Cell survival rate prediction method and system based on Monte Carlo simulation carbon ion spectrum distribution

Abstract

The invention relates to the technical field of carbon ion radiotherapy relative biological effects, and discloses a cell survival rate prediction method and system based on Monte Carlo simulation carbon ion spectrum distribution. The method comprises the following steps: collecting experimental data of isolated cells to construct a cell database; based on the energy range of carbon ion radiotherapy particles and the type of secondary particles, a monte carlo method is used to construct a monoenergetic particle DNA damage database. And then constructing a cell survival rate prediction model based on DNA damage repair, and fitting cell radiation specificity parameters of the model based on a cell database to accurately reflect the radiation sensitivity of the cells. Then on the basis of the monoenergetic particle DNA damage database, the mixed beam DNA damage characteristics are determined in a dose weighting mode, the mixed beam DNA damage characteristics are input into the model subjected to parameter fitting to predict the cell survival rate, the secondary particle biological effect is considered, and the prediction accuracy of the clinical carbon ion beam relative biological effect can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of relative biological effects of carbon ion radiotherapy, and in particular to a method and system for predicting cell survival rate based on Monte Carlo simulation of carbon ion spectrum distribution. Background Art

[0002] Carbon ion radiotherapy has become one of the focuses of radiotherapy technology development in recent years because of its unique Bragg peak dose distribution characteristics and high relative biological effect. It can effectively and accurately irradiate tumor tissues while significantly reducing the radiation dose to surrounding normal tissues. The relative biological effect (RBE) can characterize the dose difference when carbon ions and conventional photon beams produce the same biological effect. It is an important dose-dependent quantity for the safe clinical implementation of this emerging technology of carbon ion radiotherapy.

[0003] In the prior art, models widely used in clinical carbon ion radiotherapy to calculate relative biological effect (RBE), such as the local effect model (LEM) and the microdose kinetic model (MKM), only consider the energy deposition distribution on the micrometer scale, which can only reflect that the ion energy deposition increases with the increase of the linear energy transfer density (LET), and cannot describe the contraction of the particle track that occurs at the same time. Therefore, it is impossible to directly describe the trend of reduced radiation quality biological effect due to the overkill effect in the high LET range. More and more studies have shown that the initial radiation damage to cells is closely related to the size of ionized clusters produced by charged particles on the DNA scale. Therefore, nanodose quantity is regarded as a better measure to describe the particle track structure and reveal the essential characteristics of carbon ion RBE.

[0004] In 2012, Frese et al. published “A mechanism-based approach to predict the relative biological effectiveness of protons and carbon ions in radiation therapy” in the journal Int J Radiation Oncol Biol Phys. They first proposed a relative biological effect model based on the yield of DSB (DNA double-strand breaks) as the input parameter of the model to calculate the biological radiation sensitivity parameters α and β. This model reflects the mechanism of radiation-induced cell biological effects to a certain extent. However, the model assumes that β is positively correlated with the square of the number of DSBs, which cannot reflect the overkill effect of the radiation beam. Therefore, its rationality is questionable. In 2018, Wang et al. published “Modelling of cellular survival following radiation-induced DNA double-strandbreaks” in the journal Sci Rep. Based on DSB as the model input, the model parameters were used to control the effect of the number of DSBs on cell damage. To a certain extent, it is consistent with the trend of the overkill effect on the model α and β values. However, this method only considers the biological damage caused by a single particle type to cells and is therefore not applicable to mixed beams composed of carbon ions and their secondary particles. In 2022, Buglewicz et al. published "Biological effects of monoenergeticcarbon ions and their associated secondary particles" in the journal Front Oncol. By visualizing the DSB distribution of particles of different types and energies, they demonstrated that secondary particles have different biological effects and deposit a non-negligible dose after the Bragg peak. However, existing nanodosimetry models ignore the biological effects caused by secondary particles during the interaction between carbon ions and matter. This may lead to inaccurate assessment of biological dose after the Bragg peak, causing radiation toxicity and secondary induced tumors in key organs. Summary of the invention

[0005] The present application provides a method for predicting cell viability based on Monte Carlo simulation of carbon ion spectrum distribution to solve the problem in the prior art that only the primary carbon ion biological effect is considered and the relative biological effect of the carbon ion beam cannot be accurately predicted.

