A brain glioma EGFR amplification state prediction method based on terahertz time domain spectroscopy

Abstract

The application discloses a brain glioma EGFR amplification state prediction method based on a terahertz time-domain spectrum, and belongs to the technical field of machine learning. The EGFR amplification state prediction method comprises the following steps: collecting terahertz time-domain spectrum data of brain glioma tissue frozen sections, preprocessing the terahertz time-domain spectrum data of brain glioma tissue frozen sections, and constructing a sample data set; constructing an EGFR amplification state prediction model; training the constructed EGFR amplification state prediction model by using the sample data set; and realizing brain glioma EGFR amplification state prediction by using the trained EGFR amplification state prediction model. The application realizes brain glioma EGFR amplification state prediction by using the terahertz time-domain spectrum combined with a convolutional neural network, and has the advantages of nondestructive and rapid diagnosis. The application is expected to be put into practical application, and to assist doctors in predicting brain glioma EGFR amplification states during operations.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of machine learning, in particular to a brain glioma EGFR amplification state prediction method based on terahertz time domain spectroscopy. BACKGROUND

[0002] Brain glioma is the most common primary malignant tumor in the central nervous system in adults, among which glioblastoma has the highest degree of malignancy and the greatest harm. The epidermal growth factor receptor EGFR is composed of a tyrosine kinase receptor family, which directly regulates glioma angiogenesis, and angiogenesis plays an important role in the growth, invasion and recurrence of glioma. With more and more researches showing that EGFR amplification and mutation have been identified as driving events for various cancers, especially non-small cell lung cancer, breast cancer and glioblastoma.

[0003] At present, molecular biology techniques are mainly used to detect the EGFR amplification state. The methods adopted mainly include fluorescence in situ hybridization, chromogenic in situ hybridization, real-time quantitative PCR and next-generation sequencing. These methods can accurately diagnose the EGFR amplification state, but the waiting time for obtaining the results is longer. SUMMARY

[0004] The present application provides a brain glioma EGFR amplification state prediction method based on terahertz time domain spectroscopy to solve the technical problem of long detection time of EGFR amplification state at present.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] On the one hand, the present application provides a brain glioma EGFR amplification state prediction method based on terahertz time domain spectroscopy, which comprises:

[0007] Collecting terahertz time domain spectroscopy data of brain glioma tissue frozen sections, preprocessing the terahertz time domain spectroscopy data of brain glioma tissue frozen sections, and constructing a sample data set;

[0008] Constructing an EGFR amplification state prediction model;

[0009] Training the constructed EGFR amplification state prediction model using the sample data set;

[0010] Using the trained EGFR amplification state prediction model to realize brain glioma EGFR amplification state prediction.

[0011] Further, collecting terahertz time domain spectroscopy data of brain glioma tissue frozen sections comprises:

[0012] The glioma tissue removed during surgery was placed in pre-cooled phosphate buffered saline in an ice box;

[0013] After removing the glioma tissue from the phosphate buffer and drying the surface moisture, the tissue was placed in a freezing embedding box containing frozen tissue embedding medium, and the freezing embedding box was placed in a -80°C refrigerator. After the glioma tissue was completely solidified, the tissue was cut into 100-μm thick sections using a frozen tissue microtome;

[0014] The sections of brain glioma tissue were attached to polyethylene PE sheets as samples;

[0015] A transmission terahertz time-domain spectroscopy system is used to illuminate the sample and obtain its terahertz time-domain spectroscopy data.

[0016] Furthermore, when using the transmission terahertz time-domain spectroscopy system to irradiate the sample, the entire terahertz optical path of the transmission terahertz time-domain spectroscopy system is placed in a plexiglass box filled with nitrogen. During the terahertz time-domain spectroscopy data acquisition process, the relative humidity is maintained below 5% and the temperature is maintained at 20°C±0.2°C.

