Method, terminal and computer storage medium for obtaining cell survival fraction model based on different depths

Abstract

The application provides an acquisition method of a cell survival fraction model based on different depths, a terminal and a medium, which comprises the following steps: determining a neutron beam corresponding to each target depth; for different target depths, using the neutron beam corresponding to the target depth, and performing a cell survival experiment based on a cell survival experiment device to obtain a plurality of sets of fitting sample pairs under the target depth; and using the fitting sample pairs under each target depth to fit the model parameters in a cell survival fraction initial model to obtain a cell survival fraction model corresponding to each target depth. The application can quickly obtain a cell survival fraction model suitable for different target depths, not only improves the acquisition efficiency of different target depth models, but also effectively improves the model accuracy of the cell survival fraction model by using a plurality of sets of fitting sample pairs to solve the model.

Description

Technical Field

[0001] This invention relates to the field of medical physics technology, and in particular to a method, terminal, and computer storage medium for obtaining cell survival fraction models based on different depths. Background Technology

[0002] In the development of existing boron neutron capture therapy (BNCT) plans / protocols, the relative biological effectiveness (RBE) is typically used to convert physical dose into biological dose. This RBE is the ratio of the dose required to induce a biological effect from 250 keV X-rays or gamma rays to the dose required to induce the same biological effect from observed ionizing radiation. Because neutrons are continuously slowed down as they penetrate human tissue, the RBE value changes for tumors at different depths.

[0003] In existing technologies, the RBE value for mixed absorbed doses is usually obtained from biological experiments using neutron source reactors, which often has low accuracy. To address this, existing technologies have developed methods based on cell survival fraction models to obtain the total RBE value corresponding to mixed absorbed doses. However, the cell survival fraction models currently used often utilize existing empirical or experimental models, and their model distribution differs significantly from the actual distribution of cell survival fractions at the target depth, resulting in low model accuracy. This, in turn, reduces the accuracy of the equivalent photon dose value obtained based on the model and makes estimation difficult. Furthermore, in actual BNCT treatment, the location of the tumor within the body often varies, meaning the depth of the radiation source often differs. For different radiation source depths, the paths traversed by various types of reaction particles in the boron neutron reaction differ, resulting in different cell survival fractions corresponding to the same absorbed dose; that is, different cell survival fraction models correspond to different depths.

[0004] Therefore, how to quickly obtain cell survival score models corresponding to different target depths has become a technical problem that needs to be solved in this field. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method, terminal and computer storage medium for obtaining cell survival fraction models based on different depths of boron neutron reaction, which can solve the problems of poor accuracy of existing cell survival fraction models.

[0006] To achieve the above and other related objectives, the present invention provides, in a first aspect, a method for obtaining a cell survival fraction model based on a boron neutron reaction, for obtaining cell survival fraction models corresponding to different target depths; the method for obtaining the cell survival fraction model includes:

[0007] Based on each preset target depth, a corresponding cell survival score model acquisition process is executed to obtain cell survival score models corresponding to different target depths. For a single target depth, the execution of the corresponding cell survival score model acquisition process includes: collecting several sets of fitted sample pairs at the current target depth; each fitted sample pair includes: the sample absorbed dose and the cell survival score at the current target depth corresponding to the sample absorbed dose; using each fitted sample pair, the model parameters in the initial cell survival score model to be fitted are fitted to obtain the cell survival score model corresponding to the current target depth; wherein the number of fitted sample pairs is adapted to the number of model parameters to be fitted in the cell survival score model. Attached Figure Description

[0008] Figure 1 The diagram shown is a flowchart of an embodiment of the method for obtaining the cell survival fraction model based on the boron neutron reaction provided by the present invention.

[0009] Figure 2 The diagram shows a flowchart of an embodiment of the method for collecting several sets of fitted sample pairs as described in this invention.

[0010] Figure 3 The diagram shown is a flowchart of step S101 as described in one embodiment of the present invention.

[0011] Figure 4 The diagram shows a flowchart of another embodiment of the method for obtaining the cell survival fraction model based on the boron neutron reaction described in this invention.

[0012] Figure 5 The diagram shows a flowchart of an embodiment of the method for constructing several sets of fitted sample pairs at the current target depth as described in this invention.

[0013] Figure 6 The diagram shown is a structural schematic of the cell survival experimental apparatus described in this invention in one embodiment.

[0014] Figure 7 The diagram shown is a structural schematic of the cell survival experimental apparatus described in this invention in another embodiment.

[0015] Figure 8 The diagram shows a flowchart of an embodiment of the present invention, which describes how the dose distribution modulator is adapted to the corresponding target depth by adjusting the type and thickness of the moderating material in each layer.

[0016] Figure 9 The diagram shown is a flowchart of step S802 as described in one embodiment of the present invention.

[0017] Figure 10 The diagram shown is a flowchart of an embodiment of the method for obtaining the total relative biological effect based on boron neutron reaction as described in this invention.

[0018] Figure 11 The diagram shown is a structural schematic of the terminal described in one embodiment of the present invention. Detailed Implementation

[0019] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0020] It should be noted that in the following description, reference is made to the accompanying drawings, which illustrate several embodiments of this application. It should be understood that other embodiments may also be used, and changes in mechanical composition, structure, electrical system, and operation may be made without departing from the spirit and scope of this application.