[0006] Correspondingly, the present application also provides a cell viability prediction system based on Monte Carlo simulation of carbon ion spectrum distribution, an electronic device, and a computer-readable storage medium to ensure the implementation and application of the above method.

[0007] In order to solve the above technical problems, the present application discloses a method for predicting cell viability based on Monte Carlo simulation of carbon ion spectrum distribution, the method comprising:

[0008] Collect in vitro cell experimental data to build a cell database;

[0009] Based on the energy range of carbon ion radiotherapy particles and the types of secondary particles, a monoenergetic particle DNA damage database was constructed using the Monte Carlo method.

[0010] A cell survival prediction model based on DNA damage repair was constructed based on the theory of non-homologous end joining repair;

[0011] Fitting the cell radiation-specific parameters of the cell survival rate prediction model based on the cell database to obtain the target cell survival rate prediction model;

[0012] Based on the monoenergetic particle DNA damage database, the dose-weighted hybrid beam DNA damage characterization method was determined, and the hybrid beam DNA damage characteristics were calculated.

[0013] The mixed beam DNA damage characteristics are input into the target cell survival rate prediction model to predict the cell survival rate.

[0014] The present application also discloses a cell viability prediction system based on Monte Carlo simulation of carbon ion spectrum distribution, the system comprising:

[0015] A cell database construction module is used to collect in vitro cell experimental data and construct a cell database;

[0016] A single-energy particle DNA damage database construction module is used to construct a single-energy particle DNA damage database using the Monte Carlo method based on the carbon ion radiotherapy particle energy range and secondary particle type;

[0017] A cell survival rate prediction model construction module is used to construct a cell survival rate prediction model based on DNA damage repair based on the non-homologous end joining repair theory;

[0018] A model parameter fitting module is used to fit the cell radiation-specific parameters of the cell survival rate prediction model based on the cell database to obtain the target cell survival rate prediction model;

[0019] A DNA damage characterization module is used to determine a dose-weighted mixed beam DNA damage characterization method based on a monoenergetic particle DNA damage database and calculate the mixed beam DNA damage characteristics;

[0020] The prediction module is used to input the mixed beam DNA damage characteristics into the target cell survival rate prediction model to predict the cell survival rate.

[0021] The present application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, one or more methods described in the present application are implemented.

[0022] The present application also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, one or more methods described in the present application are implemented.

[0023] In this application, in vitro cell experimental data are collected to construct a cell database; based on the energy range of carbon ion radiotherapy particles and the type of secondary particles, a monoenergetic particle DNA damage database is constructed using the Monte Carlo method. Then, a cell survival rate prediction model based on DNA damage repair is constructed based on the theory of non-homologous end joining repair, and the cell radiation-specific parameters of the model are fitted based on the cell database, which can accurately reflect the radiation sensitivity of the cells and obtain a target cell survival rate prediction model. Afterwards, based on the monoenergetic particle DNA damage database, the mixed beam DNA damage characteristics are determined in a dose-weighted manner, and the mixed beam DNA damage characteristics are input into the target cell survival rate prediction model to predict cell survival rate, taking into account the biological effects of secondary particles, which can improve the prediction accuracy of the relative biological effects of clinical carbon ion beams.

[0024] Additional aspects and advantages of the present application will be given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A flow chart of a method for predicting cell viability based on Monte Carlo simulation of carbon ion spectrum distribution provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of a method for predicting cell viability based on Monte Carlo simulation of carbon ion spectrum distribution provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of a phantom for Monte Carlo simulation of a 290 MeV / u monoenergetic carbon ion beam provided in an embodiment of the present application;

[0029] Figure 4 A comparison chart of the Monte Carlo simulation and dose-weighted results of the number of DNA damages after the Bragg peak (140 mm-180 mm) of a 290 MeV / u monoenergetic carbon ion beam provided in an embodiment of the present application;

[0030] Figure 5 A comparison chart of experimental observation values ​​and model prediction values ​​after irradiation of CHO cells at different depths in the 290MeV / u monoenergetic carbon ion beam path provided in the embodiments of the present application;

[0031] Figure 6 A schematic diagram of the structure of a cell viability prediction system based on Monte Carlo simulation of carbon ion spectrum distribution provided in an embodiment of the present application;

[0032] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0034] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0035] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0036] The solution provided in the embodiment of the present application can be executed by any electronic device, such as a terminal device or a server, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, and the present application is not limited here. For the technical problems existing in the prior art, the cell viability prediction method and system based on Monte Carlo simulation of carbon ion spectrum distribution provided in the present application is intended to solve at least one of the technical problems of the prior art.

