Method, apparatus and medium for determining relative biological effectiveness of a particle beam

Abstract

The application provides a particle beam relative biological effect determination method, device, equipment and medium, which can be applied to the fields of physics and computers. The method comprises the following steps: constructing a microscopic Monte Carlo model based on a particle type, an initial energy and tissue characteristic parameters; simulating the interaction process of particles and DNA chains by using the microscopic Monte Carlo simulation model to obtain a double-strand break yield generated under a unit dose; wherein the double-strand break is divided into multiple types according to complexity; performing weighted calculation on the double-strand break yields of different complexities based on preset weights to obtain a relative biological effect; wherein the weights corresponding to double-strand breaks of different complexities are different, and the relative biological effect is used to quantify the comprehensive damage degree of particle radiation on biological tissues under a unit dose; and determining an equivalent biological dose according to the relative biological effect, and the equivalent biological dose is used to guide dose optimization of a particle beam treatment plan.

Description

Technical Field

[0001] This application relates to the fields of physics and computer science, specifically to the application of physics and computer technology in the field of radiotherapy, and more specifically to a method, apparatus, device, and medium for determining the relative biological effects of a particle beam. Background Technology

[0002] Particle radiotherapy has important applications in precision radiotherapy for tumors. Clinically, relative biological effectiveness (RBE) is usually used to characterize the difference in effect between different types of radiation and reference radiation when producing the same biological endpoint. It can be used to assess the biological effects of radiation and guide radiotherapy planning.

[0003] Existing methods for determining relative biological effects rely on a single modeling basis, neglect the differences in complexity of double-strand breaks, and suffer from low accuracy in determining relative biological effects. Summary of the Invention

[0004] This application provides a method, apparatus, device, and medium for determining the relative biological effects of particle beams.

[0005] According to the first aspect of this application, a method for determining the relative biological effects of a particle beam is provided, comprising: constructing a microscopic Monte Carlo model based on particle type, initial energy, and tissue characteristic parameters; simulating the interaction process between particles and DNA strands using the microscopic Monte Carlo simulation model to obtain the yield of double-strand breaks per unit dose; wherein, double-strand breaks are classified into multiple types according to complexity; calculating the yield of double-strand breaks of each complexity based on preset weights to obtain the relative biological effect; wherein, the weights corresponding to double-strand breaks of different complexities are different, and the relative biological effect is used to quantify the comprehensive damage degree of particle radiation to biological tissues per unit dose; determining the equivalent biological dose based on the relative biological effect, and the equivalent biological dose is used to guide the dose optimization of the particle beam treatment plan.

[0006] According to embodiments of this application, a microscopic Monte Carlo simulation model is used to simulate the interaction between particles and DNA strands to obtain the double-strand break yield per unit dose. This includes: simulating the movement of particles within a specified region based on the microscopic Monte Carlo simulation model, tracking the particle trajectory in the tissue and recording the energy deposition location; marking target sites that may trigger double-strand breaks based on energy deposition thresholds; statistically analyzing double-strand break events based on target sites and classifying each double-strand break event according to complexity; and determining the double-strand break yield per unit dose based on the total number of double-strand break events and the number of particles.

[0007] According to embodiments of this application, the method of counting double-strand break events based on target sites and classifying each double-strand break event according to its complexity includes: dividing the DNA strand into multiple DNA fragments, and assigning each site to a corresponding DNA fragment according to the coordinates of the target site; for each DNA fragment, counting the number of sites in the fragment, and determining the type of each double-strand break based on the number of sites.

[0008] According to embodiments of this application, the type of double-strand breakage includes at least one of simple double-strand breakage, low-complexity double-strand breakage, and high-complexity double-strand breakage.

[0009] According to embodiments of this application, the preset weights are obtained in at least one of the following ways: by measuring cell viability under different radiation conditions, the weights corresponding to different types of double-strand break events under each radiation condition are determined, and the weights are used to reflect the degree of influence of the double-strand break event on cell viability; during the radiation process, linear energy transfer regions are divided, and partition weight fitting is performed on different linear energy transfer regions to obtain the weights corresponding to different types of double-strand break events under each energy transfer interval.

