of the threshold is usually determined according to the experimental requirements and specific circumstances. The present invention is based on statistical methods, such as the mean value plus or minus the standard deviation to automatically calculate the variation range. Then, quantitative analysis is performed to determine the biomarker or cell with fluorescence intensity in the variation range as positive, otherwise, it is negative.

[0086] Furthermore, in an embodiment of the present invention, the quantitative statistics of each of the biomarkers are performed to obtain the positive rate of the marker in the tissue, the positive rate of the marker in the stroma, and the double positive rate between the target biomarkers corresponding to each of the biomarkers, including:

[0087] Recording the fluorescence parameters corresponding to each of the biomarkers, and performing a 16-bit to 8-bit conversion operation on the recorded results to obtain an experimental record;

[0088] Performing tissue structure recognition on the experimental record according to a pre-trained tumor tissue recognition service to obtain a tumor tissue region;

[0089] Optimizing the experimental record in the tumor tissue region according to the small object deletion operation and the hole filling operation to obtain an optimized record;

[0090] According to the mutation range, a scatter plot is set for the optimized records to obtain a single-positive ratio map, a double-positive ratio map, and a tissue ratio map. Based on the single-positive ratio map, the double-positive ratio map, and the tissue ratio map, the positive rate of the marker in the tissue corresponding to each of the biomarkers, the positive rate of the marker in the stroma, and the double-positive rate between the target biomarkers are obtained.

[0091] In the embodiment of the present invention, in the process of qualitative identification of tissues, the value range of the first 16-bit fluorescence data is 0 to 65535, and the value range of the 8-bit fluorescence data is 0 to 255, which reduces the storage space and computational complexity of the data, while retaining enough information for subsequent analysis. Then, the pre-trained tumor tissue identification service in the machine learning software is used to identify lung cancer tissue cells, distinguish lung cancer cells and stroma, and optimize the experimental records through the small object deletion operation and hole filling operation in the machine learning software. Finally, the positive rate of the marker in the tissue corresponding to each of the biomarkers, the positive rate of the marker in the stroma, and the double positive rate between the target biomarkers are identified by the proportion map.

[0092] S4. Output the tissue marker positivity rate, interstitial marker positivity rate and double positivity rate in a structured manner to obtain lung immune marker positivity data.

[0093] In detail, in the embodiment of the present invention, the step S4 includes:

[0094] Constructing a scatter plot of the marker positive rate in the tissue, the marker positive rate in the stroma, and the double positive rate to obtain a scatter plot set;

[0095] The scatter plot set and the optimization record are uniformly output in PDF format to obtain positive data of lung immune markers.

[0096] In the embodiments of the present invention, the analysis results are usually output in PDF format, wherein the scatter plots of the various biomarkers are independent of each other and require pre-construction of external table relationships to ensure the connectivity between the various images and increase the associated information.

[0097] In addition, in the embodiment of the present invention, after obtaining the positive data of the lung immune marker, the method further includes:

[0098] The lung immune marker positive data is subjected to subsequent lung task identification to obtain a lung analysis result.

[0099] The embodiment of the present invention learns the mapping relationship from input data (such as lung immune marker positive data) to the prediction result, thereby quickly analyzing the subsequent lung immune marker positive data.

[0100] Compared with the problems described in the background technology, the embodiment of the present invention first uses the fluorescence signal amplification technology of branch fluorescence to enhance the signal of the staining image, increase the accuracy of the subsequent biomarker analysis, and in addition, it can also ensure the stability of the staining image in temperature, pH and other aspects, laying the foundation for subsequent multiple quantitative analysis; then the present invention improves the accuracy of the lung tumor image in the area to be identified by the user manually inputting the area of ​​interest, and reduces the image order of magnitude, thereby improving the analysis efficiency; in addition, the best positive threshold screening strategy is adopted in the embodiment of the present invention, that is, the fluorescence signal analysis is performed by the monochrome negative control image and the monochrome bright field image, so as to obtain different positive screening thresholds for each biomarker, which has higher clarity than the traditional fixed threshold cell positive screening process and thus increases the accuracy of the analysis results, and the present invention not only analyzes the markers in the tissue, but also analyzes the markers in the stroma and the target biomarkers, so that the analysis results are more comprehensive and accurate. Therefore, the lung cancer immune marker analysis method proposed in the present invention mainly aims to improve the comprehensiveness and accuracy of the biomarker analysis of the lung cancer immune microenvironment.