[0017] Furthermore, when using a transmission-type terahertz time-domain spectroscopy system to irradiate the sample, the sample is placed in a six-hole sample rack, which includes six receiving holes, one of which is used to place a PE sheet as a reference sample, and the other five receiving holes are each used to place a sample; before collecting terahertz time-domain spectroscopy data, the six-hole sample rack is placed in an experimental box filled with flowing dry nitrogen for a preset time, and the six-hole sample rack is kept rotating at a preset speed to ensure that the surface of each sample is evenly dried; when starting to collect terahertz time-domain spectroscopy data, the six-hole sample rack is first rotated so that the center of the reference sample is at the focus of the terahertz light, and the reference signal is measured. Thereafter, the six-hole sample rack is rotated to ensure that each sample is exactly located at the focus of the terahertz light in turn, so as to obtain terahertz time-domain spectroscopy data of five samples; wherein, when collecting terahertz time-domain spectroscopy data, each sample is collected multiple times and the average of the multiple collection results is taken as the final terahertz time-domain spectroscopy data of the sample.

[0018] Furthermore, the terahertz time-domain spectroscopy data of frozen sections of brain glioma tissue were preprocessed to construct a sample dataset, including:

[0019] The reference signal and the terahertz time-domain spectrum data of the frozen section of the glioma tissue are respectively converted into frequency domain spectra by Fourier transform, and the absorption coefficient of the frozen section of the glioma tissue is calculated based on the frequency domain spectrum of the reference signal and the frequency domain spectrum of the terahertz time-domain spectrum data of the frozen section of the glioma tissue;

[0020] The absorption coefficient is smoothed using a Savitzky-Golay filter, wherein the window size of the Savitzky-Golay filter is set to 15 and the polynomial order is set to 3;

[0021] Convert the smoothed absorption coefficient into GASF image and GADF image;

[0022] Convert the generated GASF image and GADF image into grayscale images respectively;

[0023] A sample data set is constructed using the grayscale images of the GASF image and the GADF image corresponding to the frozen section of the glioma tissue, as well as the labeling results corresponding to the frozen section of the glioma tissue; wherein the labeling results corresponding to the frozen section of the glioma tissue are whether the corresponding frozen section of the glioma tissue has EGFR amplification.

[0024] Furthermore, when using Fourier transform to convert the reference signal and the terahertz time-domain spectrum data of the frozen section of the brain glioma tissue into frequency domain spectra respectively, for the data to be converted, 11ps before the first echo appears is used as the interception point, the data before the interception point is retained, and the remaining data is padded with zeros to 2048 points, and then a radix-2 fast Fourier transform is performed to transform the data into the frequency domain.

[0025] Furthermore, the EGFR amplification status prediction model includes two convolutional neural networks with the same structure, which are used to extract features from the input image, wherein the input of one convolutional neural network is the grayscale image of the GASF image, and the input of the other convolutional neural network is the grayscale image of the GADF image; the features extracted by the two convolutional neural networks are input into the mid-end fusion layer, and the mid-end fusion layer splices the features extracted by the two convolutional neural networks along one dimension into a one-dimensional feature to obtain a fused feature; three fully connected layers are connected to the mid-end fusion layer, and the last fully connected layer outputs the prediction result, that is, whether EGFR amplification exists.

[0026] Furthermore, the convolutional neural network consists of two convolutional layers with a convolution kernel size of 5×5 and two maximum pooling layers of 2×2; the number of neurons in the three fully connected layers is 512, 256, and 256, respectively.

[0027] Furthermore, the RELU function is added after the convolutional neural network and the fully connected layer, and the Sigmoid function is used for the binary classification output. The Adam optimizer is selected to compile the model, and the binary cross entropy is used as the loss function to calculate the loss value during the model optimization process.

[0028] Furthermore, when training the constructed EGFR amplification status prediction model, a five-fold cross-validation training model was used, and the number of training rounds was set to 100;

[0029] The area under the receiver operating characteristic curve was used as the performance indicator for model evaluation.

[0030] On the other hand, the present invention further provides an electronic device, comprising a processor and a memory; wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the above method.

[0031] In yet another aspect, the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the above method.