[0010] According to embodiments of this application, the method further includes: matching the simulation conditions of the microscopic Monte Carlo model with the radiation conditions, and using the weights corresponding to the matched radiation conditions as preset weights; or, determining the linear energy transfer information under the current simulation conditions and using the weights corresponding to the energy transfer regions where the linear energy transfer information is located as preset weights.

[0011] According to an embodiment of this application, the relative biological effect is obtained by weighting the double-chain fracture yield of each complexity based on a preset weight, including: multiplying the double-chain fracture yield of different complexities by the corresponding preset weight and then summing the results to obtain the relative biological effect.

[0012] According to a second aspect of this application, a device for determining the relative biological effects of a particle beam is provided, comprising: a construction module for constructing a microscopic Monte Carlo model based on particle type, initial energy, and tissue characteristic parameters; a simulation module for simulating the interaction between particles and DNA strands using the microscopic Monte Carlo simulation model to obtain the yield of double-strand breaks per unit dose; wherein the double-strand breaks are classified into multiple types according to their complexity; a calculation module for performing weighted calculations on the yield of double-strand breaks of each complexity based on preset weights to obtain the relative biological effect; the relative biological effect is used to quantify the overall damage degree of particle radiation to biological tissues per unit dose; and a determination module for determining the equivalent biological dose based on the relative biological effect, wherein the equivalent biological dose is used to guide the dose optimization of the particle beam treatment plan.

[0013] According to a third aspect of this application, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method.

[0014] According to a fourth aspect of this application, a computer-readable storage medium is provided that stores a computer program or instructions thereon, characterized in that the computer program or instructions, when executed by a processor, implement the steps of the method described above. Attached Figure Description

[0015] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0016] Figure 1 This illustration schematically shows a system architecture diagram of a particle beam relative biological effect determination method according to an embodiment of this application;

[0017] Figure 2 A flowchart illustrating a method for determining the relative biological effects of a particle beam according to an embodiment of this application is shown schematically.

[0018] Figure 3 This schematically illustrates a flowchart of the process of simulating the interaction between particles and DNA strands using a microscopic Monte Carlo simulation model according to an embodiment of this application, to obtain the yield of double-strand breaks per unit dose;

[0019] Figure 4 This schematic diagram illustrates the structural block diagram of a particle beam relative biological effect determination device according to an embodiment of this application;

[0020] Figure 5 A block diagram of an electronic device for determining the relative biological effects of a particle beam according to an embodiment of this application is shown schematically. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of a feature, step, operation, and / or component, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0023] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0024] In the description of this application, it should be understood that the terms "longitudinal", "length", "circumferential", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the subsystem or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0025] Throughout the accompanying drawings, identical elements are represented by the same or similar reference numerals. Conventional structures or configurations have been omitted where they may cause confusion in understanding this application. Furthermore, the shapes, dimensions, and positional relationships of the components in the drawings do not reflect actual size, scale, or actual positional relationships. Additionally, any reference symbols placed within parentheses should not be construed as limiting.

[0026] Similarly, to simplify this application and aid in understanding one or more of the various disclosed aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. The use of terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicates that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0027] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0028] In the technical solution of this application, the collection, storage, use, processing, transmission, provision, disclosure, and application of user personal information all comply with relevant laws and regulations, necessary confidentiality measures have been taken, and they do not violate public order and good morals. In the technical solution of this application, user authorization or consent has been obtained before acquiring or collecting user personal information.

[0029] This application provides a method, apparatus, device, and medium for determining the relative biological effects of particle beams. Before introducing the technical solutions provided in this application, the relevant technologies involved in this application will be explained.

[0030] Different types of radiation, when interacting with biological tissues, will have different effects on the tissues due to their different physical properties (such as energy deposition mode, ionization density, etc.). By modeling with the RBE model, the relative biological effects can be determined, and the killing ability of different particles (such as carbon ions and photons) on cells or tissues at the same dose can be compared to understand the differences in the biological effects of different radiations and guide the formulation of radiotherapy plans.

[0031] However, existing RBE models are usually based on the total number of DNA double-strand breaks, assuming that different types of DSBs contribute the same to cell lethality. This fails to accurately reflect the true impact of different DBSs on biological effects, ignores the differences in DSB complexity, and is prone to inaccurate assessment of biological effects.