[0101] Embodiment 2:

[0102] like Figure 3 FIG. 1 is a functional module diagram of a lung cancer immune marker analysis device provided by an embodiment of the present invention.

[0103] The lung cancer immune marker analysis device 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the lung cancer immune marker analysis device 100 may include a staining image acquisition module 101, a region of interest interception module 102, a multi-faceted qualitative module 103 and a data output module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0104] The staining image acquisition module 101 is used to acquire pre-constructed lung tumor tissue, and perform a multi-biomarker staining operation on the lung tumor tissue using a fluorescent signal amplification technology to obtain a tissue staining image set;

[0105] The region of interest capture module 102 is used to perform real cell selection on the tissue staining image set according to the region of interest input by the user to obtain a selected image set;

[0106] The multi-aspect qualitative module 103 is used to perform quantitative statistics on each biomarker in the selected image set according to a preset optimal positive threshold screening strategy, and obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker;

[0107] The data output module 104 is used to output the tissue marker positive rate, interstitial marker positive rate and double positive rate in a structured manner to obtain lung immune marker positive data.

[0108] In detail, each module in the lung cancer immune marker analysis device 100 in the embodiment of the present invention is used in the same manner as described above. Figure 1 and Figure 2 The same technical means are used as the lung cancer immune marker analysis method described in , and can produce the same technical effects, so they will not be repeated here.

[0109] Embodiment 3:

[0110] like Figure 4 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a lung cancer immune marker analysis method provided by an embodiment of the present invention.

[0111] The electronic device 1 may include a processor 10, a memory 11, a bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a lung cancer immune marker analysis program.

[0112] Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the lung cancer immune marker analysis program, but also be used to temporarily store data that has been output or is to be output.

[0113] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as lung cancer immune marker analysis programs, etc.) stored in the memory 11, and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0114] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0115] Figure 4 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 4The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0116] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0117] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0118] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0119] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0120] The lung cancer immune marker analysis program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0121] Obtaining pre-constructed lung tumor tissue, and performing a multi-biomarker-based staining operation on the lung tumor tissue using a fluorescent signal amplification technique to obtain a tissue staining image set;

[0122] According to the region of interest input by the user, real cell selection is performed on the tissue staining image set to obtain a selected image set;

[0123] According to the preset optimal positive threshold screening strategy, quantitative statistics are performed on each biomarker in the circled image set to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker;

[0124] The tissue marker positivity rate, interstitial marker positivity rate and double positivity rate are structured and output to obtain lung immune marker positivity data.

[0125] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0126] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0127] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0128] Obtaining pre-constructed lung tumor tissue, and performing a multi-biomarker-based staining operation on the lung tumor tissue using a fluorescent signal amplification technique to obtain a tissue staining image set;

[0129] According to the region of interest input by the user, real cell selection is performed on the tissue staining image set to obtain a selected image set;

[0130] According to the preset optimal positive threshold screening strategy, quantitative statistics are performed on each biomarker in the circled image set to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker;

[0131] The tissue marker positivity rate, interstitial marker positivity rate and double positivity rate are structured and output to obtain lung immune marker positivity data.

[0132] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0134] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for analyzing lung cancer immune markers, characterized in that: The method comprises: Obtaining pre-constructed lung tumor tissue, and performing a multi-biomarker-based staining operation on the lung tumor tissue using a fluorescent signal amplification technique to obtain a tissue staining image set; According to the region of interest input by the user, real cell selection is performed on the tissue staining image set to obtain a selected image set; According to the preset optimal positive threshold screening strategy, quantitative statistics are performed on each biomarker in the circled image set to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker; Outputting the tissue marker positive rate, interstitial marker positive rate and double positive rate in a structured manner to obtain lung immune marker positive data; Among them, the quantitative statistics of each biomarker in the circled image set are performed according to the preset optimal positive threshold screening strategy to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker, including: Acquire a monochrome negative control image and a monochrome bright field image for each biomarker in the circled image set; Performing Gaussian filtering on the monochrome negative control image and the monochrome bright field image to obtain a negative noise reduction image and a bright field noise reduction image; Performing fluorescence signal analysis on the negative noise reduction image and the bright field noise reduction image to obtain the fluorescence signal intensity of the marker in the negative noise reduction image and the bright field noise reduction image; According to the fluorescence signal intensity corresponding to each biomarker in each of the circled images, a fluorescence intensity histogram is drawn, and the variation range of the fluorescence intensity histogram is calculated according to a preset statistical method; Based on the mutation range as the positive standard, quantitative statistics are performed on each of the biomarkers to obtain the tissue marker positive rate, interstitial marker positive rate and double positive rate between the target biomarkers corresponding to each of the biomarkers.

