Terahertz time-domain spectroscopy-based skin tumor heterogeneity detection system and method

Abstract

The invention belongs to the technical field of terahertz technology, medicine and tumor, and particularly discloses a skin tumor heterogeneity detection system and method based on terahertz time-domain spectroscopy, and the method comprises the following steps: carrying out multi-point scanning on tumor and surrounding normal tissues through a terahertz scanner to obtain terahertz time-domain signals of the tumor, converting the terahertz time-domain signals into a terahertz absorption curve, and determining the skin tumor heterogeneity according to the terahertz absorption curve; extracting a plurality of characteristic parameters from the terahertz absorption curve, comparing the curves of different scanning points in the tumor, and analyzing the heterogeneity in the tumor; meanwhile, the curve of the tumor is compared with the curve of the normal tissue, and the heterogeneity of the tumor and the normal tissue is analyzed. The non-ionization and non-invasive characteristics of terahertz waves are utilized, the terahertz spectrum information of the skin tumors is obtained in a non-invasive mode, and then the heterogeneity in the tumors and between the tumors is accurately detected.

Description

Technical Field

[0001] The present invention relates to the fields of terahertz technology, medicine and tumor technology, and in particular to a skin tumor heterogeneity detection system and method based on terahertz time-domain spectroscopy. Background Art

[0002] Skin and soft tissue tumors are one of the important diseases that affect human health and appearance. Take melanoma as an example. It is the fifth most common cancer in the world, and its incidence and mortality rate are increasing year by year. The survival rate of melanoma patients is closely related to the stage and depth of the tumor. For example, the five-year survival rate of patients with localized melanoma after surgical resection is over 97%; but once distant metastasis occurs, the patient's survival rate drops to less than 10%. Therefore, early diagnosis and treatment are crucial to improve the survival rate of melanoma and improve the prognosis of patients. However, how to accurately identify and diagnose melanoma is still a major problem in clinical practice.

[0003] At present, the diagnostic methods for skin tumors mainly include clinical manifestations, histopathology, imaging, and biomarker detection. Among them, clinical manifestation diagnosis is the most commonly used method, but it depends on the doctor's experience and has certain subjectivity and limitations. Histopathology, as the "gold standard" for diagnosis, has high accuracy, but it requires invasive sampling, is complex to operate, is costly, and time-consuming, and is usually used for cases that cannot be diagnosed by other means. In addition, imaging and biomarker detection methods are also limited in practical applications due to their insufficient sensitivity and specificity, and have not yet been widely used in clinical practice.

[0004] In recent years, more and more studies have incorporated tumor heterogeneity into the study of benign and malignant diagnosis. Tumor heterogeneity is a significant feature of tumors, which means that different tumor cells can exhibit different genetic and phenotypic characteristics. These characteristics include differences in cell karyotype, gene mutation, copy number variation, cell morphology, gene expression, proteins and markers. Tumor heterogeneity is not only manifested between different types of tumors, that is, there are significant differences between different types of tumors; it also exists in the performance differences of the same type of tumor in different patients, and even between different cells in the same tumor. It is precisely because of these genetic and phenotypic differences that tumors vary in degree of differentiation, invasiveness, metastatic ability and resistance to treatment. This heterogeneity directly affects the choice of treatment options and the prognosis of patients. In addition, tumor heterogeneity also plays an important indicative role in judging the benign and malignant nature of tumors. Generally, benign tumors have lower cellular heterogeneity, while malignant tumors often have higher heterogeneity. Higher heterogeneity usually means more complex treatment challenges and poorer prognosis.

[0005] In this context, terahertz (THz) technology has gradually become a hot topic in related research due to its unique advantages. Terahertz waves refer to electromagnetic waves with frequencies between 0.1 and 10 THz and wavelengths between 3 and 30 microns. Terahertz radiation can be strongly absorbed by water molecules, so its penetration depth is only tens to hundreds of microns, which is very suitable for imaging superficial tissues such as skin. Terahertz technology has the characteristics of non-ionization, non-invasiveness and sensitivity to polar molecules (such as water molecules), and has shown application potential in fields such as burn skin monitoring. However, in the detection and research of skin tumors and their heterogeneity, terahertz technology has not yet been ideally applied and developed.