[0032] In the high linear energy transfer (LET) and low LET regions, existing models lack sufficient prediction accuracy and struggle to accurately describe the overkill effect. The overkill effect refers to the reduced cell-killing effect of radiation under high LET conditions. Existing models cannot adequately handle this complex radiation effect, leading to discrepancies between predicted and actual results. Furthermore, in actual carbon ion radiotherapy, the energy spectrum and beam pattern may change, and existing models cannot adapt to these variations, resulting in reduced reliability of predictions under different conditions.

[0033] Figure 1 The diagram illustrates a system architecture of a particle beam relative biological effect determination method according to an embodiment of this application.

[0034] like Figure 1 As shown, application scenario 100 according to this embodiment may include terminal devices 101, 102, and 103, network 104, and server 105. Network 104 is used as a medium to provide a communication link between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0035] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as physics simulation applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0036] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0037] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using terminal devices 101, 102, and 103 (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0038] It should be noted that the particle beam relative biological effect determination method provided in this application embodiment can generally be executed by server 105. Correspondingly, the particle beam relative biological effect determination device provided in this application embodiment can generally be located in server 105. The particle beam relative biological effect determination method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the particle beam relative biological effect determination device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0039] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0040] The following will be based on Figure 1 The described scene, through Figures 2-3The method for determining the relative biological effects of particle beams according to the disclosed embodiments is described in detail.

[0041] Figure 2 A flowchart illustrating a method for determining the relative biological effects of a particle beam according to an embodiment of this application is shown.

[0042] like Figure 2 As shown, the method for determining the relative biological effects of a particle beam in this embodiment includes operations S210 to S240.

[0043] In operating S210, a microscopic Monte Carlo model is constructed based on particle type, initial energy, and organizational characteristic parameters.

[0044] In some embodiments, the microscopic Monte Carlo model simulates particle trajectories based on the principles of the Monte Carlo method. This model is based on probability statistics and simulates the trajectories of particles in biological tissues and their interactions with atoms and molecules within the tissues through random sampling.

[0045] Particle type, initial energy, and tissue characteristic parameters have a crucial impact on the process and outcome of particle-biological tissue interactions. Different types of particles (such as protons, carbon ions, and photons) interact with matter through different physical mechanisms; the initial energy of a particle determines its range within the tissue and the spatial distribution of energy deposition; tissue characteristic parameters, such as density, atomic number, and electron density, significantly influence particle propagation and energy loss within the tissue. Based on particle type, initial energy, and tissue characteristic parameters, constructing a microscopic Monte Carlo model can accurately characterize particle behavior and its impact on tissues.

[0046] In operating S220, the process of particle-DNA strand interaction is simulated using a microscopic Monte Carlo simulation model to obtain the yield of double-strand breaks per unit dose; among them, double-strand breaks are classified into multiple types according to their complexity.

[0047] In some embodiments, the collected parameters are input into the constructed microscopic Monte Carlo model, and the simulation program is started. During the simulation, information such as the position and manner of interaction between each particle and the DNA strand is recorded based on the motion of each particle. When the interaction between the particle and the DNA strand leads to a double-strand break (DSB), the DSBs are classified according to their complexity, and the number of DSBs of each complexity generated per unit dose is counted.

[0048] In operation S230, the yield of double-strand fractures of different complexities is weighted and calculated based on preset weights to obtain the relative biological effect. The weights corresponding to double-strand fractures of different complexities are different, and the relative biological effect is used to quantify the overall damage of particle radiation to biological tissues per unit dose.

[0049] In some embodiments, based on extensive biological experimental research and theoretical analysis, the damage and relative contribution of double-strand breaks of different complexities to biological tissues can be determined, i.e., preset weights. Generally, complex double-strand breaks have a stronger lethal effect on cells due to their greater difficulty in repair and higher probability of erroneous repair, meaning that complex double-strand breaks have a relatively higher weight.

[0050] The number of double-strand breaks of various complexities obtained statistically is multiplied by their respective preset weights, and the results are summed to obtain the relative biological effect. The relative biological effect can quantify the overall degree of damage to biological tissues caused by particle radiation per unit dose.