[0037] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0038] The embodiment of the present application provides a method for predicting cell viability based on Monte Carlo simulation of carbon ion spectrum distribution. The method can be executed by any electronic device, and optionally, can be executed on a server or terminal device.

[0039] like Figure 1 As shown in , the method may include the following steps:

[0040] Step 101, collecting in vitro cell experimental data to build a cell database.

[0041] Among them, the in vitro cell experiment database is used to store the cell survival rates of different cell lines under different irradiation conditions, and the existing resources can be used to establish a shared in vitro cell experiment database.

[0042] Step 102, based on the carbon ion radiotherapy particle energy range and secondary particle type, a monoenergetic particle DNA damage database is simulated using a Monte Carlo method.

[0043] Among them, the monoenergetic particle DNA damage database is used to store DNA damage data of different particle types and energies, and a DNA damage database widely applicable to various centers can be established according to the beam energies published by different carbon ion therapy centers. In the embodiment of the present application, a monoenergetic particle DNA damage database is pre-constructed to reduce the time cost of subsequent model prediction.

[0044] Step 103, constructing a cell survival rate prediction model based on DNA damage repair based on non-homologous end joining repair theory.

[0045] In the implementation of the present application, a cell survival rate prediction model can be constructed based on the mammalian DNA damage and non-homologous end joining repair theory.

[0046] Step 104 , fitting the cell radiation-specific parameters of the cell survival rate prediction model based on the cell database to obtain the target cell survival rate prediction model.

[0047] Step 105 : determining a dose-weighted mixed beam DNA damage characterization method based on the monoenergetic particle DNA damage database, and calculating and obtaining mixed beam DNA damage characteristics.

[0048] Step 106: Input the mixed beam DNA damage characteristics into the target cell survival rate prediction model to predict the cell survival rate.

[0049] In the examples of the present application, in vitro cell experimental data are collected to construct a cell database; based on the energy range of carbon ion radiotherapy particles and the type of secondary particles, a monoenergetic particle DNA damage database is constructed using the Monte Carlo method. Then, a cell survival rate prediction model based on DNA damage repair is constructed based on the theory of non-homologous end joining repair, and the cell radiation-specific parameters of the model are fitted based on the cell database, which can accurately reflect the radiation sensitivity of the cells and obtain a target cell survival rate prediction model. Afterwards, based on the monoenergetic particle DNA damage database, the mixed beam DNA damage characteristics are determined in a dose-weighted manner, and the mixed beam DNA damage characteristics are input into the target cell survival rate prediction model to predict cell survival rate, taking into account the biological effects of secondary particles, and can quickly and accurately predict the repair of cell damage caused by the mixed radiation beam, thereby improving the accuracy of predicting the relative biological effects of clinical carbon ion beams.

[0050] In an optional embodiment, the in vitro cell experimental data in the cell database includes the cell line type, the number of DNA base pairs, the size of the cell nucleus, the radiation beam energy, the linear energy transfer density (LET) of the radiation beam, the average number Y of DNA double-strand breaks deposited per unit dose of the radiation beam in each cell, and the average number λ of DNA double-strand breaks generated by each primary particle that causes DNA double-strand breaks. p , direct lethality coefficient α value and indirect lethality coefficient β value.

[0051] In an optional embodiment, based on the carbon ion radiotherapy particle energy range and secondary particle type, a monoenergetic particle DNA damage database is constructed using the Monte Carlo method, including:

[0052] Pre-simulate the beam according to the beam energy and range of carbon ion radiotherapy, record the image space files at different depths within the beam range, and analyze the types and energy range of secondary particles;

[0053] According to the types and energy ranges of secondary particles, a monoenergetic particle DNA damage database was constructed using the Monte Carlo method.

[0054] In an optional embodiment, the monoenergetic particle DNA damage database includes particle type, energy, energy transfer line density, the average number of DNA double-strand breaks (DSB) lesions after unit dose irradiation of cells, and the average number of DNA double-strand breaks generated by primary particles that cause DNA double-strand breaks.