[0006] Most diagnostic methods in the prior art (such as histopathology) rely on invasive sampling, which is complex, time-consuming, and often causes discomfort to patients. More importantly, these methods can only analyze local tissues, and it is difficult to conduct a comprehensive and integrated evaluation of the entire tumor non-invasively, especially when the tumor heterogeneity is high, and it is impossible to accurately reflect the cell differences in different areas within the tumor. Therefore, the prior art has significant limitations in the assessment of tumor heterogeneity.

[0007] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0008] The purpose of the present invention is to solve the technical problems existing in the background technology. To this end, a skin tumor heterogeneity detection system and method based on terahertz time-domain spectroscopy are provided.

[0009] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0010] The skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy includes:

[0011] Terahertz scanner, used to obtain terahertz time domain signals of tumor tissue and surrounding normal tissue;

[0012] A signal processing module is used to generate terahertz absorption curves of tumors and normal tissues by Fourier transforming the acquired terahertz time domain signal;

[0013] An analysis unit, used to extract characteristic parameters of the terahertz absorption curve and compare heterogeneity within and between tumors;

[0014] The output display module is used to display the analysis results, which include intra-tumor heterogeneity, inter-tumor heterogeneity and comprehensive evaluation of tumor heterogeneity.

[0015] The skin tumor heterogeneity detection method based on terahertz time-domain spectroscopy is implemented by the above-mentioned skin tumor heterogeneity detection system, and includes the following steps:

[0016] Step S1, scanning: using a terahertz scanner to perform multi-point scanning on the tumor and its surrounding normal tissues to obtain their terahertz time domain signals;

[0017] Step S2, signal processing: the signal processing module converts the collected terahertz time domain signal into a terahertz absorption curve through Fourier transform;

[0018] Step S3, parameter extraction: the analysis unit extracts multiple characteristic parameters from the terahertz absorption curve, including absorption coefficient, absorption peak, absorption peak area, and slope change;

[0019] Step S4, parameter comparison: comparing the curves of different scanning points inside the tumor to analyze the heterogeneity inside the tumor; at the same time, comparing the curve of the tumor with the curve of normal tissue to analyze the heterogeneity between the tumor and normal tissue;

[0020] Step S5, heterogeneity calculation:

[0021] Calculation of intratumor heterogeneity: Based on the absorption peak area of ​​the curve at each scanning point, the ratio of the minimum absorption peak area to the maximum absorption peak area in the tumor is calculated, and combined with the slope of the curve change, the intratumor heterogeneity is calculated using a regression model;

[0022] Calculation of heterogeneity between tumors: Compare the absorption peak area and curve slope of tumors and normal tissues to calculate the heterogeneity between tumors and normal tissues.

[0023] The following is a technical solution further defined by the method of the present invention, in which a multi-point scan is performed on the tumor, including at least five scanning points at the center and surrounding areas of the tumor.

[0024] The following is a technical solution further defined by the method of the present invention, which performs symmetrical scanning on normal tissue, away from the tumor, and obtains normal tissue signals for comparison with the tumor tissue.

[0025] The following is a technical solution further defined by the method of the present invention. The absorption curve A(v) of the time domain signal S(t) in the frequency domain is obtained by the following steps:

[0026] Use Fourier transform to convert the time domain signal S(t) into the frequency domain signal S(v):

[0027]

[0028] Where v is the frequency, t is the time, and S(t) is the measured time domain signal;

[0029] The terahertz absorption curve A(v) is represented by the amplitude and phase characteristics of the frequency domain signal S(v):

[0030]

[0031] Where k(v) is the absorption coefficient of the material, extracted from the complex optical constants, n(v) is the complex refractive index, ν is the frequency, c is the speed of light, and Im means taking the imaginary part of the complex number.