[0051] In operation S240, the equivalent biological dose is determined based on the relative biological effect, and the equivalent biological dose is used to guide the dose optimization of the particle beam therapy plan.

[0052] In some embodiments, the equivalent biological dose can be calculated based on the relative biological effect and in conjunction with a relevant dose-response model. The equivalent biological dose converts the physical dose of particle radiation into a biological dose that reflects the actual degree of damage to biological tissues, making the doses of different types and energies of particle radiation and under different tissue conditions comparable.

[0053] In a treatment planning system, the calculated equivalent biological dose can be used as a reference indicator. Combined with factors such as target shape, location, and the tolerance dose of surrounding normal tissues, the particle beam treatment plan can be optimized. By adjusting parameters such as the energy, incident angle, and radiation dose distribution of the particle beam, the tumor area receives a sufficiently high equivalent biological dose while minimizing damage to surrounding normal tissues.

[0054] In simulating the interaction between particles and DNA strands, this application categorizes double-strand breaks into multiple types based on complexity, laying the foundation for considering the different contributions of different types of DSBs to biological effects. For DSBs of varying complexity, this application introduces adjustable weighting parameters that reflect the relative contribution of DSBs of different complexities to cell lethality. By rationally setting these weighting parameters, the differences in biological effects among different types of DSBs are quantitatively considered, more accurately reflecting the actual damage to biological tissues caused by particle radiation. This improves the prediction accuracy of relative biological effectiveness (RBE), guiding the development of particle beam therapy plans and enhancing the efficacy of particle beam therapy.

[0055] Figure 3 The illustration schematically shows a flowchart of the process of simulating the interaction between particles and DNA strands using a microscopic Monte Carlo simulation model according to an embodiment of this application, to obtain the yield of double-strand breaks per unit dose.

[0056] like Figure 3 As shown, this embodiment uses a microscopic Monte Carlo simulation model to simulate the process of particle-DNA strand interaction to obtain the yield of double-strand breaks per unit dose, including operations S310~S340.

[0057] In operation of S310, the movement of particles in a specified area is simulated based on a microscopic Monte Carlo simulation model, the trajectory of particles in the tissue is tracked and the location of energy deposition is recorded.

[0058] In some embodiments, a microscopic Monte Carlo simulation model is used to simulate the random motion of particles within a specified region, based on the particles' initial energy, type (e.g., protons, carbon ions, etc.), and predefined tissue characteristic parameters. During the simulation, the trajectory of each particle is tracked, and its energy deposition at various locations within the tissue is recorded. The specified region represents a part of the biological tissue, and its size, boundary conditions, and tissue characteristic parameters (e.g., density, atomic composition, etc.) can be determined by preset parameters. These parameters affect particle motion and energy deposition within the tissue.

[0059] When operating S320, target sites that may trigger double-strand breaks are marked based on the energy deposition threshold.

[0060] In some embodiments, an energy deposition threshold can be set. When the energy deposition of a particle at a certain location in the tissue exceeds this threshold, that location is considered to potentially induce a DNA double-strand break and is marked as a target site. The energy deposition threshold can be determined based on existing experimental data and theoretical research. This threshold is related to the structure and chemical bond energy of the DNA molecule. When the energy deposition of a particle at a certain location in the tissue exceeds this threshold, it may damage the DNA strands and induce a double-strand break.

[0061] By traversing the energy deposition locations of particles in the tissue, locations where energy deposition exceeds a threshold can be marked as target sites. These target sites represent regions where DNA double-strand breaks may occur.

[0062] In operation S330, double-strand breakage events are statistically analyzed based on the target site, and each double-strand breakage event is classified according to its complexity.

[0063] In some embodiments, statistical analysis is performed on the labeled target sites to count the total number of double-strand breaks occurring within a specific region (such as a cell nucleus or a segment of DNA). The double-strand break events are classified according to the distribution of the target sites and their distances (i.e., complexity). Double-strand break events can be classified, for example, into the following categories: simple double-strand breaks (DSB0), which contain only one double-strand break within a specific region; low-complexity double-strand breaks (DSB1), which contain one double-strand break accompanied by an additional single-strand break within a specific region; and high-complexity double-strand breaks (DSB2). 2X It contains a double-chain break within a specific region, accompanied by at least two additional chain breaks.