2. The lung cancer immune marker analysis method according to claim 1, characterized in that: The quantitative statistics of each of the biomarkers are performed to obtain the positive rate of the marker in the tissue, the positive rate of the marker in the stroma and the double positive rate between the target biomarkers corresponding to each of the biomarkers, including: Recording the fluorescence parameters corresponding to each of the biomarkers, and performing a 16-bit to 8-bit conversion operation on the recorded results to obtain an experimental record; Performing tissue structure recognition on the experimental record according to a pre-trained tumor tissue recognition service to obtain a tumor tissue region; Optimizing the experimental record in the tumor tissue region according to the small object deletion operation and the hole filling operation to obtain an optimized record; According to the mutation range, a scatter plot is set for the optimized records to obtain a single-positive ratio map, a double-positive ratio map, and a tissue ratio map. Based on the single-positive ratio map, the double-positive ratio map, and the tissue ratio map, the positive rate of the marker in the tissue corresponding to each biomarker, the positive rate of the marker in the stroma, and the double-positive rate between the target biomarkers are obtained.

3. The lung cancer immune marker analysis method according to claim 1, characterized in that: The step of performing real cell selection on the tissue staining image set according to the region of interest input by the user to obtain the selected image set includes: grayscale the tissue staining image set to obtain a grayscale image set; Performing edge detection on each grayscale image in the grayscale image set, and segmenting the grayscale image according to an edge detection algorithm to obtain a tissue block image set corresponding to each grayscale image; Using the mouse interactive service, obtaining the tissue block images included in the tissue block image set by the mouse and clustering and outputting them, so as to obtain the region of interest corresponding to the grayscale image; Each of the regions of interest in the tissue staining image set is extracted to obtain a circled image set.

4. The lung cancer immune marker analysis method according to claim 1, characterized in that: The method of using the fluorescence signal amplification technology to perform a multi-biomarker staining operation on the lung tumor tissue to obtain a tissue staining image set includes: Using immunostaining technology to perform visual staining on the lung tumor tissue based on multiple biomarkers to obtain a basic tissue staining image set; The signal of the basic tissue staining image set is enhanced by using branched fluorescence technology to obtain a tissue staining image set.

5. The lung cancer immune marker analysis method according to claim 2, characterized in that: The tissue marker positive rate, interstitial marker positive rate and double positive rate are structured and output to obtain lung immune marker positive data, including: Constructing a scatter plot of the marker positive rate in the tissue, the marker positive rate in the stroma, and the double positive rate to obtain a scatter plot set; The scatter plot set and the optimization record are uniformly output in PDF format to obtain positive data of lung immune markers.

6. The lung cancer immune marker analysis method according to claim 1, characterized in that: After obtaining the positive data of the lung immune marker, the method further comprises: The lung immune marker positive data is subjected to subsequent lung task identification to obtain a lung analysis result.

7. A lung cancer immune marker analysis device, characterized in that: The device for applying the lung cancer immune marker analysis method according to any one of claims 1 to 6 comprises: A staining image acquisition module, used to acquire pre-constructed lung tumor tissue, and perform a multi-biomarker-based staining operation on the lung tumor tissue using a fluorescent signal amplification technique to obtain a tissue staining image set; A region of interest capture module, used to perform real cell selection on the tissue staining image set according to the region of interest input by the user, to obtain a selected image set; A multi-faceted qualitative module, for performing quantitative statistics on each biomarker in the circled image set according to a preset optimal positive threshold screening strategy, and obtaining the tissue marker positive rate, interstitial marker positive rate and double positive rate between target biomarkers corresponding to each biomarker; The data output module is used to output the tissue marker positivity rate, interstitial marker positivity rate and double positivity rate in a structured manner to obtain lung immune marker positivity data.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the lung cancer immune marker analysis method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for analyzing lung cancer immune markers according to any one of claims 1 to 6 is implemented.

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