[0021] And, as used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of the stated feature, operation, element, component, item, kind, and / or group, but do not preclude the presence, occurrence, or addition of one or more other features, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are to be construed as inclusive, or mean any one or any combination thereof.

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the invention.

[0023] To address the technical problems existing in the prior art, this application first provides a method for obtaining a cell survival fraction model based on boron neutron reaction, which is used to obtain a cell survival fraction model corresponding to a target depth.

[0024] The cell survival fraction model is a model function used to characterize the relationship between the cell uptake dose of the mixed particles (hereinafter referred to as "mixed uptake dose") and the cell survival fraction; the mixed dose is the boron neutron reaction (…). 10 B captures a neutron, and an event occurs.10 B(n,α) 7 The sum of the cellular uptake doses corresponding to each reaction particle produced during the Li reaction.

[0025] It should be noted that the method for obtaining the cell survival fraction model based on boron neutron reaction provided in this application can also be applied to situations where the target depth is single or multiple.

[0026] Please see Figure 1 The diagram shows a flowchart of an embodiment of the method for obtaining the cell survival fraction model based on the boron neutron reaction provided in this application.

[0027] like Figure 1 As shown, the method includes the following steps:

[0028] S100: Collect several sets of fitted sample pairs at the current target depth;

[0029] The fitted sample pair is the sample data pair required for model fitting and solving the cell survival fraction model;

[0030] In a single fitted sample pair, the cell absorbed dose is included, and the cell survival fraction corresponding to the cell absorbed dose at the current target depth; that is, the cell survival fraction corresponding to the same cell absorbed dose is different at different target depths.

[0031] Specifically, in the cell survival experiment of the boron neutron reaction, several sets of fitted sample pairs were collected; the number of fitted sample pairs was matched with the number of model parameters to be fitted. For a single set of fitted sample pairs, the collection method was as follows:

[0032] Select a cell absorption dose and collect the cell survival fraction corresponding to that cell absorption dose at the current target depth; construct a set of fitting sample pairs by combining the collected cell survival fractions with the corresponding cell absorption doses.

[0033] In this embodiment, the number of fitted sample pairs is not less than the number of model parameters to be fitted in the cell survival fraction model; for example, when the number of model parameters to be fitted is 4, the number of fitted sample pairs constructed is not less than 4.

[0034] It should be noted that, in some specific embodiments, the number of sets of fitted sample pairs may be slightly less than the number of model parameters; for example, when the number of parameters of the model to be fitted is 4, the number of sets of fitted sample pairs constructed may also be 3.

[0035] S200, using each of the fitted sample pairs, fit the model parameters in the initial model of the cell survival score to be fitted, so as to obtain the cell survival score model corresponding to the current target depth.

[0036] Specifically, each set of fitted sample pairs at the current target depth is input into the cell survival score model; the model parameters to be fitted in the model are solved to obtain the optimal solution of each model parameter at the current target depth; and each optimal solution is substituted into the cell survival score model to obtain the cell survival score model at the current target depth.

[0037] The method for obtaining the cell survival fraction model based on the boron neutron reaction provided in this embodiment obtains the cell survival fraction model by collecting multiple sets of fitted sample pairs and solving the cell survival fraction model based on the fitted sample pairs. This not only improves the efficiency of obtaining the cell survival fraction model, but also effectively enhances the model accuracy of the cell survival fraction model.

[0038] In some optional embodiments, the model parameters in the initial model of cell survival fraction include linear coefficients and quadratic coefficients; wherein the linear coefficients and quadratic coefficients correspond to the effects of irreparable cell damage and repairable cell damage on the absorbed dose, respectively.

[0039] More specifically, the initial model for the cell survival fraction is as follows:

[0040]

[0041] Where D represents the absorbed dose and S represents the cell survival fraction. and These represent the first-order coefficient and the second-order coefficient corresponding to the absorbed dose, respectively.

[0042] It should be noted that, in other specific embodiments, the initial model for the cell survival fraction may also employ other model functions used to characterize the relationship between the mixed absorbed dose and the cell survival fraction.

[0043] When the constructed cell survival fraction model does not consider the mutual influence of different reactant particles on each other's bioefficiency during the boron neutron reaction, the cell survival fraction or mixed absorbed dose calculated based on the cell survival fraction model often differs from the true value, resulting in insufficient accuracy of the fitted cell survival fraction model. Therefore, to further improve the accuracy of the cell survival fraction model, in some embodiments, the initial cell survival fraction model is a cell survival fraction model optimized based on cooperating parameters. That is, cooperating parameters between each reactant particle are introduced into the preset basic cell survival fraction model to obtain the initial cell survival fraction model to be fitted.

[0044] The cell survival fraction basic model is an existing cell survival fraction model, and the model parameters of this model include at least the quadratic term coefficient, which is used to characterize the effect of repairable cell damage on the absorbed dose of various reactive particles.