[0064] In operation S340, the double-strand break yield per unit dose is determined based on the total number of double-strand break events and the number of particles.

[0065] In some embodiments, the double-strand breakage yield per unit dose can be calculated based on the total number of double-strand breakage events and the corresponding number of particles, combined with the conversion relationship between particle number and dose. For example, if N particles are used in the simulation, M double-strand breakage events occur, and the dose corresponding to these N particles is D Gy, then the double-strand breakage yield per unit dose (1 Gy) is M / D.

[0066] The microscopic Monte Carlo simulation model used in this application fully considers the randomness of particle movement and the non-uniformity of energy deposition in tissues, more realistically simulating the complex process of particle-DNA strand interaction. By classifying double-strand break events according to complexity, a more detailed understanding of DNA damage caused by particle radiation is obtained, providing information on the number of different types of double-strand breaks per unit dose for optimizing treatment plans. This helps relevant personnel more accurately predict treatment effects and the risk of damage to normal tissues.

[0067] According to one embodiment of this application, the method of counting double-strand break events based on target sites and classifying each double-strand break event according to its complexity includes: dividing the DNA strand into multiple DNA fragments, and assigning each site to a corresponding DNA fragment according to the coordinates of the target site; for each DNA fragment, counting the number of sites in the fragment, and determining the type of each double-strand break according to the number of sites.

[0068] In some embodiments, the DNA chain can be divided into multiple DNA fragments according to a fixed length, biological characteristics, or research needs. This application embodiment divides the DNA chain based on a fixed length, into fragments with a length not exceeding a preset base pair threshold (e.g., 10 bp). Those skilled in the art can also divide the DNA chain according to actual needs, and this application does not impose any limitations on this.

[0069] A correspondence is established between the coordinates of each target site and the DNA fragment, accurately assigning each target site to its corresponding DNA fragment. For each DNA fragment, the number of target sites contained within that fragment is counted, and each double-strand break event is classified according to a pre-defined classification standard based on the number of sites in each DNA fragment. For example, within a DNA fragment whose length does not exceed a preset base pair threshold (e.g., 10 bp), a double-strand break containing only one instance is classified as a simple double-strand break (DSB0). If a DNA fragment meets this condition, the double-strand break event within that fragment is classified as DSB0. If a DNA fragment contains both a double-strand break and an additional single-strand break, the double-strand break event within that fragment is classified as a low-complexity double-strand break (DSB1).

[0070] This application's embodiments, by dividing the DNA strand into multiple fragments and counting the number of sites within each fragment, can more accurately locate the position and frequency of double-strand break events. Compared to a general analysis of the entire DNA strand, this subdivision method avoids statistical errors caused by excessively long DNA strands, making the analysis of double-strand break events more accurate and reliable. For example, using a shorter length such as 10 bp as a threshold to divide DNA fragments allows for a more detailed observation of the distribution of double-strand break events in local regions.

[0071] According to an embodiment of this application, the preset weights are obtained in at least one of the following ways: by measuring cell viability under different radiation conditions, the weights corresponding to different types of double-strand break events under each radiation condition are determined, and the weights are used to reflect the degree of influence of the double-strand break event on cell viability; during the radiation process, linear energy transfer regions are divided, and partition weight fitting is performed on different linear energy transfer regions to obtain the weights corresponding to different types of double-strand break events under each energy transfer interval.

[0072] In some embodiments, the relative biological effectiveness (RBE) data measured by cell survival experiments (such as 10% survival fraction) can be used to back-calculate the weights of various DSBs using the minimum error criterion.

[0073] For example, multiple radiation sources can be selected as reference radiation (e.g., X-rays, gamma rays), and proton beams, heavy ion beams, etc., as test radiation. Different radiation sources have different linear energy transfer (LET) characteristics, which will produce different biological effects on cells. A series of radiation doses can be set, covering a range from low to high doses, to comprehensively observe cell survival under different radiation doses.