[0032] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0033] 1. The present invention uses terahertz time-domain spectroscopy combined with convolutional neural networks to predict the EGFR amplification status of brain glioma, which has the advantages of non-destructive and rapid diagnosis.

[0034] 2. The present invention is the first to convert the absorption coefficient into two-dimensional images of GASF and GADF as a data set to predict the EGFR amplification status of brain glioma, which can effectively improve the accuracy of EGFR amplification status prediction.

[0035] 3. Compared with conventional machine learning methods, the present invention has higher classification accuracy and generalization ability, and is expected to be put into practical application to assist doctors in predicting the EGFR amplification status of brain gliomas during surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 Schematic diagram of the execution flow of the method for predicting the EGFR amplification status of glioma based on terahertz time-domain spectroscopy provided by an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the optical path of a terahertz time-domain spectrometer provided by an embodiment of the present invention;

[0039] Figure 3 is a time domain spectrum curve diagram provided by an embodiment of the present invention;

[0040] Figure 4 is a schematic diagram of sample data processing provided by an embodiment of the present invention;

[0041] Figure 5 1 is a structural diagram of an EGFR amplification status prediction model provided by an embodiment of the present invention;

[0042] Figure 6 This is a system block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0044] First, it should be noted that in the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "exemplarily" is intended to present concepts in a concrete manner. In addition, in the embodiments of the present invention, the meaning of "and / or" can be both or either of the two.

[0045] First embodiment

[0046] This embodiment provides a method for predicting the EGFR amplification status of brain glioma based on terahertz time-domain spectroscopy. The method can be implemented by an electronic device. The execution process of the method is as follows: Figure 1 As shown, the following steps are included:

[0047] S1, collecting terahertz time-domain spectroscopy data of frozen sections of brain glioma tissue, preprocessing the terahertz time-domain spectroscopy data of frozen sections of brain glioma tissue, and constructing a sample data set;

[0048] Specifically, in this embodiment, the implementation process of the above S1 is as follows:

[0049] S11, the surgically removed tumor and peritumoral tissue were immediately placed in pre-chilled phosphate buffered saline in an ice box and brought back to the laboratory for frozen section processing;

[0050] S12: After removing the specimen from the phosphate buffer and drying the surface moisture, place it in a freezing embedding box containing frozen tissue embedding medium and place it in a -80°C freezer. After it is completely solidified, use a frozen tissue slicer to cut the tissue into 100-micron thick slices. Then, use a customized 2-mm-thick polyethylene sheet as a carrier, and evenly attach the frozen slices to the PE sheet for the terahertz measurement experiment. All slices are immediately stored at -80°C until the start of the terahertz measurement experiment.

[0051] S13, using a transmission terahertz time-domain spectroscopy (THz-TDS) system to illuminate the slice and obtain the sample signal;

[0052] It should be noted that the optical path of the transmission terahertz time-domain spectroscopy system is as follows: Figure 2 As shown, the terahertz wave generation and detection utilizes a low-temperature gallium arsenide-based photoconductive antenna. Infrared light from a femtosecond laser (Toptica Photonics Femtofiber Smart 780, central wavelength 780nm, pulse width <90fs) is split and directed at the center of the photoconductive antenna as pump and probe light, respectively, with an intensity of 15mW. A stepper motor delay line is added to the pump optical path to achieve waveform scanning of the terahertz pulse. The terahertz pulse is beam-converted by four off-axis parabolic mirrors and focused at the sample area, with a beam waist diameter of 5mm. The time-domain signal is scanned with a step size of 33.3fs, and a total of 1024 points are collected. When biological samples are present in the terahertz wave propagation path, the available frequency band of 0.2-1.4THz is selected.