[0045] The synergistic parameter is used to characterize the synergistic effect of various types of reactant particles on the absorbed dose of each other during the boron neutron reaction; that is, the effect of the bioefficiency of a certain type of reactant particle (such as boron particles) in the reaction process on the bioefficiency of another type of reactant particle (such as hydrogen particles) in the same reaction process; in this embodiment, the synergistic parameter is a parameter constructed based on the quadratic term coefficient.

[0046] In one specific embodiment, the basic model for cell survival fraction adopts the classical cell survival fraction model, which is:

[0047]

[0048] The collaborative parameter is the geometric mean constructed based on the coefficients of the quadratic term, and is:

[0049]

[0050] in, This represents the coefficient of the quadratic term corresponding to reactant particle i (the i-th type of reactant particle); This represents the coefficient of the quadratic term corresponding to reactant particle j (the j-th type of reactant particle).

[0051] In the classic cell survival score model, the aforementioned cooperating parameter is introduced, and the logarithm of the cell survival score model after the introduction is taken to obtain a new cell survival score model:

[0052]

[0053] In the formula, This represents the coefficient of the first-order term corresponding to reactant particle i; This represents the coefficient of the quadratic term corresponding to reactant particle i; This represents the coefficient of the quadratic term corresponding to reactant particle j.

[0054] Based on the new cell survival fraction model, the coefficients of the model to be fitted include four linear coefficients. 1, 2, 3, 4) and the coefficients of the four quadratic terms ( , , , There are a total of 8 model coefficients, so the number of fitted sample pairs corresponding to each target depth is not less than 8.

[0055] It should be noted that, in other specific embodiments, the synergy coefficient can also be other mathematical expressions used to characterize the synergistic effect between two types of reactive particles on biological effects, such as: Pearson correlation coefficient, Spearman correlation coefficient, etc.

[0056] To further improve the fitting effect of the cell survival fraction model, in some optional embodiments, the method of collecting several sets of fitting sample pairs at the current target depth is as follows: Figure 2 As shown, it includes:

[0057] S101, Based on the number of fitted sample pairs, select the corresponding number of sample absorption doses:

[0058] The absorbed dose of each sample is uniformly distributed within the specified range.

[0059] S102, Collect the cell survival fraction corresponding to the absorbed dose of each sample at the current target depth;

[0060] S103, Based on the sample absorbed dose and the corresponding cell survival fraction, construct fitting sample pairs corresponding to different depths.

[0061] In one specific embodiment, when step S101 is executed, as follows: Figure 3 As shown, it includes the following sub-steps:

[0062] S101A, to obtain the range of mixed absorbed dose at the current target depth during the boron neutron reaction process;

[0063] S101B, Based on the number of fitted sample pairs, the interval range is divided into a corresponding number of sub-intervals;

[0064] S101C, in each of the sub-intervals, extract the average value of the mixed absorbed dose within that sub-interval, and use it as the sample absorbed dose corresponding to that sub-interval.

[0065] By extracting the sample absorbed dose from different sub-intervals, the distribution of the obtained sample absorbed dose within the interval range is made more uniform, thereby making the data distribution of the fitted sample pairs constructed based on the sample absorbed dose more uniform. This avoids poor fitting effect caused by uneven data distribution, thereby improving the fitting accuracy of the cell survival fraction model.

[0066] In some optional embodiments, the acquisition of several sets of fitted sample pairs at the current target depth is obtained based on a cell survival experimental device.

[0067] In actual BNCT treatment, the location of the tumor in the body is often different, that is, the depth of the radiation source is often different. For different radiation source depths, the paths that various types of reaction particles in the boron neutron reaction pass through are different, so the cell survival fraction corresponding to the same absorbed dose is often different, that is, the cell survival fraction model corresponding to different depths is different.

[0068] Therefore, in order to obtain cell survival score models corresponding to different target depths, this application also provides another method for obtaining cell survival score models based on boron neutron reactions, which is used to obtain cell survival score models corresponding to different target depths.

[0069] Please see Figure 4 The diagram shows a flowchart of an embodiment of the method for obtaining the cell survival fraction model based on the boron neutron reaction provided in this application.

[0070] like Figure 4 As shown, the method includes:

[0071] S10, determine the neutron beam corresponding to each target depth;

[0072] Specifically, based on the current target depth value, the energy of the neutron stream corresponding to the current target depth is determined by utilizing the mapping relationship between the target depth and the neutron stream energy; based on this energy, the corresponding neutron stream beam is output.

[0073] S20, for different target depths, the process of obtaining the cell survival score model corresponding to the target depth is executed respectively to obtain the cell survival score model corresponding to each target depth.

[0074] Among them, for different target depths, the following is adopted: Figure 1 The method shown performs the process of obtaining the cell survival score model corresponding to each target depth to obtain the cell survival score model corresponding to each target depth.

[0075] For example, when the target depth includes a first target depth H1, a second target depth H2 and a third target depth H3, n sets of first fitted sample pairs under the first target depth H1, n sets of second fitted sample pairs under the second target depth H2 and n sets of third fitted sample pairs under the third target depth H3 are collected respectively.