[0032] The following is a technical solution further defined in the method of the present invention. The absorption coefficient of step S3 is given by the imaginary part of the absorption curve and is calculated by extracting the amplitude of the frequency domain signal:

[0033]

[0034] Where n(v) is the complex refractive index, ν is the frequency, c is the speed of light, and Im represents the imaginary part of the complex number;

[0035] The absorption peak value in step S3 refers to the frequency point showing a local maximum value in the absorption curve, and the absorption peak position in the curve and its corresponding absorption intensity are identified by the derivative method, and then the local maximum value of the curve is calculated to extract the absorption peak;

[0036] The absorption peak area of ​​step S3 is the integral of the absorption curve within a specific frequency range, reflecting the total absorption intensity within the frequency range. The absorption peak area is obtained by integrating a specific area of ​​the absorption curve:

[0037]

[0038] Where A(v) is the absorption curve, v start and v end are the start and end frequencies of the absorption peak;

[0039] The slope change in step S3 is used to measure the rate of change of the absorption curve, which is obtained by calculating the derivative of the absorption curve in the area before and after the absorption peak, that is,

[0040] The following is a technical solution further defined in the method of the present invention, which includes:

[0041] Extract absorption peak area:

[0042] For each scanning point of the terahertz absorption curve, extract its absorption peak area A peak,i , where i is the index of the scan point;

[0043] The calculation formula for the absorption peak area is:

[0044]

[0045] Among them A i (ν) is the absorption curve of the i-th scanning point, ν start and νend are the start and end frequencies of the absorption peak;

[0046] Calculate the ratio of the minimum and maximum absorption peak areas:

[0047] Calculate the minimum absorption peak area A in the tumor from the absorption peak areas of all scanning points. min and the maximum absorption peak area A max ;

[0048] The ratio formula is:

[0049]

[0050] Calculate the slope change:

[0051] The slope change of the absorption curve at each scanning point is calculated by calculating the derivative of the absorption curve. The calculation formula for the slope change is:

[0052]

[0053] Calculate the slope change ΔS at each scanning point i And calculate the standard deviation σ of these changes slope , to reflect the complexity of the curve changes within the tumor;

[0054] Calculate the heterogeneity within the tumor:

[0055] The heterogeneity within the tumor was quantified according to the regression model, with the absorption peak area ratio and slope change as independent variables and the heterogeneity within the tumor as the dependent variable. The formula is as follows:

[0056]

[0057] Among them, β0 is a constant term, β1 and β2 are regression coefficients, ∈ is an error term, and H in Score the heterogeneity between tumors.

[0058] The following is a technical solution further defined in the method of the present invention, which includes:

[0059] Calculate the difference in absorption peak area:

[0060] Compare the absorption peak areas of tumors and normal tissues, calculate the difference in absorption peak areas between tumors and normal tissues, and reflect the difference in their absorption characteristics:

[0061] ΔA peak =A peak,tumor -A peak,normal

[0062] Calculate the slope difference:

[0063] Comparison of the absorption curve slopes of tumors and normal tissues The difference in slopes characterizes the optical response between tumors and normal tissues:

[0064] ΔS=Slope tumor -Slope normal

[0065] Calculate the heterogeneity between tumors:

[0066] The heterogeneity between tumors and normal tissues was quantified based on the regression model, and the heterogeneity between tumors was calculated:

[0067] H inter =β0+β1·ΔA peak +β2·ΔS+∈

[0068] Among them, β0 is a constant term, β1 and β2 are regression coefficients, ∈ is an error term, and H inter Score the heterogeneity between tumors.

[0069] The following is a technical solution further defined in the method of the present invention, wherein the analysis unit generates a comprehensive evaluation of tumor heterogeneity based on intra-tumor heterogeneity and inter-tumor heterogeneity in combination with a regression model, including:

[0070] Calculate the comprehensive evaluation: Based on the regression model obtained from previous training, calculate the comprehensive score:

[0071] H total =β0+β1·H in +β2·H inter +∈

[0072] H total H is the comprehensive heterogeneity score of the tumor. in and H inter is the intra-tumor and inter-tumor heterogeneity score; β0 is the intercept; β1 and β2 are regression coefficients, indicating the contribution of internal and external heterogeneity to the comprehensive score; ∈ is the error term.