[0055] In an optional embodiment, the expression of the cell survival rate prediction model is:

[0056] -lnS=αD+βD 2 (1)

[0057]

[0058] Where S is the cell survival rate; α and β are the direct and indirect lethal coefficients, respectively; D is the dose; μ x , μ y , ζ, ξ and η(λ p ) is a cell radiation-specific parameter; Y is the average number of DNA double-strand breaks per cell with a 1 Gy dose obtained by Monte Carlo simulation; λ p is the average number of DNA double-strand breaks generated by each primary particle causing DNA double-strand breaks obtained by Monte Carlo simulation.

[0059] In an optional embodiment, fitting the cell radiation-specific parameters of the cell survival rate prediction model based on the cell database to obtain the target cell survival rate prediction model includes:

[0060] Select target cell type data from the cell database;

[0061] Filter α>0Gy from target cell type data -1 , β>0Gy -2 The data were used to fit the cell radiation-specific parameters of the cell survival rate prediction model by the least squares method to obtain the target cell survival rate prediction model.

[0062] like Figure 2 As shown in , several in vitro cell experimental data can be obtained from the cell database to perform parameter fitting on the radiation-specific parameters of the model cells, thereby obtaining a prediction model for the survival rate of the target cells.

[0063] In the present embodiment, theoretical simulation construction is realized in the Python environment. Based on the in vitro cell experimental data in the cell database, a set of cell radiation-specific parameters suitable for various secondary particle types are fitted by the least squares method. The cell radiation-specific parameters include μ x, μ y , ζ, ξ and η(λ p ).

[0064] In an optional embodiment, determining a dose-weighted mixed beam DNA damage characterization method based on the monoenergetic particle DNA damage database, and calculating and obtaining mixed beam DNA damage characteristics, includes:

[0065] Based on the predicted cell radiation conditions, macroscopic Monte Carlo simulation is used to obtain the particle spectrum distribution at different depths within the beam range millimeter by millimeter; Figure 2 As shown, in the embodiment of the present application, for the new in vitro cell experiment, the Monte Carlo method is used to simulate the radiation conditions of the predicted cells, and the particle spectrum distribution (primary and secondary particle spectrum distribution) is obtained based on the Monte Carlo method.

[0066] Using the particle spectrum distribution as the key value, search in the monoenergetic particle DNA damage database to obtain the type and energy range of the target particle;

[0067] defining a radiation field in which the interaction occurs in a macroscopic voxel (e.g., a millimeter-sized voxel); wherein the average number of DNA lesions in the macroscopic voxel is weighted by the number of DNA lesions per particle associated with the particle track in the radiation field;

[0068] The type of target particles and the dose of energy range are used as weighted quantities of the mixed beam radiation characteristics in macroscopic voxels to determine the mixed beam DNA damage characterization method;

[0069] The mixed beam DNA damage characteristics are obtained according to the mixed beam DNA damage characterization method.

[0070] In an optional embodiment, the mixed beam DNA damage is characterized by:

[0071]

[0072] In the formula, The weighted average number of DNA double-strand breaks per cell with a dose of 1 Gy; is the average number of DNA double-strand breaks generated by each primary particle that causes DNA double-strand breaks obtained by weighted calculation; (λ p ) p,E The average number of DNA double-strand breaks generated by each primary particle with energy E and type p that causes DNA double-strand break damage; Y p,E is the number of DNA double-strand breaks in a cell when p-type particles with energy E deposit 1 Gy dose; ΔD p,E is the dose deposited by p-type particles with energy E within a macroscopic voxel.

[0073] According to formulas (4) and (5), the mixed beam DNA damage characteristics can be accurately described, where the mixed beam DNA damage characteristic value is and

[0074] In the present application embodiment, Figure 2 As shown, the dose-weighted DNA damage characteristic value and The target cell survival rate is input into the prediction model to predict the cell survival rate under the corresponding experimental conditions, and finally the predicted value of the new in vitro cell survival rate is obtained.