[0076] Using n sets of first fitted sample pairs at the first target depth H1, the model parameters in the initial model of cell survival score to be fitted are fitted to obtain the cell survival score model corresponding to the first target depth H1.

[0077] Using n sets of second fitted sample pairs at the second target depth H2, the model parameters in the initial model of cell survival score to be fitted are fitted to obtain the cell survival score model corresponding to the second target depth H2.

[0078] Using n sets of third-fit sample pairs at the third target depth H3, the model parameters in the initial model of cell survival score to be fitted are fitted to obtain the cell survival score model corresponding to the third target depth H3.

[0079] In some optional embodiments, for a single target depth, the method for constructing several sets of fitted sample pairs at the current target depth is as follows: Figure 5 As shown, it includes:

[0080] S21, Based on the cell survival experimental device, using a neutron beam corresponding to the current target depth, a cell survival experiment is performed on boron-containing solutions at different irradiation distances to obtain the cell survival fraction of the boron-containing solutions at each irradiation distance;

[0081] The irradiation distance is the distance between the boron-containing solution and the incident port of the neutron beam along the incident direction.

[0082] The irradiation distance corresponds to the sample absorbed dose; that is, different irradiation distances correspond to different sample absorbed doses.

[0083] Specifically, after determining the absorbed dose of each sample, a neutron beam corresponding to the current target depth is used to perform cell survival experiments on boron-containing solutions at different irradiation distances in the cell survival experimental device; the experimental results corresponding to each boron-containing solution are measured to obtain the cell survival fraction corresponding to different sample absorbed doses.

[0084] S22, based on the sample absorbed dose and cell survival fraction corresponding to the same irradiation distance, a fitted sample pair is constructed;

[0085] Perform the above steps S21 to S22 on each of the target depths to obtain the fitted sample pairs corresponding to each target depth.

[0086] In one specific embodiment, step S21 is performed using a cell survival experimental apparatus; please refer to [link to relevant documentation]. Figure 6 The diagram shows a schematic representation of the cell survival experimental apparatus in one embodiment; as shown below. Figure 6 As shown, the cell survival experimental device includes: an incident port 100, a water phantom 200, several cell suspension tubes 300, and a rotating screw 400.

[0087] The incident port 100 is located on one end face of the cell survival experimental device, and is used to allow the neutron beam to enter the cell survival experimental device through the incident port 100.

[0088] The water mold 200 is used to hold liquids, such as water.

[0089] The cell suspension tubes 300 are arranged at certain intervals in the water model and are connected to each other by screws 400, so that when the screws are rotated, each cell suspension tube also rotates.

[0090] Using the aforementioned cell survival experimental apparatus, cell survival experiments were performed on boron-containing solutions at different irradiation distances within the apparatus, including:

[0091] Cells were cultured in a medium containing the second-generation boron drug BPA (a culture medium obtained by dissolving the second-generation boron drug BPA in Tris-HCl pH=8.0 buffer and adding the buffer to ordinary culture medium) and placed in a CO2 incubator. The cells were used for experiments when they reached the logarithmic growth phase. V79 cells in the logarithmic growth phase were transferred to cell suspension tubes. Starting from the front surface of the water phantom, cell suspension tubes were placed at different positions relative to the front surface based on a preset distance interval to obtain a sufficient number of samples for solving the cell survival model. For example, the distance interval was 0.5 cm.

[0092] Cell suspension tubes containing cells were fixed to a rotating screw, with a rotation speed of 3 revolutions per minute to maintain cell suspension. Irradiation was initiated, and to avoid the influence of sublethal cell damage repair on the experimental results, the irradiation time was set to be the same for each irradiation. After irradiation, the cells in each cell suspension tube were counted. The cells were then diluted and seeded into culture dishes and cultured in a CO2 incubator. Cells were fixed with formaldehyde and stained, and cell viability was detected using a cell imaging system to obtain the cell viability fraction corresponding to each cell suspension tube.

[0093] In practical applications, since the power, materials and thickness of the neutron source accelerator that forms the neutron beam are relatively fixed, the energy of the neutron beam formed by the neutron source accelerator is often relatively fixed and not easy to adjust according to the needs. Therefore, the target depth corresponding to the existing cell survival experimental device is often relatively simple. As a result, it is not possible to conveniently and efficiently obtain cell survival score models corresponding to different target depths based on the existing cell survival experimental device.

[0094] To address this technical problem, in some specific embodiments, a cell survival experimental device with neutron source energy regulation is used to perform step S21; such as... Figure 7 As shown, this cell survival experimental device and Figure 6 The devices shown are basically the same, except that the cell survival experiment device is equipped with a neutron source modulator 500 at the neutron incident port to adjust the energy of the neutron beam in order to obtain a neutron beam that is adapted to the target depth.

[0095] Furthermore, to improve the adjustment precision and accuracy of the neutron beam energy, the neutron source modulator includes a dose distribution shifter (DDS); the dose distribution shifter is a structural component formed by stacking several layers of different types of moderator materials; by adjusting the type and thickness of the moderator material in each layer, the dose distribution shifter can be adapted to the corresponding target depth.