[0074] Select suitable cells and irradiate the cell samples using reference radiation (such as X-rays or gamma rays) according to the set radiation dose. Then, irradiate the cell samples using test radiation (such as proton beams or heavy ion beams) following the same experimental procedure.

[0075] Relative biological effect (RBE) refers to the ratio of the test radiation dose to the reference radiation dose, which produces the same biological effect. In this experiment, the 10% survival fraction (i.e., when cell viability is 10%) was used as the measure of biological effect. Based on cell viability data, the doses required to achieve a 10% survival fraction for both the reference and test radiation were determined. and Then calculate We collected RBE data from various test radiations under different conditions to provide a basis for subsequent analysis.

[0076] Based on factors such as the complexity of double-strand breaks and the ease of repair, DSBs are categorized into different types. It is assumed that the effects of each type of DSB on cell survival are independent, and that the contribution of different types of DSBs to cell survival can be represented by weights.

[0077] Mathematical models can be established to describe the relationship between cell survival rate and the number of different types of DSBs.

[0078] Appropriate molecular biology techniques were used to determine the number of different types of double-strand breaks (DSBs) in cells under different radiation conditions. The measured RBE data, cell viability under different radiation conditions, and the number of different types of DSBs were substituted into an established model relating cell viability to DSBs. By adjusting the weights wᵢ of different types of DSBs, the error between the cell viability calculated by the model and the experimentally measured cell viability was minimized, thus obtaining the weights corresponding to each type of double-strand break event.

[0079] In some embodiments, weights can be determined by linear energy transfer (LET) region partitioning.

[0080] The radiation region can be divided into different intervals based on the LET value. LET refers to the energy deposited by radiation per unit length of its trajectory, reflecting the ionization density of the radiation. Within each LET interval, relevant experimental data or existing research results are collected. This data can include information such as the frequency of different types of double-strand break events and cell viability. From the collected data, the first data point belonging to the low LET interval and the second data point belonging to the high LET interval are selected. The first and second data points can include cell viability, RBE values, and the number of different types of DSBs under different radiation doses. Weight parameters for the low LET interval are fitted using the first data point, and weight parameters for the high LET interval are fitted using the second data point. Based on the fitting results, the weights corresponding to different types of double-strand break events in each LET interval are determined. These weights reflect the degree of influence of different types of double-strand break events on cells under different LET radiation conditions.

[0081] Taking the low LET interval and the first data as an example, the process of fitting the weight parameters can be as follows: Substitute the selected low LET interval data into the established model, adopt the minimum error criterion, and adjust the weights wᵢ of different types of DSBs through optimization algorithms (such as least squares method, gradient descent method, etc.) to minimize the error between the cell survival rate calculated by the model and the cell survival rate measured experimentally, thus obtaining the weights corresponding to different types of double-strand break events in the low LET interval. The fitting process of the weight parameters in the high LET interval is similar and will not be elaborated here.

[0082] This application's embodiments determine weights by measuring cell viability and / or dividing linear energy transfer regions, effectively improving the accuracy and flexibility of weight determination to suit different application scenarios. Determining weights based on cell viability directly reflects the actual impact of different DSB times on cell survival, thus improving the accuracy of predicting cell survival under different radiation conditions. Based on dividing linear energy transfer regions to determine weights, and considering the influence of the physical properties of radiation on double-strand break events, the weights accurately reflect the relative biological effects of double-strand break events under different radiation conditions, improving the accuracy of subsequent simulations.

[0083] This application utilizes biological effect data to fit or segmentally correct the weights. By combining this data with actual experimental data, the weight parameters can be optimized and adjusted based on experimental results under different LET intervals and different beam types, thereby improving the prediction accuracy and applicability of relative biological effects under different conditions.

[0084] In some embodiments, weights can be determined by using fixed weights or simplified weights. For example, some complex DSBs can be merged and assigned the same weight to reduce the model's degrees of freedom and improve stability.

[0085] According to one embodiment of this application, before calculating the total weighted double-chain fracture effect, the method further includes: matching the simulation conditions of the microscopic Monte Carlo model with the radiation conditions, and using the weights corresponding to the matched radiation conditions as preset weights; or, determining the linear energy transfer information under the current simulation conditions and using the weights corresponding to the energy transfer regions where the linear energy transfer information is located as preset weights.