[0075] As a first example, the method in the embodiment of the present application is further described by taking CHO cells as an example:

[0076] The present application example predicts the survival rate of CHO cells. 131 CHO in vitro cell experimental data are selected for fitting the cell radiation-specific parameters of the model. The in vitro cell survival rate of an experiment conducted by the National Institutes for Quantum Science and Technology (QST) of Japan is predicted (290 MeV / u monoenergetic carbon ion beam irradiation of CHO cells), and the model prediction results are compared with the experimental observations.

[0077] First, the effective values ​​of the model of CHO in vitro cell experimental data were collected and sorted. The effective values ​​of the model included the physical beam conditions and experimental observation values ​​during the in vitro cell experiment. The physical beam conditions included the radiation energy, type, LET, DNA damage data values ​​Y and λ p , the experimental observation values ​​include α and β values.

[0078] In the present embodiment, the experimental data including H + 、He 2+ , C 6+ 、Ne 10+ 4 beam types, LET range 1~700keV / μm, beam energy range 0.5-500MeV / u, α>0Gy -1 , β>0Gy -2 , a total of 131 items.

[0079] Secondly, based on the types and energies of carbon ions and their main secondary particles in carbon ion radiotherapy, a 0.1-1000 MeV H + Ions and 1~1000MeV / u He 2+ , Li 3+ 、Be 4+ , B 5+ , C 6+ 、N7+ , O 8+ The database contains the particle type, energy, energy density, average number of DSB damage per unit dose after irradiation, average number of DSBs generated by primary particles causing DSB damage, and average number of DSBs generated by primary particles causing DSB damage. p .

[0080] Next, the cell radiation-specific parameter fitting of the model was implemented by the least squares method in the Python environment. The specific fitting process of the parameters is as follows:

[0081] In the present embodiment, the radiation beam DNA damage characteristic values ​​Y and λ of the CHO in vitro cell experimental data are p , α value is input into formula (2) to get μ x , μ y The fitted four cell radiation specific parameters and β value are used as the input of formula (3) to obtain the cell radiation specific parameter η(λ p ).

[0082] Then, after the model training optimal parameters are fitted, for new in vitro cell experimental data, it is necessary to first obtain accurate particle spectrum distribution through Monte Carlo simulation according to its experimental conditions, and find the corresponding DNA damage data values ​​Y and λ in the pre-built monoenergetic particle DNA damage database. p The DNA damage characteristics of the mixed beam were characterized using a dose-weighted approach. and The calculated DNA damage characteristic value is input into the model with the fitted parameters (i.e., the target cell survival rate prediction model) to obtain the survival rate prediction value of the new in vitro cell experiment. In the embodiment of the present application, the model setting during the simulation experiment is as follows:

[0083] like Figure 3 As shown in the figure, a 2 mm thick polystyrene sheet is placed in front of the water phantom to simulate the thickness of the bottom of the cell culture bottle. The size of the water phantom is set to a 0.4 cm × 0.4 cm × 20 cm cuboid, and the physical process is set to QG_SP_BIC_HP. A 270.5 MeV / u monoenergetic carbon ion beam is incident vertically from the center of the left side of the phantom, showing a Gaussian distribution δ in the vertical and horizontal directions. x =δ y =1mm (full width at half maximum is 2.35mm), and the scattering angle is set to None. The number of incident carbon ions is determined according to the measurement results of detector A (at 10mm), and the incidence is stopped when the deposited dose at detector A is 2, 3, 5, and 10Gy respectively. Detector B is placed at the Bragg peak and after the peak (140-180mm) to count the flux, dose, and energy spectrum distribution of carbon ions and their secondary particles in the phantom millimeter by millimeter.

[0084] In order to verify the accuracy of the weighting method, the present application example compares the DSB value of Monte Carlo simulation at 140mm-180mm after the Bragg peak when a dose of 2Gy is deposited at a beam position of 10mm, and the DSB damage value calculated by dose weighting, and calculates the root mean square error, thereby evaluating the accuracy of the weighting method. Figure 4 As shown, the error mean and variance are 1.39% ± 1.98% and 0.64% ± 1.30% respectively. Figure 4 is the average number of DSB damage in cells after unit dose of mixed radiation beam deposited by Monte Carlo method and dose-weighted method comparison; Figure 4 is the average number of DSBs generated by the primary particles caused by the above two methods comparison.