[0053] It should also be noted that water vapor in the terahertz wave propagation path strongly absorbs the signal. To eliminate the influence of humidity changes on the characterization, the entire terahertz optical path was placed in a nitrogen-filled plexiglass box. During the test, the relative humidity was maintained below 5% and the temperature was maintained at 20°C ± 0.2°C. To improve testing efficiency and ensure the same testing environment within the same control group, this experiment used a homemade six-hole sample rack, each of which housed one reference sample (pure PE sheet) and five experimental samples (PE sheets with tissue samples). During the experiment, the samples were installed and placed on a porous rotating sample holder in sequence. Before measurement, in order to remove the condensed water generated on the surface of the samples after transferring from a frozen environment to a room temperature environment, they needed to be placed in an experimental box filled with flowing dry nitrogen for a period of time. During the process, the sample holder was kept rotating slowly to ensure that the surface of each sample was evenly dry. When the measurement started, the sample position was first rotated and adjusted so that the center of the pure PE sheet serving as the reference signal was at the focus of the THz light, and the reference signal was measured. Thereafter, the sample holder was rotated 60 degrees each time to ensure that the subsequent measured samples were exactly located at the focused light. In this way, 5 subsequent groups of sample signals were obtained in sequence. Each measurement data was collected twice and averaged to reduce errors.

[0054] S14, converting the reference signal and the terahertz time-domain spectrum data of the frozen section of the glioma tissue into frequency domain spectra respectively by Fourier transform, and calculating the absorption coefficient of the frozen section of the glioma tissue based on the frequency domain spectrum of the reference signal and the frequency domain spectrum of the terahertz time-domain spectrum data of the frozen section of the glioma tissue;

[0055] It should be noted that when the collected raw data is plotted in the time domain spectrum, a curve with echoes will be obtained. At this time, direct Fourier transform will result in the instrument effect caused by multiple reflections inside the sample. In order to eliminate the oscillation in the spectrum signal, Figure 3 As shown, in this embodiment, a certain point (11ps) before the first echo appears is used as the interception point, the data before the interception point is retained, and the remaining time domain data is padded with zeros to 2048 (2 11 ) point and then perform a radix-2 fast Fourier transform to the frequency domain. Figure 4 As shown, the reference signal spectrum is recorded as E ref (ω), the sample signal is recorded as E sam (ω), and the transfer function T(ω) inside the sample is obtained by using the ratio of the two. Since the data was truncated before, only the main peak signal is retained after preprocessing, and multiple reflections do not need to be considered when calculating the terahertz parameters. Therefore, the refractive index n of the sample can be obtained by calculation. s (ω), extinction coefficient κ s (ω), and the absorption coefficient α s (ω), the specific calculation formula is as follows:

[0056]

[0057] The absorption coefficient of the sample can be calculated from the extinction coefficient:

[0058]

[0059] Where d is the sample thickness, T(ω) is the sample transfer function, c is the speed of light in vacuum, and n is the sample transfer function. PE is the refractive index of PE, and n is measured experimentally. PE =1.50.

[0060] S15, using Savitzky-Golay filter to smooth the absorption coefficient; wherein the window size of Savitzky-Golay filter is set to 15 and the polynomial order is set to 3;

[0061] S16, converting the smoothed absorption coefficient into a two-dimensional image of a Gram Angle Sum Field (GASF) image and a Gram Angle Difference Field (GADF), as follows:

[0062]

[0063]

[0064] Where GASF is the cosine of the angle sum, GADF is the cosine of the angle difference, n is the number of frequency points, φ n is the angle value at the nth frequency point.

[0065] S17, converting the GASF image and the GADF image into grayscale images with a resolution of 256×256 respectively, so the size of a single image is 256×256×1 (pixel×pixel×channel);

[0066] S18. Construct a sample data set using the grayscale images of the GASF image and the GADF image corresponding to the frozen section of the brain glioma tissue, as well as the labeling results corresponding to the frozen section of the brain glioma tissue; wherein the labeling results corresponding to the frozen section are whether EGFR amplification exists in the corresponding frozen section of the brain glioma tissue.