[0086] In some embodiments, the weight values ​​corresponding to different types of DSBs under the simulation conditions can be obtained by matching the simulation conditions with the radiation conditions, and the weight values ​​corresponding to the matched radiation conditions can be used as preset weights for subsequent calculation and analysis.

[0087] Alternatively, during the simulation, the particle trajectory and energy deposition can be tracked. Based on the definition of LET, LET values ​​at different locations within the tissue can be calculated, and the LET range can be divided into different energy regions based on these values ​​to obtain LET information under the current simulation conditions. For example, regions with LET values ​​less than 10 keV / μm can be classified as low LET regions, and regions greater than 10 keV / μm as high LET regions. Based on the LET information under the current simulation conditions, the energy transfer region where the LET is located can be determined, and the weights corresponding to different Reading DSB times within that energy region can be used as preset weights for subsequent calculations and analyses.

[0088] This application's embodiments determine preset weights by matching the simulation conditions of the microscopic Monte Carlo model with the radiation conditions, enabling the simulation results to more realistically reflect the actual effects of radiation on biological tissues and improving the accuracy of the simulation results. Determining preset weights based on linear energy transfer information fully considers the influence of radiation quality on the weights of different types of DSB events, improving the adaptability of the microscopic Monte Carlo model to complex radiation fields, accurately predicting the comprehensive effects of different radiation components on biological tissues, and enhancing the accuracy of the simulation results.

[0089] According to one embodiment of this application, the relative biological effect is obtained by weighting the double-chain fracture yield of each complexity based on a preset weight, including: multiplying the double-chain fracture yield of different complexities by the corresponding preset weight and then summing the results to obtain the relative biological effect.

[0090] In some embodiments, relative biological effects The calculation formula is:

[0091]

[0092] in, It is the i-th type DSB yield induced by particle radiation; It is the i-th type DSB yield induced by reference radiation (such as X-rays); The preset weights corresponding to the i-th type of DSB; i includes DSB0, DSB1, and DSB. 2X At least one of them.

[0093] Figure 4 A schematic block diagram of a particle beam relative biological effect determination device according to an embodiment of this application is shown.

[0094] like Figure 4 As shown, the particle beam relative biological effect determination device 400 of this embodiment includes a construction module 410, a simulation module 420, a calculation module 430 and a determination module 440.

[0095] The construction module 410 is used to construct a microscopic Monte Carlo model based on particle type, initial energy, and organization characteristic parameters. In one embodiment, the construction module 410 can be used to perform the operation S210 described above, which will not be repeated here.

[0096] The simulation module 420 is used to simulate the interaction between particles and DNA strands using a microscopic Monte Carlo simulation model to obtain the yield of double-strand breaks per unit dose; wherein, double-strand breaks are classified into multiple types according to their complexity. In one embodiment, the simulation module 420 can be used to perform the operation S220 described above, which will not be repeated here.

[0097] The calculation module 430 is used to perform weighted calculations on the double-strand breakage yields of various complexities based on preset weights to obtain the relative biological effect; the relative biological effect is used to quantify the overall damage degree of particle radiation to biological tissues per unit dose. In one embodiment, the calculation module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0098] The determination module 440 is used to determine the equivalent biological dose based on the relative biological effect, which is used to guide the dose optimization of the particle beam therapy plan. In one embodiment, the determination module 440 may be used to perform the operation S240 described above, which will not be repeated here.

[0099] According to embodiments of this application, any plurality of modules among the construction module 410, simulation module 420, calculation module 430, and determination module 440 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this application, at least one of the construction module 410, simulation module 420, calculation module 430, and determination module 440 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the construction module 410, simulation module 420, calculation module 430, and determination module 440 can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0100] Figure 5 A block diagram of an electronic device for determining the relative biological effects of a particle beam according to an embodiment of this application is shown schematically.

[0101] like Figure 5 As shown, an electronic device 500 according to an embodiment of this application includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage portion 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0102] RAM 503 stores various programs and data required for the operation of electronic device 1100. Processor 501, ROM 502, and RAM 503 are interconnected via bus 504. Processor 501 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 502 and / or RAM 503. It should be noted that the program may also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in one or more memories.