[0073] The following is a technical solution further defined by the method of the present invention, wherein the output display module displays intra-tumor heterogeneity, inter-tumor heterogeneity, and comprehensive evaluation of tumor heterogeneity.

[0074] Compared with the prior art, the present invention has the following technical effects:

[0075] First, the non-invasive and non-intrusive detection of terahertz waves avoids the trauma and discomfort caused by invasive operations in traditional tissue pathology, and significantly improves patient acceptance. At the same time, terahertz waves are highly sensitive to water and polar molecules, and can capture the molecular differences inside the tumor and between the tumor and normal tissue, thereby providing a highly sensitive heterogeneity assessment, solving the problem that the existing technology is difficult to accurately assess tumor heterogeneity; the present invention can also quickly obtain diagnostic results, greatly shorten the diagnosis time, and improve the diagnostic efficiency. In addition, through the comprehensive evaluation of the regression model, the present invention can accurately judge the benign and malignant nature of the tumor, especially in the detection of irregular tumors or early tumors, providing a reliable guide for personalized treatment plans. Through the establishment of the terahertz skin tumor database and model optimization, the evaluation accuracy and adaptability have been further improved, and it has a wide range of clinical application prospects. The present invention effectively solves the problems of invasiveness, difficulty in heterogeneity assessment and long diagnosis time in traditional technologies, and provides a powerful tool for the judgment of benign and malignant tumors and personalized treatment.

[0076] The present invention is further described below in conjunction with the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art will be briefly introduced below. 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 paying creative work.

[0078] Figure 1 is a flow chart of the method of the present invention;

[0079] Figure 2 It is a terahertz absorption curve diagram of the present invention. DETAILED DESCRIPTION

[0080] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0081] Tumor heterogeneity refers to the significant differences in genetic and phenotypic characteristics of the same tumor or different tumor cells. These characteristics include karyotype, gene mutation, copy number variation, cell morphology, gene expression, and different expressions of proteins and markers. Tumors with high heterogeneity are usually more invasive and drug-resistant.

[0082] Terahertz technology is extremely sensitive to water and polar molecules (such as nucleic acids and proteins). Since there are significant differences in the composition of water, nucleic acids and proteins between malignant and benign tumors, these differences are reflected in the terahertz absorption curve. Benign tumor tissue is relatively uniform, so the terahertz curve changes less; while malignant tumors have complex cell structures and rapid metabolic activities, resulting in significant changes in the slope and absorption peak area of ​​the absorption curve, reflecting its high heterogeneity.

[0083] This embodiment provides a skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy, including the following components:

[0084] Terahertz scanner: used to obtain terahertz time domain signals of tumor tissue and surrounding normal tissue. The scanner collects terahertz reflection signals from different areas through coherent detection.

[0085] Signal processing module: used to generate terahertz absorption curves of tumors and normal tissues by Fourier transforming the acquired terahertz time domain signal.

[0086] Analysis unit: used to extract characteristic parameters of the terahertz absorption curve and compare heterogeneity within and between tumors.

[0087] Output display module: displays the analysis results, including intra-tumor heterogeneity, inter-tumor heterogeneity and comprehensive evaluation of tumor heterogeneity.

[0088] like Figure 1 As shown, this embodiment provides a method for detecting heterogeneity of skin tumors based on terahertz time-domain spectroscopy, which utilizes the non-ionizing and non-invasive characteristics of terahertz waves to obtain terahertz spectrum information of skin tumors in a non-invasive manner, and then accurately detects heterogeneity within and between tumors. The specific steps are as follows:

[0089] 1. Scanning: Use a terahertz scanner to perform multi-point scanning on the tumor and its surrounding normal tissue to obtain its terahertz time domain signal.

[0090] The absorption curve A(v) of the time domain signal S(t) in the frequency domain can be obtained by the following steps:

[0091] 1) Use Fourier transform to convert the time domain signal S(t) into the frequency domain signal S(v), expressed as:

[0092]

[0093] Here, v is frequency, t is time, and S(t) is the measured time domain signal.