[0067] S2, constructing a prediction model for EGFR amplification status;

[0068] Among them, the EGFR amplification status prediction model structure built in this embodiment is as follows Figure 5 As shown in the figure, the network primarily consists of two convolutional neural networks with identical structures, which are fused and fully connected after feature extraction. The convolutional neural network model primarily consists of two convolutional layers with a kernel size of 5×5 and two 2×2 maximum pooling layers. Mid-end fusion concatenates the extracted features along one dimension into a single one-dimensional feature. Finally, the model constructs three fully connected layers with 512, 256, and 256 neurons, respectively. Furthermore, a RELU function is added after the convolutional and fully connected layers, and the binary classification output uses a Sigmoid function. The Adam optimizer is used to compile the model, and binary cross entropy is used as the loss function during model optimization. The model is trained using 5-fold cross-validation, with 100 training rounds.

[0069] S3, using the sample dataset to train the constructed EGFR amplification status prediction model;

[0070] In this example, a total of 440 samples were collected, of which 120 were EGFR amplified and the remaining 320 were EGFR non-amplified. After smoothing with a Savitzky-Golay filter and generating corresponding two-dimensional images using GASF and GADF, a total of 440 sets of image data were obtained, each containing corresponding GASF and GADF images. This data was divided into training and test sets in a 4:1 ratio. The training set was used for model training and validation using 5-fold cross-validation.

[0071] Furthermore, this embodiment uses the area under the receiver operating characteristic curve (ROC AUC) value as the main performance indicator of model evaluation. The ROC curve is a graphical tool for representing the performance of a classification model. It plots the relationship between the true positive rate (TPR) and the false positive rate (FPR) under different threshold settings. AUC stands for the area under the ROC curve and is used to measure the performance of the classifier. The closer the AUC value is to 1, the better the classifier performance is, while an AUC value close to 0 indicates poor performance.

[0072] After experiments, the model of this embodiment finally achieved a prediction AUC value of 94.74% on the test set, which proves that the model of this embodiment has high classification accuracy and is expected to be put into practical application to assist doctors in predicting the EGFR amplification status of brain glioma during surgery.

[0073] S4, use the trained EGFR amplification status prediction model to predict the EGFR amplification status of brain glioma.

[0074] In summary, this embodiment provides a method for predicting the EGFR amplification status of gliomas based on terahertz time-domain spectroscopy. This method uses terahertz time-domain spectroscopy combined with a convolutional neural network to predict the EGFR amplification status of gliomas, offering the advantages of non-destructive and rapid diagnosis. Compared to conventional machine learning methods, this method, for the first time, converts the absorption coefficient into a two-dimensional image dataset of GASF and GADF to predict the EGFR amplification status of gliomas, effectively improving the accuracy of EGFR amplification status prediction. This method has high classification accuracy and generalization capabilities, and can be used to assist physicians in intraoperatively predicting the EGFR amplification status of gliomas.

[0075] Second embodiment

[0076] This embodiment provides an electronic device, such as Figure 6 As shown, the electronic device includes: a processor and a memory; wherein the processor and the memory can be connected via a communication bus; the memory stores at least one instruction, which is loaded and executed by the processor to implement the method of the first embodiment described above. In addition, the electronic device may also include a transceiver; the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0077] Next, combine Figure 6 A detailed introduction to the various components of the electronic device is given below:

[0078] Among them, the processor is the control center of the electronic device, and the electronic device may include multiple processors, each of which may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here can be a processor or a general term for multiple processing elements. For example, the processor is one or more central processing units (CPUs), or other general-purpose processors, application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement an embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor can perform various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0079] In a specific implementation, as an embodiment, the processor may include one or more CPUs, such as Figure 6 The CPU0 and CPU1 shown in FIG are, of course, only exemplary.

[0080] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0081] Optionally, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and accessed through the interface circuit ( Figure 6 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0082] The transceiver may include a receiver and a transmitter ( Figure 6 The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function. The transceiver can be integrated with the processor or exist independently and communicate with the electronic device through the interface circuit ( Figure 6 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0083] In addition, it should be noted that Figure 6 The structure of the electronic device shown in the figure does not constitute a limitation on the device. The actual device may include more or fewer components than shown, or may combine certain components, or arrange the components differently. In addition, the technical effects achieved by the electronic device when executing the method of the first embodiment can refer to the technical effects described in the first embodiment above, and therefore will not be repeated here.