[0103] According to embodiments of this application, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output (I / O) interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 1107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0104] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0105] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0106] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of this application, those skilled in the art can make various substitutions and modifications, and these substitutions and modifications should all be included within the protection scope of this application.

Claims

1. A method for determining the relative biological effects of a particle beam, characterized in that, include: A microscopic Monte Carlo model is constructed based on particle type, initial energy, and tissue characteristic parameters. The microscopic Monte Carlo simulation model was used to simulate the interaction between particles and DNA strands to obtain the yield of double-strand breaks per unit dose; among which, double-strand breaks are classified into multiple types according to their complexity. The relative biological effect is obtained by weighting the double-strand fracture yield of each complexity based on preset weights; wherein, the weights corresponding to double-strand fractures of different complexities are different, and the relative biological effect is used to quantify the comprehensive damage degree of particle radiation to biological tissues per unit dose. The equivalent biological dose is determined based on the relative biological effect, and the equivalent biological dose is used to guide the dose optimization of the particle beam therapy plan.

2. The method according to claim 1, characterized in that, The process of simulating particle-DNA strand interaction using the microscopic Monte Carlo simulation model to obtain the double-strand break yield per unit dose includes: Based on the aforementioned microscopic Monte Carlo simulation model, the movement of particles within a specified area is simulated, the trajectory of particles in the tissue is tracked, and the location of energy deposition is recorded. Target sites that may trigger double-strand breaks are marked based on energy deposition thresholds; The double-strand breakage events are statistically analyzed based on the target site, and each double-strand breakage event is classified according to its complexity. The yield of double-strand breaks per unit dose is determined based on the total number of double-strand break events and the number of particles.

3. The method according to claim 2, characterized in that, The method of statistically analyzing double-strand breakage events based on target sites and classifying each double-strand breakage event according to complexity includes: The DNA chain is divided into multiple DNA fragments, and each site is assigned to a corresponding DNA fragment according to the coordinates of the target site. For each DNA fragment, the number of sites in the fragment is counted, and the type of each double-strand break is determined based on the number of sites.

4. The method according to claim 3, characterized in that, The types of double-strand breaks include at least one of simple double-strand breaks, low-complexity double-strand breaks, and high-complexity double-strand breaks.

5. The method according to claim 1, characterized in that, The preset weights are obtained based on at least one of the following methods: By measuring cell viability under different radiation conditions, the weights of different types of double-strand break events under each radiation condition are determined. These weights are used to reflect the degree of influence of the double-strand break event on cell viability. During the radiation process, linear energy transfer regions are divided, and weighted fitting is performed on different linear energy transfer regions to obtain the weights corresponding to different types of double-strand breakage events in each energy transfer interval.

6. The method according to claim 5, characterized in that, Also includes: The simulation conditions of the microscopic Monte Carlo model are matched with the radiation conditions, and the weights corresponding to the matched radiation conditions are used as preset weights. or, Determine the linear energy transfer information under the current simulation conditions and use the weight corresponding to the energy transfer region where the linear energy transfer information is located as the preset weight.

7. The method according to claim 1, characterized in that, The relative biological effects are obtained by weighting the double-strand breakage yields of different complexities based on preset weights, including: The relative biological effect is obtained by multiplying the double-chain fracture yield of different complexities with their corresponding preset weights and then summing the results.

8. A device for determining the relative biological effects of a particle beam, characterized in that, include: The building blocks are used to construct microscopic Monte Carlo models based on particle type, initial energy, and organization property parameters. The simulation module is used to simulate the interaction between particles and DNA strands using the microscopic Monte Carlo simulation model to obtain the yield of double-strand breaks per unit dose; wherein, double-strand breaks are classified into multiple types according to their complexity. The calculation module is used to perform weighted calculations on the double-strand breakage yield of each complexity based on preset weights to obtain the relative biological effect; the relative biological effect is used to quantify the comprehensive damage degree of particle radiation to biological tissues at a unit dose. A determination module is used to determine an equivalent biological dose based on the relative biological effect, the equivalent biological dose being used to guide dose optimization of the particle beam therapy plan.

9. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

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