[0096] In one specific embodiment, the dose distribution modulator is adapted to the corresponding target depth by adjusting the type and thickness of the moderating material in each layer, such as... Figure 8 As shown, it includes:

[0097] Step S801: Randomly generate multiple initial dose distribution regulator data. The initial dose distribution regulator data includes the material type of each layer and the thickness of each layer. All initial dose distribution regulator data constitute an iterative data set.

[0098] Specifically, all materials suitable for constructing dose distribution modulators are selected as candidate materials. All materials suitable for neutron moderation and absorption can be considered as candidates. Since hydrogen-containing materials combined with thermal neutron absorbers can achieve thermal neutron distribution modulation, hydrogen-containing materials are preferred as candidate materials for dose distribution modulators. For example, candidate materials for dose distribution modulators may include moderators such as polyethylene, water, acrylic acid, boron-containing polyethylene, and boron carbide. All available candidate materials for dose distribution modulators under the current conditions are obtained and numbered so that each candidate material has a distinct number.

[0099] Based on the numbering of the current candidate materials, multiple number groups are randomly generated, each containing multiple numbers. Then, based on the candidate materials corresponding to the numbers in each number group, material distribution data is generated for each number group. For example, assuming polyethylene is numbered 1, water is numbered 2, acrylic acid is numbered 3, boron-containing polyethylene is numbered 4, and boron carbide is numbered 5, then the material distribution data corresponding to number group 1-3-5 is polyethylene, acrylic acid, and boron carbide. Based on experimental data, designing the number of material layers in the dose distribution modulator data to 3 layers makes it easier to achieve the maximum dose induced by thermal neutrons deposited at the tumor center compared to setting it to other numbers. Therefore, the preferred number of material layers in the dose distribution modulator data is 3 layers, resulting in 3 numbers in each number group. Then, the thickness of each material in each material distribution data is randomly generated within a preset range, thereby obtaining the initial dose distribution modulator data corresponding to each material distribution data. Furthermore, the preset range for the random thickness of each candidate material in each material distribution data can be set to 0-3 cm. Each initial dose distribution modulator data point obtained in this way has a fixed material type and a fixed thickness for each material type. After obtaining multiple initial dose distribution modulator data points, it is necessary to construct an iterative data set from all the multiple initial dose distribution modulator data points.

[0100] Step S802: Iteratively optimize the iterative data set using a genetic algorithm to obtain an optimized iterative data set, and use the dose distribution regulator data with the smallest fitness value in the optimized iterative data set as the design scheme for the dose distribution displacement device.

[0101] like Figure 9 As shown, step S802 specifically includes sub-steps S8021 and S8022.

[0102] Step S8021: Obtain the current iteration data set and obtain the new iteration data set.

[0103] Specifically, the current iteration data set is first used as the existing population in the genetic algorithm. The fitness value of each dose distribution regulator data point in the current iteration data set is calculated. Then, based on the fitness values ​​in ascending order, a portion of the dose distribution regulator data is selected from the current iteration data set. These selected dose distribution regulator data points are then subjected to crossover mutation to obtain multiple crossover mutated dose distribution regulator data points. Next, the fitness values ​​of all crossover mutated dose distribution regulator data points are obtained. Based on all dose distribution regulator data points in the current iteration data set and all crossover mutated dose distribution regulator data points, a portion of the dose distribution regulator data points is selected again based on the fitness values ​​in ascending order to form a new population. This new population is used as the data set for the next round of iterations.

[0104] The process of cross-mutating the selected dose distribution modulator data specifically includes: cross-mutating and modifying the material type of the selected dose distribution modulator data to obtain dose distribution modulator data with cross-mutated material type; then, cross-mutating and modifying the material thickness of the dose distribution modulator data with cross-mutated material type to obtain multiple dose distribution modulator data with cross-mutation. It should be noted that it is also possible to first cross-mutate the material thickness of the dose distribution modulator data, and then cross-mutate the material type of the dose distribution modulator data.

[0105] The fitness value of the dose distribution regulator data is obtained as follows: a simulated dose distribution regulator is obtained by modeling in Monte Carlo software (MCNP) based on the dose distribution regulator data. Then, the Monte Carlo software is run to output the thermal neutron flux distribution data when the simulated dose distribution regulator is set at the beam exit port of the beam shaper in the BNCT device. That is, the thermal neutron flux distribution data corresponding to the dose distribution regulator data is obtained. Then, the programming software MATLAB reads the thermal neutron flux distribution data file output by the Monte Carlo software, extracts the calculation results and outputs the fitness value of the dose distribution regulator data.

[0106] It should be noted that the Monte Carlo software described above uses the target depth of the tumor center as the optimization objective; and the fitness value of the dose distribution modulator data needs to be set to be linearly related to the distance between the maximum depth of thermal neutron flux in the recipient and the target depth of the tumor center.

[0107] The specific fitness value is obtained based on the fitness function calculation. The fitness function expression in the programming software MATLAB is designed as follows:

[0108]

[0109] Wherein, OBJ represents the fitness value, and the lower the fitness value, the closer the individual is to the set optimization goal; A and B both represent weighting factors. When performing optimization for different goals, the weighting factors can be adjusted according to the importance of different optimization goals, and the weighting factors can be set to negative numbers. This represents the distance between the depth of the maximum thermal neutron flux within the recipient cell and the target depth at the tumor center. This represents the maximum thermal neutron flux within the recipient cell, which is obtained based on the exit neutron energy spectrum of the beam shaper in the current BNCT device.

