Skin tumor heterogeneity detection system and method based on terahertz time-domain spectroscopy

Abstract

This invention belongs to the fields of terahertz technology, medicine, and oncology. It specifically discloses a system and method for detecting skin tumor heterogeneity based on terahertz time-domain spectroscopy. The system comprises: performing multi-point scanning of a tumor and surrounding normal tissue using a terahertz scanner to obtain terahertz time-domain signals, which are then converted into terahertz absorption curves. Multiple characteristic parameters are extracted from the terahertz absorption curves, and the curves at different scanning points within the tumor are compared to analyze the heterogeneity within the tumor. Simultaneously, the tumor curves are compared with those of normal tissue to analyze the heterogeneity between the two groups. This invention utilizes the non-ionizing and non-invasive characteristics of terahertz waves to non-invasively obtain terahertz spectral information of skin tumors, thereby accurately detecting heterogeneity within and between tumors.

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 among the most significant diseases affecting human health and appearance. Melanoma, for example, is the fifth most common cancer worldwide, with both incidence and mortality rates increasing annually. Melanoma survival is closely related to the stage and depth of the tumor. For example, after surgical resection, patients with localized melanoma have a five-year survival rate exceeding 97%. However, once distant metastasis occurs, the survival rate drops to less than 10%. Therefore, early diagnosis and treatment are crucial for improving melanoma survival and patient outcomes. However, accurately identifying and diagnosing melanoma remains a major challenge in clinical practice.

[0003] Currently, 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 relies on the physician's experience and has certain subjectivity and limitations. Histopathology, as the "gold standard" of diagnosis, although highly accurate, requires invasive sampling, is complex, costly, and time-consuming, and is usually used for cases that cannot be diagnosed by other means. In addition, imaging and biomarker detection methods have limitations in practical application due to their insufficient sensitivity and specificity, and have not yet been widely used in clinical practice.

[0004] In recent years, an increasing number of studies have incorporated tumor heterogeneity into the diagnosis of benign and malignant tumors. Tumor heterogeneity is a prominent characteristic of tumors, referring to the fact that different tumor cells can exhibit distinct genetic and phenotypic features. These features include differences in karyotype, gene mutations, copy number variations, cell morphology, gene expression, proteins, and markers. Tumor heterogeneity not only manifests itself between different tumor types, but also exists within different patients within the same tumor. Heterogeneity can even exist between different cells within the same tumor. These genetic and phenotypic differences lead to variations in tumor differentiation, invasiveness, metastatic potential, and resistance to treatment. This heterogeneity directly influences treatment options and patient prognosis. Furthermore, tumor heterogeneity plays an important role in determining whether a tumor is benign or malignant. Generally, benign tumors exhibit less cellular heterogeneity, while malignant tumors tend to exhibit greater heterogeneity. Higher heterogeneity often indicates more complex treatment challenges and a 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 existing diagnostic methods (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 localized tissue, making it difficult to provide a comprehensive and integrated non-invasive assessment of the entire tumor. This is especially true when tumors are highly heterogeneous, as they cannot accurately reflect cellular differences between different regions within the tumor. Therefore, existing technologies have significant limitations in assessing 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 prior art known to ordinary technicians in this 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] A terahertz scanner is 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 signals;

[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, including 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 within the tumor is calculated. Combined with the slope of the curve, 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 limited by the method of the present invention, which performs multi-point scanning 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 limited 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 frequency, t is 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 represents 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 in 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 in step S3 refers to the frequency point showing a local maximum in the absorption curve. The absorption peak position and its corresponding absorption intensity in the curve are identified by the derivative method, and then the local maximum of the curve is calculated to extract the absorption peak;

[0036] The absorption peak area in 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 the 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, in the process of calculating the heterogeneity between tumors:

[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 within 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, in the process of calculating the heterogeneity between tumors:

[0059] Calculate the difference in absorption peak area:

[0060] Compare the absorption peak areas of tumors and normal tissues, and calculate the difference in absorption peak areas between tumors and normal tissues to 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 intertumor heterogeneity:

[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 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: 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 using terahertz waves avoids the trauma and discomfort caused by invasive procedures in traditional histopathology, significantly improving patient acceptance. At the same time, terahertz waves are highly sensitive to water and polar molecules and can capture molecular differences within tumors and between tumors and normal tissues, thereby providing highly sensitive heterogeneity assessments and solving the problem of the difficulty of accurately assessing tumor heterogeneity in existing technologies. The present invention can also quickly obtain diagnostic results, greatly shortening the diagnosis time and improving diagnostic efficiency. In addition, through the comprehensive evaluation of the regression model, the present invention can accurately determine the benign and malignant nature of tumors, especially in the detection of irregular tumors or early tumors, providing a reliable guide for personalized treatment plans. Through the establishment of a terahertz skin tumor database and model optimization, the assessment accuracy and adaptability have been further improved, and it has broad 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 judging the benign and malignant nature of tumors and personalized treatment.

[0076] The present invention will be further described below with reference to 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 following briefly introduces the drawings required for use in the embodiments or the prior art. 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 any creative work.

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

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

[0080] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0081] Tumor heterogeneity refers to significant differences in genetic and phenotypic characteristics within the same tumor or among different tumor cells. These characteristics include karyotype, gene mutations, copy number variations, cell morphology, gene expression, and differential expression of proteins and markers. Tumors with high heterogeneity are often more aggressive and drug-resistant.