[0085] In order to verify the effect of dose weighting on cell survival rate, the present embodiment compares the prediction results of cell survival rate considering only a single particle type and characterizing the beam DNA damage characteristics according to dose weighting, and calculates the root mean square error between the model prediction value and the experimental observation value, thereby evaluating the accuracy of the model prediction dose weighting method. Figure 5 As shown, considering only carbon ions and using the dose-weighted prediction method, the mean error and variance of the results compared with the in vitro cell experimental measurements in the Bragg peak region (140-142 mm) are 0.6% ± 0.4% and 0.3% ± 0.24%, respectively, and the mean error and variance of the results compared with the in vitro cell experimental measurements after the Bragg peak (142-180 mm) are 4.6% ± 0.4% and 2.3% ± 0.24%, respectively. Figure 5 When Figure 3 Comparison of cell observation values ​​and model prediction values ​​in the range after the Bragg peak (140mm-180mm) when 2, 3, 5, and 10Gy doses are deposited at detector position A as shown, where the blue line represents the model prediction value considering only carbon ions, and the yellow line represents the model prediction value based on particle spectrum distribution.

[0086] Based on the same principle as the method provided in the embodiment of the present application, the embodiment of the present application also provides a cell viability prediction system based on Monte Carlo simulation of carbon ion spectrum distribution, such as Figure 6 As shown, the system comprises:

[0087] A cell database construction module 601 is used to collect in vitro cell experimental data to construct a cell database;

[0088] A single-energy particle DNA damage database construction module 602 is used to construct a single-energy particle DNA damage database using a Monte Carlo method based on the carbon ion radiotherapy particle energy range and secondary particle type;

[0089] A cell survival rate prediction model construction module 603 is used to construct a cell survival rate prediction model based on DNA damage repair based on non-homologous end joining repair theory;

[0090] A model parameter fitting module 604 is used to fit the cell radiation specific parameters of the cell survival rate prediction model based on the cell database to obtain a target cell survival rate prediction model;

[0091] A DNA damage characterization module 605 is used to determine a dose-weighted mixed beam DNA damage characterization method based on a monoenergetic particle DNA damage database, and calculate and obtain mixed beam DNA damage characteristics;

[0092] The prediction module 606 is used to input the mixed beam DNA damage characteristics into the target cell survival rate prediction model to predict the cell survival rate.

[0093] In the examples of the present application, in vitro cell experimental data are collected to construct a cell database; based on the energy range of carbon ion radiotherapy particles and the type of secondary particles, a monoenergetic particle DNA damage database is constructed using the Monte Carlo method. Then, a cell survival rate prediction model based on DNA damage repair is constructed based on the theory of non-homologous end joining repair, and the cell radiation-specific parameters of the model are fitted based on the cell database, which can accurately reflect the radiation sensitivity of the cells and obtain a target cell survival rate prediction model. Afterwards, based on the monoenergetic particle DNA damage database, the mixed beam DNA damage characteristics are determined in a dose-weighted manner, and the mixed beam DNA damage characteristics are input into the target cell survival rate prediction model to predict cell survival rate, taking into account the biological effects of secondary particles, and can quickly and accurately predict the repair of cell damage caused by the mixed radiation beam, which can improve the accuracy of predicting the relative biological effects of clinical carbon ion beams.

[0094] The cell viability prediction system based on Monte Carlo simulation of carbon ion spectrum distribution provided in the embodiment of the present application can achieve Figures 1 to 5 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0095] The cell viability prediction system based on Monte Carlo simulation of carbon ion spectrum distribution in the embodiments of the present application can execute the cell viability prediction method based on Monte Carlo simulation of carbon ion spectrum distribution provided in the embodiments of the present application, and the implementation principle is similar. The actions performed by each module and unit in the cell viability prediction system based on Monte Carlo simulation of carbon ion spectrum distribution in each embodiment of the present application correspond to the steps in the cell viability prediction method based on Monte Carlo simulation of carbon ion spectrum distribution in each embodiment of the present application. For the detailed functional description of each module of the cell viability prediction system based on Monte Carlo simulation of carbon ion spectrum distribution, please refer to the description of the corresponding cell viability prediction method based on Monte Carlo simulation of carbon ion spectrum distribution shown in the previous text, which will not be repeated here.