[0094] 2) The terahertz absorption curve A(ν) is usually represented by the amplitude and phase characteristics of the frequency domain signal

[0095] The equation for the terahertz absorption curve A(ν) is:

[0096]

[0097] Where k(v) is the absorption coefficient of the material, which can usually be extracted from the complex optical constants. n(ν) is the complex refractive index, ν is the frequency, c is the speed of light, and Im means taking the imaginary part of the complex number.

[0098] Perform multi-point scanning on the tumor, including at least five scanning points in the center and around the tumor to ensure the representativeness of the scan data. Perform symmetrical scanning on normal tissue, as far away from the tumor as possible, to obtain normal tissue signals for comparison with the tumor tissue.

[0099] 2. Signal processing: The signal processing module converts the collected terahertz time domain signals into terahertz absorption curves through Fourier transform. These curves reflect the absorption characteristics of tissues at different frequencies and provide basic data for subsequent heterogeneity analysis.

[0100] 3. Parameter extraction: The analysis unit extracts multiple characteristic parameters from the terahertz absorption curve, including absorption coefficient, absorption peak, absorption peak area, slope change, etc. Specifically:

[0101] 1) The absorption coefficient is usually given by the imaginary part of the absorption curve and can be calculated by extracting the amplitude of the frequency domain signal:

[0102]

[0103] Among them, n(ν) is the complex refractive index, ν is the frequency, c is the speed of light, and Im represents the imaginary part of the complex number.

[0104] 2) The absorption peak refers to the frequency point that shows a local maximum in the absorption curve. Therefore, the absorption peak position in the curve and its corresponding absorption intensity are identified by the derivative method, and then the local maximum of the curve is calculated to extract the absorption peak.

[0105] 3) The absorption peak area is the integral of the absorption curve within a specific frequency range, reflecting the total absorption intensity within the frequency range. The absorption peak area is obtained by integrating a specific area of ​​the absorption curve.

[0106]

[0107] Where A(ν) is the absorption curve, v start and v end are the start and end frequencies of the absorption peak. Regions with larger slopes indicate faster changes in absorption characteristics, which usually occur near the absorption peak.

[0108] 4) The slope change is used to measure the rate of change of the absorption curve, especially in the area before and after the absorption peak, which can be obtained by calculating the derivative of the absorption curve, that is,

[0109] 4. Parameter comparison: Compare the curves of different scanning points inside the tumor to analyze the heterogeneity inside the tumor; at the same time, compare the curve of the tumor with the curve of normal tissue to analyze the heterogeneity between the tumor and normal tissue.

[0110] 5. Heterogeneous computing:

[0111] Calculation of intratumor heterogeneity: Based on the absorption peak area of ​​the curve at each scanning point, the ratio of the minimum absorption peak area to the maximum absorption peak area in the tumor is calculated, and combined with the slope of the curve change, the intratumor heterogeneity is calculated using a regression model; specifically:

[0112] 1) Extract the absorption peak area

[0113] For each scanning point of the terahertz absorption curve, extract its absorption peak area A peak,i , where i is the index of the scan point.

[0114] The calculation formula for the absorption peak area is

[0115]

[0116] Among them A i (ν) is the absorption curve of the i-th scanning point, ν start and νend are the start and end frequencies of the absorption peak.

[0117] 2) Calculate the ratio of the minimum and maximum absorption peak areas:

[0118] Calculate the minimum absorption peak area A in the tumor from the absorption peak areas of all scanning points. min and the maximum absorption peak area A max .

[0119] The ratio formula is:

[0120]

[0121] This ratio can reflect the heterogeneity of the absorption intensity inside the tumor. A lower ratio indicates greater heterogeneity within the tumor.

[0122] 3) Calculate the slope change:

[0123] The slope change of the absorption curve at each scanning point is calculated, usually by calculating the derivative of the absorption curve. The calculation formula for the slope change is:

[0124] The slope can be obtained by the difference method or by numerically calculating the derivative.

[0125] Calculate the slope change ΔS at each scanning point i And calculate the standard deviation σ of these changes slope , to reflect the complexity of the curve changes within the tumor.