[0082] Terahertz technology is extremely sensitive to water and polar molecules (such as nucleic acids and proteins). Because malignant and benign tumors differ significantly in their composition of water, nucleic acids, and proteins, these differences are reflected in the terahertz absorption curve. Benign tumors are relatively uniform, resulting in minimal variability in the terahertz curve. However, malignant tumors, due to their complex cellular structure and rapid metabolic activity, exhibit significant variations in the slope and peak area of ​​their absorption curves, reflecting their 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 from tumor tissue and surrounding normal tissue. The scanner collects terahertz reflection signals from different regions through coherent detection.

[0085] Signal processing module: used to generate terahertz absorption curves of tumors and normal tissues through Fourier transform of 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 skin tumor heterogeneity based on terahertz time-domain spectroscopy. By utilizing the non-ionizing and non-invasive characteristics of terahertz waves, terahertz spectral information of skin tumors is obtained in a non-invasive manner, thereby accurately detecting heterogeneity within and between tumors. The specific steps are as follows:

[0089] 1. Scanning: Use a terahertz scanner to perform multi-point scanning of 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), which is 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 complex optical constants. 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.

[0098] Perform multi-point scans of the tumor, including at least five scan points around the tumor center and its periphery, to ensure representativeness of the scan data. Perform symmetrical scans of 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, providing 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] Where 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 and its corresponding absorption intensity in the curve 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 that frequency range. The absorption peak area is obtained by integrating a specific region 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 within 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. Heterogeneity calculation:

[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 within the tumor is calculated. Combined with the slope of the curve, the intratumor heterogeneity is calculated using a regression model. Specifically:

[0112] 1) Extract 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 within 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 absorption intensity within 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 computing 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 be used to characterize the optical response between tumors and normal tissues.

[0136] ΔS=Slope tumor -Slope normal

[0137] 3) Calculation of inter-tumor heterogeneity:

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

[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. Higher heterogeneity generally indicates a higher degree of tumor malignancy and a worse prognosis. Specifically:

[0142] 1) Calculate comprehensive evaluation: Calculate 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) Compare the comprehensive score with the trained scoring standard. If the comprehensive score is higher than the standard, it is considered to be at a higher risk of malignant tumor; if the comprehensive score is lower than the standard, it is considered to be at a lower risk of malignant tumor.

[0146] 7. Result Output: The final results are displayed through the output display module, showing intra-tumor heterogeneity, inter-tumor heterogeneity, and a comprehensive evaluation of tumor heterogeneity. This comprehensive evaluation of tumor heterogeneity can be used to guide clinicians in making benign and malignant judgments and formulating treatment plans.

[0147] like Figure 2 As shown in Figure 1, six skin tumors were scanned by terahertz to obtain their terahertz spectra. These tumors included 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 01 is a separate malignant tumor; Malignant 02 and Malignant 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 varied significantly. For example, the slope of malignant 01 changed significantly, and malignant 02 showed multiple absorption peaks, suggesting that the internal structure of the tumor was complex and there was 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 the composition of 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 description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Any person skilled in the art can utilize the methods and technical contents disclosed above to make many possible variations and modifications to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, or modify them into equivalent embodiments with equivalent variations. Therefore, any equivalent variations made in accordance with the shape, structure, and principles of the present invention without departing from the content of the technical solutions of the present invention should be included in the scope of protection of the present invention.

Claims

1. A skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy, characterized by: include: A terahertz scanner is 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 signals; An analysis unit, used to extract characteristic parameters of the terahertz absorption curve and compare heterogeneity within and between tumors; An output display module is used to display the analysis results, including intra-tumor heterogeneity, inter-tumor heterogeneity, and comprehensive evaluation of tumor heterogeneity; The system is used to implement a skin tumor heterogeneity detection method based on terahertz time-domain spectroscopy, and the method comprises the following steps: 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 within the tumor is calculated. Combined with the slope of the curve, 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.

2. The skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy according to claim 1, characterized in that: Perform multi-point scanning of the tumor, including at least five scanning points in the center and surrounding areas of the tumor.

3. The skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy according to claim 2, characterized in that: Scan normal tissue symmetrically, away from the tumor, to obtain normal tissue signals for comparison with the tumor tissue.

4. The skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy according to claim 1, 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 ν is frequency, t is time, and S(t) is the measured time domain signal; The terahertz absorption curve A(ν) is represented by the amplitude and phase characteristics of the frequency domain signal S(ν): Where κ(ν) is the absorption coefficient of the material, 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 represents the imaginary part of the complex number.

5. The skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy according to claim 1, characterized in that: 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: 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 in step S3 refers to the frequency point showing a local maximum in the absorption curve. The absorption peak position and its corresponding absorption intensity in the curve are identified by the derivative method, and then the local maximum of the curve is calculated to extract the absorption peak; The absorption peak area in step S3 is the integral of the absorption curve within the frequency range, reflecting the total absorption intensity within the frequency range. The absorption peak area is obtained by integrating the area of ​​the absorption curve: Where A(ν) 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, 6. The skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy according to claim 1, characterized in that: The process of calculating intertumor heterogeneity includes: 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 within 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.

7. The skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy according to claim 1, characterized in that: The process of calculating intertumor heterogeneity includes: Calculate the difference in absorption peak area: Compare the absorption peak areas of tumors and normal tissues, and calculate the difference in absorption peak areas between tumors and normal tissues to 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 intertumor heterogeneity: 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.

8. The skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy according to claim 1, characterized in that: 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: Calculate 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.

9. The skin tumor heterogeneity detection system based on terahertz time-domain spectroscopy according to claim 8, characterized in that: The output display module shows intra-tumor heterogeneity, inter-tumor heterogeneity and comprehensive evaluation of tumor heterogeneity.

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