[0084] Third embodiment

[0085] This embodiment provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method of the first embodiment described above. The computer-readable storage medium may be a ROM, random access memory, CD-ROM, magnetic tape, floppy disk, or optical data storage device. The instructions stored therein can be loaded by a processor in a terminal to execute the method described above.

[0086] Furthermore, it should be noted that the present invention may be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention may take the form of a fully or partially hardware embodiment, a fully or partially software embodiment, or an embodiment combining software and hardware aspects. Furthermore, when implemented using software, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired connection (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium. The semiconductor medium may be a solid state drive.

[0087] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the process in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0089] It should also be noted that, in this document, relational terms such as first and second are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. The terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the process, method, article, or terminal device comprising the element. In addition, the term "and / or" is merely a description of an associative relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: the presence of A alone, the presence of A and B simultaneously, or the presence of B alone, where A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding. "At least one" means one or more, and "more" means two or more. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0090] In addition, it can be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0091] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0092] In the several embodiments provided herein, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of functional modules / units is merely a logical functional division. In actual implementation, other division methods may be used, such as multiple units or components being combined or integrated into another device, or some features being ignored or not implemented. Furthermore, the coupling or direct coupling or communication connection shown or discussed between each other may be through some interface, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs. In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0093] If the method 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. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0094] Finally, it should be noted that the above description is only the preferred embodiment of the application, it should be pointed out that although the preferred embodiment of the application has been described, for those skilled in the art, once the basic creative concept of the application is known, several improvements and refinements can be made without departing from the principles of the application, and these improvements and refinements should also be considered as the protection scope of the application. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the application.

Claims

1. A method for predicting EGFR amplification status in brain glioma based on terahertz time-domain spectroscopy, characterized in that: The method for predicting the EGFR amplification status of brain glioma based on terahertz time-domain spectroscopy includes: The method includes collecting terahertz time-domain spectral data of frozen sections of glioma tissue, preprocessing the terahertz time-domain spectral data of frozen sections of glioma tissue, and constructing a sample data set, including: using Fourier transform to convert the reference signal and the terahertz time-domain spectral data of the frozen sections of glioma tissue into frequency domain spectra respectively, and calculating the absorption coefficient of the frozen sections of glioma tissue based on the frequency domain spectrum of the reference signal and the frequency domain spectrum of the terahertz time-domain spectral data of the frozen sections of glioma tissue; and smoothing the absorption coefficient using a Savitzky-Golay filter; wherein, The window size of the Savitzky-Golay filter was set to 15, and the polynomial order was set to 3. The smoothed absorption coefficient was converted into a GASF image and a GADF image. The generated GASF image and GADF image were converted into grayscale images respectively. A sample dataset was constructed using the grayscale images of the GASF image and GADF image corresponding to the frozen sections of glioma tissue, as well as the labeling results corresponding to the frozen sections of glioma tissue. The labeling results corresponding to the frozen sections of glioma tissue indicated whether EGFR amplification was present in the corresponding frozen sections of glioma tissue. Constructing an EGFR amplification status prediction model; the EGFR amplification status prediction model includes two convolutional neural networks with the same structure, which are used to extract features from the input image, wherein the input of one convolutional neural network is a grayscale image of the GASF image, and the input of the other convolutional neural network is a grayscale image of the GADF image; the features extracted by the two convolutional neural networks are input into a mid-end fusion layer, which concatenates the features extracted by the two convolutional neural networks along one dimension into a one-dimensional feature to obtain a fused feature; three fully connected layers are connected after the mid-end fusion layer, and the final fully connected layer outputs a prediction result, i.e., whether EGFR amplification exists; The constructed EGFR amplification status prediction model was trained using the sample dataset; The trained EGFR amplification status prediction model is used to predict the EGFR amplification status of brain glioma.