[0110] The fitness values ​​of each dose distribution regulator data in the current iteration data set, as well as the fitness values ​​of all mutated dose distribution regulator data, are obtained in the manner described above.

[0111] Step S8022: Determine whether the new round of iteration data group meets the iteration termination condition. If so, select the dose distribution regulator data with the smallest fitness value in the new round of iteration data group as the dose distribution displacement device design scheme. Otherwise, take the new round of iteration data group as the current iteration data group and obtain a new round of iteration data group based on the current iteration data group.

[0112] Specifically, it is determined whether the latest acquired iteration data set meets the iteration termination condition. Further, it is determined whether the iteration number to which the acquired new iteration data set belongs (i.e., the number of iterations that have been performed so far) is greater than the preset iteration number. If so, it means that the current iteration number has reached a sufficient number. At this time, the dose distribution regulator data with the smallest fitness value in the new iteration data set can be selected as the design scheme of the dose distribution displacement device. Otherwise, it means that the current iteration number has not met the requirements. At this time, the latest acquired iteration data set should be used as the current iteration data set, and step S8021 should be executed again to obtain a new iteration data set (i.e., obtain a new iteration data set based on the current iteration data set). It should be noted that the above-mentioned iteration termination condition can also be set to the existence of dose distribution regulator data with a fitness value greater than the preset fitness threshold in the new round of iteration data group. That is, it is determined whether there is dose distribution regulator data with a fitness value greater than the preset fitness threshold in the latest acquired new round of iteration data group. If so, it means that a dose distribution displacement device design scheme that meets the requirements has been found. At this time, the dose distribution regulator data with the smallest fitness value in the new round of iteration data group can be selected as the design scheme of the dose distribution displacement device. Otherwise, it means that there is no dose distribution displacement device design scheme that meets the requirements in the new round of iteration data group. At this time, the latest acquired new round of iteration data group is taken as the current iteration data group, and step S802 is re-executed to obtain a new new round of iteration data group (i.e., obtaining a new round of iteration data group based on the current iteration data group).

[0113] Furthermore, the two iteration termination conditions mentioned above can also exist simultaneously, meaning that the iteration can stop as long as either of the two termination conditions is met during the judgment.

[0114] The method for obtaining the cell survival score model provided in this embodiment can use the same cell survival experimental device to collect sample absorption doses and corresponding cell survival scores at different target depths. Based on the collected sample absorption doses and corresponding cell survival scores, it can quickly obtain each set of fitted sample pairs, and then calculate the cell survival score model based on the fitted samples. Compared with the cumbersome operation of calculating the models corresponding to different target depths separately in the prior art, this application can quickly obtain cell survival score models applicable to different target depths. It not only improves the efficiency of obtaining models at different target depths, but also effectively improves the model accuracy of the cell survival score model by using multiple sets of fitted sample pairs to calculate the model.

[0115] Based on the same technical concept, this application also provides a method for obtaining the total relative biological effect value based on boron neutron reaction, which is used to obtain the total relative biological effect value corresponding to the mixed absorbed dose at different target depths, that is, the total RBE value of the mixed absorbed dose.

[0116] The total RBE value of the mixed absorbed dose is a comprehensive value composed of the RBE values ​​corresponding to the boron dose, the hydrogen dose, and the nitrogen dose, and is used to characterize the total relative biological effect of the mixed particles during the boron neutron reaction.

[0117] For example, when the cell survival fraction to be measured is 1%, the total RBE value of the mixed absorbed dose corresponding to a 1% cell survival fraction can be obtained based on the cell survival fraction model acquisition method provided in this application, with the 1% cell survival fraction as the biological endpoint.

[0118] Please see Figure 10 The diagram shows a flowchart of an embodiment of the method for obtaining the total relative biological effect based on the boron neutron reaction provided in this application.

[0119] like Figure 10 As shown, the method includes the following steps:

[0120] S1, obtain the cell survival score model corresponding to each target depth, and obtain the reference model corresponding to each target depth;

[0121] The reference model is a cell survival fraction model that characterizes the gamma ray, that is, it is used to characterize the distribution of the relationship between the cell absorbed dose and the cell survival fraction corresponding to the gamma ray.

[0122] Specifically, after determining the number of target depths, the cell survival score model corresponding to each target depth is obtained using any of the cell survival score models described above; this process is the same as the implementation process in the above embodiments, and will not be repeated here.

[0123] S2, For each target depth, based on the cell survival fraction to be measured, obtain the mixed absorbed dose and reference absorbed dose corresponding to the cell survival to be measured, and obtain the total RBE value of the mixed absorbed dose at the current target depth based on the mixed absorbed dose and reference absorbed dose;

[0124] Wherein, the mixed absorbed dose value is the absorbed dose value corresponding to the cell survival fraction to be measured in the cell survival fraction model corresponding to the target depth; the reference absorbed dose value is the absorbed dose value corresponding to the cell survival fraction to be measured in the reference model corresponding to the target depth.