[0096] Based on the same principle as the method shown in the embodiment of the present application, the embodiment of the present application also provides an electronic device, which may include but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for executing the cell survival rate prediction method based on Monte Carlo simulation of carbon ion spectrum distribution shown in any optional embodiment of the present application by calling a computer program. Compared with the prior art, the cell survival rate prediction method based on Monte Carlo simulation of carbon ion spectrum distribution provided in the present application collects in vitro cell experimental data to build a cell database; based on the energy range of carbon ion radiotherapy particles and the type of secondary particles, a monoenergetic particle DNA damage database is constructed using the Monte Carlo method. Then, based on the theory of non-homologous end joining repair, a cell survival rate prediction model based on DNA damage repair is constructed, and based on the cell database fitting model's cell radiation-specific parameters, the radiation sensitivity of the cell can be accurately reflected to obtain a target cell survival rate prediction model. Then, based on the monoenergetic particle DNA damage database, the mixed beam DNA damage characteristics were determined in a dose-weighted manner, and the mixed beam DNA damage characteristics were input into the target cell survival rate prediction model to predict the cell survival rate. The secondary particle biological effects were taken into account, and the hybrid radiation beam-induced cell damage and repair conditions could be predicted quickly and accurately, which can improve the accuracy of predicting the relative biological effects of clinical carbon ion beams.

[0097] In an optional embodiment, an electronic device is also provided, such as Figure 7 As shown, Figure 7 The electronic device 700 shown may be a server, including: a processor 701 and a memory 703. The processor 701 and the memory 703 are connected, such as through a bus 702. Optionally, the electronic device 700 may further include a transceiver 704. It should be noted that in actual applications, the transceiver 704 is not limited to one, and the structure of the electronic device 700 does not constitute a limitation on the embodiments of the present application.

[0098] Processor 701 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 701 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0099] The bus 702 may include a path to transmit information between the above components. The bus 702 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 702 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0100] The memory 703 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0101] The memory 703 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 701. The processor 701 is used to execute the application code stored in the memory 703 to implement the contents shown in the above method embodiment.

[0102] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0103] The server provided in this application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected by wired or wireless communication, and this application does not limit this.

[0104] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0105] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0106] It should be noted that the computer-readable storage medium mentioned above in the present application can also be a computer-readable signal medium or a combination of a computer-readable storage medium and a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0107] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0108] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0109] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the cell viability prediction method and system based on Monte Carlo simulation of carbon ion spectrum distribution provided in the above-mentioned various optional implementations.

[0110] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0112] The modules involved in the embodiments described in the present application can be implemented by software or hardware. The name of the module does not limit the module itself in some cases. For example, the cell database construction module can also be described as "a cell database construction module for collecting in vitro cell experimental data to construct a cell database".

[0113] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A method for predicting cell viability based on Monte Carlo simulation of carbon ion spectrum distribution, characterized in that: The method comprises: Collect in vitro cell experimental data to build a cell database; Based on the energy range of carbon ion radiotherapy particles and the types of secondary particles, a monoenergetic particle DNA damage database was constructed using the Monte Carlo method. A cell survival prediction model based on DNA damage repair was constructed based on the theory of non-homologous end joining repair; Fitting the cell radiation-specific parameters of the cell survival rate prediction model based on the cell database to obtain a target cell survival rate prediction model; Determining a dose-weighted mixed beam DNA damage characterization method based on the monoenergetic particle DNA damage database, and calculating and obtaining mixed beam DNA damage characteristics; The mixed beam DNA damage characteristics are input into the target cell survival rate prediction model to predict the cell survival rate.

2. The method for predicting cell viability based on Monte Carlo simulation of carbon ion spectrum distribution according to claim 1, characterized in that: The in vitro cell experimental data in the cell database include cell line type, number of DNA base pairs, cell nucleus size, radiation beam energy, radiation beam energy transfer line density, the average number of DNA double-strand breaks deposited per unit dose of the radiation beam in each cell, the average number of DNA double-strand breaks generated by each primary particle that causes DNA double-strand breaks, direct lethality coefficient and indirect lethality coefficient.