2. The method for predicting EGFR amplification status of brain glioma based on terahertz time-domain spectroscopy according to claim 1, characterized in that: Acquire terahertz time-domain spectroscopy data of frozen sections of brain glioma tissue, including: The glioma tissue removed during surgery was placed in pre-cooled phosphate buffered saline in an ice box; After removing the glioma tissue from the phosphate buffer and drying the surface moisture, the tissue was placed in a freezing embedding box containing frozen tissue embedding medium, and the freezing embedding box was placed in a -80°C refrigerator. After the glioma tissue was completely solidified, the tissue was cut into 100-μm thick sections using a frozen tissue microtome; Slices of brain glioma tissue were attached to polyethylene (PE) sheets as samples; A transmission terahertz time-domain spectroscopy system is used to illuminate the sample and obtain its terahertz time-domain spectroscopy data.

3. The method for predicting EGFR amplification status of glioma based on terahertz time-domain spectroscopy according to claim 2, characterized in that: When using the transmission terahertz time-domain spectroscopy system to irradiate the sample, the entire terahertz optical path of the transmission terahertz time-domain spectroscopy system was placed in a plexiglass box filled with nitrogen. During the terahertz time-domain spectroscopy data acquisition process, the relative humidity was maintained below 5% and the temperature was maintained at 20℃±0.2℃.

4. The method for predicting EGFR amplification status of brain glioma based on terahertz time-domain spectroscopy according to claim 2, wherein: When using a transmission-type terahertz time-domain spectroscopy system to irradiate the sample, the sample is placed in a six-hole sample rack, which includes six receiving holes, one of which is used to place a PE sheet as a reference sample, and the other five receiving holes are each used to place a sample; before collecting terahertz time-domain spectroscopy data, the six-hole sample rack is placed in a laboratory box filled with flowing dry nitrogen for a preset time, and the six-hole sample rack is kept rotating at a preset speed to ensure that the surface of each sample is evenly dried; when starting to collect terahertz time-domain spectroscopy data, the six-hole sample rack is first rotated so that the center of the reference sample is at the focus of the terahertz light, and the reference signal is measured. Thereafter, the six-hole sample rack is rotated to ensure that each sample is exactly located at the focus of the terahertz light in turn, so as to obtain terahertz time-domain spectroscopy data of five samples; wherein, when collecting terahertz time-domain spectroscopy data, each sample is collected multiple times and the average of the multiple collection results is taken as the final terahertz time-domain spectroscopy data of the sample.

5. The method for predicting EGFR amplification status of brain glioma based on terahertz time-domain spectroscopy according to claim 1, wherein: When using Fourier transform to convert the reference signal and the terahertz time-domain spectrum data of the frozen section of the brain glioma tissue into frequency domain spectra respectively, for the data to be converted, 11ps before the first echo appears is used as the interception point, the data before the interception point is retained, the remaining data is padded with zeros to 2048 points, and then a radix-2 fast Fourier transform is performed to transform the data into the frequency domain.

6. The method for predicting EGFR amplification status of brain glioma based on terahertz time-domain spectroscopy according to claim 1, wherein: The convolutional neural network consists of two convolutional layers with a convolution kernel size of 5×5 and two maximum pooling layers of 2×2; the number of neurons in the three fully connected layers is 512, 256, and 256, respectively.

7. The method for predicting EGFR amplification status of brain glioma based on terahertz time-domain spectroscopy according to claim 6, characterized in that: The RELU function is added after the convolutional neural network and the fully connected layer, and the Sigmoid function is used for the binary classification output. The Adam optimizer is selected to compile the model, and the binary cross entropy is used as the loss function to calculate the loss value during the model optimization process.

8. The method for predicting EGFR amplification status of brain glioma based on terahertz time-domain spectroscopy according to claim 7, characterized in that: When training the constructed EGFR amplification status prediction model, a five-fold cross-validation training model was used, and the number of training rounds was set to 100; The area under the receiver operating characteristic curve was used as the performance indicator for model evaluation.

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