[0125] Specifically, for the first target depth H1, the cell survival fraction M to be measured is input into the cell survival fraction model corresponding to the current target depth to obtain the corresponding mixed absorbed dose value D1; and the cell survival fraction M to be measured is input into the reference model corresponding to the current target depth to obtain the corresponding reference absorbed dose value D0.

[0126] Based on the mixed absorbed dose value D1 and the reference absorbed dose value D0, the total RBE value at the first target depth H1 is obtained as follows:

[0127] RBE 总1 = D0 / D1

[0128] Similarly, for the second target depth H1, the total RBE value at the second target depth H1 is obtained as follows:

[0129] RBE 总2 = D0 / D2

[0130] And so on, which will not be elaborated further here.

[0131] It should be noted that, unless otherwise specified, the above embodiments and features can be combined with each other; for example, based on Figure 7 The cell survival experimental apparatus shown performs... Figure 1 The method for obtaining the cell survival score model shown is to conveniently and quickly obtain the cell survival score model corresponding to the target depth.

[0132] Based on the same technical concept, the method for obtaining the cell survival fraction model based on boron neutron reaction or the method for obtaining the relative total value of biological effects based on boron neutron reaction provided in the above embodiments of the present invention can be implemented on the terminal side or the server side.

[0133] Please see Figure 11This is a schematic diagram of an optional hardware structure of an electronic terminal 700 provided in an embodiment of the present invention. The electronic terminal 700 can be a live streaming device, camera, mobile phone, computer equipment, tablet device, personal digital processing device, factory back-end processing equipment, etc., integrating photo / video recording functions. The electronic terminal 700 includes: at least one processor 701, a memory 702, at least one network interface 704, and a user interface 706. The various components in the device are coupled together through a bus system 705. It is understood that the bus system 705 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 705 also includes a power bus, a control bus, and a status signal bus.

[0134] The user interface 706 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0135] It is understood that memory 702 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0136] In this embodiment of the invention, the memory 702 is used to store various types of data to support the operation of the electronic terminal 700. Examples of this data include: any executable program for operation on the electronic terminal 700, such as the operating system 7021 and application program 7022; the operating system 7021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 7022 may contain various applications, such as media players, browsers, etc., for implementing various application services. The method for obtaining the cell survival fraction model based on the boron neutron reaction or the method for obtaining the relative total biological effect value based on the boron neutron reaction described in this embodiment of the invention may be included in the application program 7022.

[0137] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by processor 701. Processor 701 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 701 or by instructions in software form. The processor 701 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 701 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 701 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0138] In an exemplary embodiment, the electronic terminal 700 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0139] This invention also provides a computer-readable storage medium storing a computer program that, when invoked by a processor, implements the method for obtaining the cell survival fraction model based on the boron neutron reaction or the method for obtaining the relative total biological effect value based on the boron neutron reaction provided by this invention.

[0140] A computer-readable storage medium can be a tangible device capable of holding and storing instructions used by an instruction execution device. Computer-readable storage media can be, for example, (but not limited to) electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, and mechanical encoding devices.

[0141] The computer-readable program described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network, to an external computer or external storage device. A network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and forwards these instructions to the computer-readable storage medium in the respective computing / processing device.

[0142] It should be noted that, in the various embodiments of this application, the sequence number of each step does not indicate the order of execution. The execution order of each step should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0143] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for obtaining cell survival fraction models based on different depths, characterized in that, Used to obtain cell survival score models corresponding to different target depths; the method for obtaining the cell survival score model includes: Based on each preset target depth, a corresponding cell survival score model acquisition process is executed to obtain cell survival score models corresponding to different target depths; wherein, for a single target depth, the execution of the corresponding cell survival score model acquisition process includes: Collect several sets of fitted sample pairs at the current target depth; each fitted sample pair includes: sample absorbed dose, and cell survival fraction at the current target depth corresponding to the sample absorbed dose; using each fitted sample pair, fit the model parameters in the initial model of the cell survival fraction to be fitted, so as to obtain the cell survival fraction model corresponding to the current target depth; wherein, the number of fitted sample pairs is adapted to the number of model parameters to be fitted in the cell survival fraction model; The step of fitting the model parameters in the initial cell survival score model to obtain the cell survival score model corresponding to the current target depth using each of the fitted sample pairs includes: inputting each set of fitted sample pairs at the current target depth into the initial cell survival score model; solving the model parameters to be fitted in the model to obtain the optimal solution for each model parameter at the current target depth; and substituting each optimal solution into the initial cell survival score model to obtain the cell survival score model at the current target depth. The initial cell survival fraction model is obtained by introducing synergistic parameters between various reactants into a pre-defined basic cell survival fraction model. The model parameters of the basic cell survival fraction model include at least quadratic coefficients, which are used to characterize the effect of repairable cell damage on the absorbed dose of various reactants. The synergistic parameters are used to characterize the synergistic effect of various reactants on the absorbed dose of each other during the boron neutron reaction.