3. The method for predicting cell viability based on Monte Carlo simulation of carbon ion spectrum distribution according to claim 1, characterized in that: The monoenergetic particle DNA damage database is constructed using the Monte Carlo method based on the carbon ion radiotherapy particle energy range and secondary particle type, including: Pre-simulate the beam according to the beam energy and range of carbon ion radiotherapy, record the image space files at different depths within the beam range, and analyze the types and energy range of secondary particles; According to the types and energy ranges of secondary particles, the monoenergetic particle DNA damage database is constructed using the Monte Carlo method.

4. The method for predicting cell viability based on Montero simulation of carbon ion spectrum distribution according to claim 1, characterized in that: The expression of the cell survival rate prediction model is: -lnS=αD+βD 2 Where S is the cell survival rate; α and β are the direct and indirect lethal coefficients, respectively; D is the dose; μ x , μ y , ζ, ξ and η(λ p ) is a cell radiation-specific parameter; Y is the average number of DNA double-strand breaks per cell at a dose of 1 Gy obtained by Monte Carlo simulation; λ p is the average number of DNA double-strand breaks generated by each primary particle causing DNA double-strand breaks obtained by Monte Carlo simulation.

5. The method for predicting cell viability based on Montero simulation of carbon ion spectrum distribution according to claim 2, characterized in that: Fitting the cell radiation-specific parameters of the cell survival rate prediction model based on the cell database to obtain a target cell survival rate prediction model comprises: Selecting target cell type data from the cell database; α>0Gy was selected from the target cell type data -1 , β>0Gy -2 The target cell survival rate prediction model is obtained by fitting the cell radiation-specific parameters of the cell survival rate prediction model using the least squares method.

6. The method for predicting cell viability based on Montero simulation of carbon ion spectrum distribution according to claim 3, characterized in that: The step of determining a dose-weighted mixed beam DNA damage characterization method based on the monoenergetic particle DNA damage database and calculating mixed beam DNA damage characteristics includes: Based on the predicted cell radiation conditions, the particle spectrum distribution at different depths within the beam range is obtained millimeter by millimeter using macroscopic Monte Carlo simulation; Using the particle spectrum distribution as a key value, searching in the monoenergetic particle DNA damage database to obtain the type and energy range of the target particle; Defining a radiation field in which an interaction occurs in a macroscopic voxel; wherein the average number of DNA lesions in the macroscopic voxel is obtained by weighting the number of DNA lesions per particle associated with the particle track in the radiation field; Determining a DNA damage characterization method of the mixed beam using the type of target particles and the dose of the energy range as a weighted amount of the mixed beam radiation characteristics in the macroscopic voxel; The mixed beam DNA damage characteristics are obtained according to the mixed beam DNA damage characterization method.

7. The method for predicting cell viability based on Montero simulation of carbon ion spectrum distribution according to claim 6, characterized in that: The mixed beam DNA damage characterization method is: In the formula, The weighted average number of DNA double-strand breaks per cell with a dose of 1 Gy; is the average number of DNA double-strand breaks generated by each primary particle that causes DNA double-strand breaks obtained by weighted calculation; (λ p ) p,E The average number of DNA double-strand breaks generated by each primary particle with energy E and type p that causes DNA double-strand break damage; Y p,E is the number of DNA double-strand breaks in a cell when p-type particles with energy E deposit 1 Gy dose; ΔD p,E is the dose deposited by p-type particles with energy E within a macroscopic voxel.

8. A cell viability prediction system based on Monte Carlo simulation of carbon ion spectrum distribution, characterized in that: The system comprises: A cell database construction module is used to collect in vitro cell experimental data and construct a cell database; A single-energy particle DNA damage database construction module is used to construct a single-energy particle DNA damage database using the Monte Carlo method based on the carbon ion radiotherapy particle energy range and secondary particle type; A cell survival rate prediction model construction module is used to construct a cell survival rate prediction model based on DNA damage repair based on the non-homologous end joining repair theory; A model parameter fitting module, used for fitting the cell radiation-specific parameters of the cell survival rate prediction model based on the cell database to obtain a target cell survival rate prediction model; A DNA damage characterization module, used to determine a dose-weighted mixed beam DNA damage characterization method based on the monoenergetic particle DNA damage database, and calculate and obtain mixed beam DNA damage characteristics; The prediction module is used to input the mixed beam DNA damage characteristics into the target cell survival rate prediction model to predict the cell survival rate.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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