[0126] 4) Calculate the heterogeneity within the tumor:

[0127] The heterogeneity within the tumor was quantified according to the regression model, with the absorption peak area ratio and slope change as independent variables and the heterogeneity within the tumor as the dependent variable. The formula is as follows:

[0128]

[0129] Among them, β0 is a constant term, β1 and β2 are regression coefficients, ∈ is an error term, and H in Score the heterogeneity between tumors.

[0130] Calculation of heterogeneity between tumors: Compare the absorption peak area and curve slope of tumors and normal tissues to calculate the heterogeneity between tumors and normal tissues. Specifically:

[0131] 1) Calculate the difference in absorption peak area:

[0132] The absorption peak areas of tumors and normal tissues were compared, and the difference in absorption peak areas between tumors and normal tissues was calculated to reflect the difference in their absorption characteristics.

[0133] ΔA peak =A peak,tumor -A peak,normal

[0134] 2) Calculate the slope difference:

[0135] The difference in the slope of the absorption curves of tumors and normal tissues can characterize the optical response between tumors and normal tissues.

[0136] ΔS=Slope tumor -Slope normal

[0137] 3) Calculation of tumor heterogeneity:

[0138] The heterogeneity between tumors and normal tissues was quantified based on the regression model. The heterogeneity between tumors was calculated as:

[0139] H inter =β0+β1·ΔA peak +β2·ΔS+∈

[0140] Among them, β0 is a constant term, β1 and β2 are regression coefficients, ∈ is an error term, and H inter Score the heterogeneity between tumors.

[0141] 6. Comprehensive evaluation: The analysis unit generates a comprehensive evaluation of tumor heterogeneity based on intra-tumor heterogeneity and inter-tumor heterogeneity combined with a regression model. The higher the heterogeneity, the higher the malignancy of the tumor and the worse the prognosis. Specifically:

[0142] 1) Calculate the comprehensive evaluation: Calculate the comprehensive score based on the regression model obtained from previous training

[0143] H total =β0+β1·H in +β2·H inter +∈

[0144] H total H is the comprehensive heterogeneity score of the tumor. in and H inter is the intra-tumor and inter-tumor heterogeneity score; β0 is the intercept; β1 and β2 are regression coefficients, indicating the contribution of internal and external heterogeneity to the comprehensive score; ∈ is the error term.

[0145] 2) Refer to the scoring standards obtained through training and compare the comprehensive score with them. If the comprehensive score is higher than the standard, it is considered that the risk of malignant tumor is higher, and if the comprehensive score is lower than the standard, it is considered that the risk of malignant tumor is lower.

[0146] 7. Result output: The final result is displayed through the output display module, showing the intra-tumor heterogeneity, inter-tumor heterogeneity and comprehensive evaluation of tumor heterogeneity. The comprehensive evaluation of tumor heterogeneity can be used to guide clinicians to make benign and malignant judgments and formulate treatment plans.

[0147] like Figure 2 As shown in Figure 1, six skin tumors were scanned by terahertz to obtain their terahertz spectra. They include three benign tumors and three malignant tumors, as follows:

[0148] Benign tumors: Benign 01 is a separate benign tumor; Benign 02 and Benign 03 are from different tissue areas of the same benign tumor.

[0149] In the terahertz absorption curve, benign tumors show the following characteristics:

[0150] The absorption curve changes slightly and the slope remains basically unchanged, indicating that the tissue properties in different regions are relatively consistent.

[0151] The rate of change of the area under the absorption curve is close to 1. For example, the curve areas of benign 02 and benign 03 are almost the same, indicating that the heterogeneity within the tumor is very low.

[0152] From this, it can be judged that the heterogeneity of benign tumors is low both between and within tumors, and the tissue is relatively uniform.

[0153] Malignant tumor: Malignant tumor 01 is a separate malignant tumor; Malignant tumor 02 and Malignant tumor 03 are from different tissue areas of the same malignant tumor.