2. The method for obtaining cell survival fraction models based on different depths according to claim 1, characterized in that, The method for acquiring several sets of fitted sample pairs at the current target depth includes: based on a cell survival experimental device, using a neutron beam corresponding to the current target depth, performing a cell survival experiment on boron-containing solutions at different irradiation distances to obtain the cell survival fraction in the boron-containing solutions at each irradiation distance; wherein, the irradiation distance corresponds to the sample absorbed dose; and constructing a fitted sample pair by the sample absorbed dose and the cell survival fraction corresponding to the same irradiation distance.

3. The method for obtaining cell survival fraction models based on different depths according to claim 2, characterized in that, The cell survival experiment apparatus includes a neutron source modulator, which is disposed at the neutron incident port of the cell survival experiment apparatus and is used to adjust the energy of the neutron beam to obtain a neutron beam adapted to the target depth.

4. The method for obtaining cell survival fraction models based on different depths according to claim 3, characterized in that, The neutron source modulator includes a dose distribution modulator; the dose distribution modulator is a structural component formed by stacking several layers of different types of moderator materials.

5. The method for obtaining cell survival fraction models based on different depths according to claim 4, characterized in that, By adjusting the type and thickness of the moderating material in each layer of the dose distribution modulator, the dose distribution modulator can be adapted to the corresponding target depth.

6. The method for obtaining cell survival fraction models based on different depths according to claim 5, characterized in that, Adjusting the type and thickness of the moderating material in each layer of the dose distribution modulator to adapt the dose distribution modulator to the corresponding target depth includes: Multiple initial dose distribution modulator data are randomly generated. The initial dose distribution modulator data includes the material type and thickness of each layer. All initial dose distribution modulator data constitute an iterative data set. The iterative optimization of the data set is performed using a genetic algorithm to obtain an optimized iterative data set. The dose distribution regulator data with the smallest fitness value in the optimized iterative data set is selected as the design scheme for the dose distribution displacement device. The fitness value is calculated based on a fitness function, the expression of which is: ； Wherein, OBJ represents the fitness value, and the lower the fitness value, the closer the individual is to the set optimization goal; A and B both represent weight factors, and the weight factors can be adjusted according to the importance of different optimization goals when performing optimization for different goals, and the weight factors can be set to negative numbers; This represents the distance between the depth of the maximum thermal neutron flux within the recipient body and the target depth at the tumor center. This represents the maximum thermal neutron flux within the recipient cell, which is obtained based on the exit neutron energy spectrum of the beam shaper in the current BNCT device.

7. The method for obtaining cell survival fraction models based on different depths according to claim 6, characterized in that, The method for generating the initial dose distribution modulator data includes: All materials that can be used to construct dose distribution modulators are selected as candidate materials for dose distribution modulators, and all the candidate materials are numbered. Multiple number groups are randomly generated, and each number group contains multiple numbers. Based on the candidate materials corresponding to the numbers in each number group, material distribution data corresponding to each number group is generated. Within a preset range, the thickness of each material in each material distribution data is randomly generated to obtain the initial dose distribution modulator data corresponding to each material distribution data.

8. The method for obtaining cell survival fraction models based on different depths according to claim 6, characterized in that, The step of iteratively optimizing the iterative data set using a genetic algorithm to obtain an optimized iterative data set includes: Obtain a new set of iteration data based on the current set of iteration data; Determine whether the new iteration data set meets the iteration termination condition. If so, select the dose distribution regulator data with the smallest fitness value in the new iteration data set as the dose distribution displacement device design scheme; otherwise, use the new iteration data set as the current iteration data set and re-execute the iterative optimization process. Determine whether the latest acquired iteration data set meets the iteration termination condition, that is, determine whether the iteration number to which the acquired new iteration data set belongs is greater than the preset iteration number; or, determine whether there is dose distribution regulator data in the latest acquired iteration data set whose fitness value is greater than the preset fitness threshold.

9. The method for obtaining cell survival fraction models based on different depths according to claim 8, characterized in that, The process of obtaining a new round of iterative data based on the current iterative data group includes: Based on the fitness values ​​in ascending order, a portion of dose distribution regulator data is selected from the current iteration data set. Then, the selected dose distribution regulator data is subjected to cross-mutation to obtain multiple cross-mutated dose distribution regulator data. Next, obtain the fitness values ​​of all dose distribution regulator data after crossover mutation, and select a portion of the dose distribution regulator data to form a new population based on the fitness values ​​in ascending order. Use the new population as the data group for a new round of iteration. The process of cross-mutating the selected dose distribution modulator data includes: cross-mutating and mutating the material type of the selected dose distribution modulator data, and then cross-mutating and mutating the material thickness of the dose distribution modulator data after cross-mutation of the material type; or, cross-mutating the material thickness of the dose distribution modulator data and then cross-mutating the material type of the dose distribution modulator data.

10. A terminal, characterized in that, include: A processor and a memory, wherein the memory and the processor are communicatively connected; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to enable the terminal to perform the method for obtaining a cell survival score model based on different depths as described in any one of claims 1 to 9.

11. A computer storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for obtaining cell survival score models based on different depths as described in any one of claims 1 to 9.

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