[0154] The terahertz absorption curves of malignant tumors show obvious differences:

[0155] The absorption curves vary greatly. For example, the slope of malignant 01 changes significantly, and malignant 02 shows multiple absorption peaks, indicating that the internal structure of the tumor is complex and there is significant heterogeneity.

[0156] The rate of change of the area under the absorption curve is greater than 1. For example, the area difference between malignant 02 and malignant 03 is obvious, indicating that there are large differences in composition between different areas of the tumor.

[0157] Comprehensive analysis shows that the heterogeneity of malignant tumors is higher than that of benign tumors, both within the tumor and between different tumors, which is consistent with the complexity and irregular growth characteristics of malignant tumors.

[0158] The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the above disclosed methods and technical contents without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, all equivalent changes made according to the shape, structure and principle of the present invention without departing from the content of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy, characterized in that: include: Terahertz scanner, used to obtain terahertz time domain signals of tumor tissue and surrounding normal tissue; A signal processing module is used to generate terahertz absorption curves of tumors and normal tissues by Fourier transforming the acquired terahertz time domain signal; An analysis unit, used to extract characteristic parameters of the terahertz absorption curve and compare heterogeneity within and between tumors; The output display module is used to display the analysis results, which include intra-tumor heterogeneity, inter-tumor heterogeneity and comprehensive evaluation of tumor heterogeneity.

2. A skin tumor heterogeneity detection method based on terahertz time-domain spectroscopy, implemented by the skin tumor heterogeneity detection system according to claim 1, characterized in that: The steps include: Step S1, scanning: using a terahertz scanner to perform multi-point scanning on the tumor and its surrounding normal tissues to obtain their terahertz time domain signals; Step S2, signal processing: the signal processing module converts the collected terahertz time domain signal into a terahertz absorption curve through Fourier transform; Step S3, parameter extraction: the analysis unit extracts multiple characteristic parameters from the terahertz absorption curve, including absorption coefficient, absorption peak, absorption peak area, and slope change; Step S4, parameter comparison: comparing the curves of different scanning points inside the tumor to analyze the heterogeneity inside the tumor; at the same time, comparing the curve of the tumor with the curve of normal tissue to analyze the heterogeneity between the tumor and normal tissue; Step S5: Heterogeneity calculation: Calculation of intratumor heterogeneity: Based on the absorption peak area of ​​the curve at each scanning point, the ratio of the minimum absorption peak area to the maximum absorption peak area in the tumor is calculated, and combined with the slope of the curve change, the intratumor heterogeneity is calculated using a regression model; Calculation of heterogeneity between tumors: Compare the absorption peak area and curve slope of tumors and normal tissues to calculate the heterogeneity between tumors and normal tissues.

3. The method for detecting skin tumor heterogeneity based on terahertz time-domain spectroscopy according to claim 2, characterized in that: Perform multi-point scanning on the tumor, including at least five scanning points in the center and around the tumor.

4. The method for detecting skin tumor heterogeneity based on terahertz time-domain spectroscopy according to claim 3, characterized in that: Scan normal tissue symmetrically, away from the tumor, to obtain normal tissue signals for comparison with the tumor tissue.

5. The method for detecting skin tumor heterogeneity based on terahertz time-domain spectroscopy according to claim 2, characterized in that: The absorption curve A(v) of the time domain signal S(t) in the frequency domain is obtained by the following steps: Use Fourier transform to convert the time domain signal S(t) into the frequency domain signal S(v): Where v is the frequency, t is the time, and S(t) is the measured time domain signal; The terahertz absorption curve A(v) is represented by the amplitude and phase characteristics of the frequency domain signal S(v): Where κ(v) is the absorption coefficient of the material, extracted from the complex optical constants, n(v) is the complex refractive index, ν is the frequency, c is the speed of light, and Im represents the imaginary part of the complex number.

6. The method for detecting skin tumor heterogeneity based on terahertz time-domain spectroscopy according to claim 2, characterized in that: The absorption coefficient of step S3 is given by the imaginary part of the absorption curve, calculated by extracting the amplitude of the frequency domain signal: Where n(v) is the complex refractive index, ν is the frequency, c is the speed of light, and Im represents the imaginary part of the complex number; The absorption peak value in step S3 refers to the frequency point showing a local maximum value in the absorption curve, and the absorption peak position in the curve and its corresponding absorption intensity are identified by the derivative method, and then the local maximum value of the curve is calculated to extract the absorption peak; The absorption peak area of ​​step S3 is the integral of the absorption curve within a specific frequency range, reflecting the total absorption intensity within the frequency range. The absorption peak area is obtained by integrating a specific area of ​​the absorption curve: Where A(v) is the absorption curve, v start and v end are the start and end frequencies of the absorption peak; The slope change in step S3 is used to measure the rate of change of the absorption curve, which is obtained by calculating the derivative of the absorption curve in the area before and after the absorption peak, that is, 7. The method for detecting skin tumor heterogeneity based on terahertz time-domain spectroscopy according to claim 2, characterized in that: In the process of calculating the heterogeneity between tumors, the following are included: Extract absorption peak area: For each scanning point of the terahertz absorption curve, extract its absorption peak area A peak,i , where i is the index of the scan point; The calculation formula for the absorption peak area is: Among them A i (ν) is the absorption curve of the i-th scanning point, ν start and νend are the start and end frequencies of the absorption peak; Calculate the ratio of the minimum and maximum absorption peak areas: Calculate the minimum absorption peak area A in the tumor from the absorption peak areas of all scanning points. min and the maximum absorption peak area A max ; The ratio formula is: Calculate the slope change: The slope change of the absorption curve at each scanning point is calculated by calculating the derivative of the absorption curve. The calculation formula for the slope change is: Calculate the slope change ΔS at each scanning point i And calculate the standard deviation σ of these changes slope , to reflect the complexity of the curve changes within the tumor; Calculate the heterogeneity within the tumor: The heterogeneity within the tumor was quantified according to the regression model, with the absorption peak area ratio and slope change as independent variables and the heterogeneity within the tumor as the dependent variable. The formula is as follows: Among them, β0 is a constant term, β1 and β2 are regression coefficients, ∈ is an error term, and H in Score the heterogeneity between tumors.

8. The method for detecting skin tumor heterogeneity based on terahertz time-domain spectroscopy according to claim 2, characterized in that: In the process of calculating the heterogeneity between tumors, the following are included: Calculate the difference in absorption peak area: Compare the absorption peak areas of tumors and normal tissues, calculate the difference in absorption peak areas between tumors and normal tissues, and reflect the difference in their absorption characteristics: ΔA peak = Yes peak,tumor -IN peak,normal Calculate the slope difference: Comparison of the absorption curve slopes of tumors and normal tissues The difference in slopes characterizes the optical response between tumors and normal tissues: ΔS=Slope tumor -Slope normal Calculate the heterogeneity between tumors: The heterogeneity between tumors and normal tissues was quantified based on the regression model, and the heterogeneity between tumors was calculated: H inter =β0+β1·ΔA peak +β2·ΔS+∈ Among them, β0 is a constant term, β1 and β2 are regression coefficients, ∈ is an error term, and H inter Score the heterogeneity between tumors.

9. The method for detecting skin tumor heterogeneity based on terahertz time-domain spectroscopy according to claim 2, characterized in that: The analysis unit generates a comprehensive evaluation of tumor heterogeneity based on intra-tumor heterogeneity and inter-tumor heterogeneity combined with a regression model, including: Calculate the comprehensive evaluation: Based on the regression model obtained from previous training, calculate the comprehensive score: H total =β0+β1·H in +β2·H inter +∈ H total H is the comprehensive heterogeneity score of the tumor. in and H inter is the intra-tumor and inter-tumor heterogeneity score; β0 is the intercept; β1 and β2 are regression coefficients, indicating the contribution of internal and external heterogeneity to the comprehensive score; ∈ is the error term.

10. The method for detecting skin tumor heterogeneity based on terahertz time-domain spectroscopy according to claim 9, characterized in that: The output display module shows intra-tumor heterogeneity, inter-tumor heterogeneity and comprehensive evaluation of tumor heterogeneity.

Images