Radiation therapy planning method and device based on nanodosimetric quantities and electronic device

Abstract

The application provides a radiotherapy plan determination method and device based on nanodosimetry and electronic equipment, and relates to the technical field of radiation. The radiotherapy plan determination method based on nanodosimetry comprises the following steps: obtaining time-varying distribution data of nanodosimetry in a heavy ion beam mixed radiation field by using a simulation platform; the heavy ion beam mixed radiation field corresponds to a candidate radiotherapy plan under an ultrahigh dose rate for a target patient; the ultrahigh dose rate is a dose rate greater than a target threshold; obtaining a dose correction factor corresponding to the candidate radiotherapy plan by using a cell survival fraction model according to the time-varying distribution data of nanodosimetry; and adjusting the candidate radiotherapy plan according to the dose correction factor corresponding to the candidate radiotherapy plan to obtain a target radiotherapy plan for the target patient. The application can establish an accurate and reliable biophysical model of a heavy ion ultrahigh dose rate radiation field, so as to determine a precise radiotherapy plan based on the biophysical model.

Description

Technical Field

[0001] This invention relates to the field of radiation technology, and in particular to a method, apparatus, and electronic device for determining radiotherapy plans based on nanodosimetrics. Background Technology

[0002] Radiotherapy, as an important means of cancer treatment, aims to destroy tumor tissue to the greatest extent possible while minimizing damage to normal tissues. Dosage, as a key factor influencing tissue damage, has long been a crucial consideration in radiotherapy research and application. However, recent studies have revealed that dose rate is also a key element in reducing damage to normal tissues.

[0003] Preclinical studies have found that compared to traditional low-dose-rate (CONV) radiotherapy, ultra-high-dose-rate radiotherapy, delivered within milliseconds—"FLASH" radiotherapy—demonstrates a significant advantage in protecting healthy tissues while maintaining anti-tumor activity. Particle therapy provides an ideal pathway for the clinical translation of FLASH radiotherapy, with heavy ion beams (carbon ion beams) exhibiting outstanding advantages. They possess both the physical and biological characteristics of dense ionizing radiation, meeting the high dose-rate requirements for triggering the FLASH effect, and, thanks to the Bragg peak unique to heavy ions, becoming a superior FLASH radiotherapy method for deep tumors. The combination of these two aspects can broaden the therapeutic window. However, the advancement of FLASH radiotherapy in clinical application faces numerous challenges. On the hardware side, further improvements in the precise control of the dose rate are needed; on the software side, treatment planning needs optimization to better protect normal tissues. Currently, the calculation of biological effects in FLASH radiotherapy—a key factor distinguishing CONV radiotherapy—is still incomplete. Most FLASH treatment planning systems employ phenomenological models, which estimate the effective biological dose based only on experimental observations and a few parameters, without addressing the biological mechanisms triggering the FLASH effect.

[0004] Therefore, how to establish an accurate and reliable biophysical model of the heavy ion ultra-high dose rate radiation field, and how to determine a precise radiotherapy plan based on this biophysical model, is an urgent technical problem to be solved. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for determining radiotherapy plans based on nanodosimetry, in order to overcome the above-mentioned deficiencies in the prior art. It is used to establish an accurate and reliable biophysical model of a heavy ion ultra-high dose rate radiation field, so as to determine a precise radiotherapy plan based on the biophysical model.

[0006] This invention provides a method for determining radiotherapy planning based on nanodosimetric doses, comprising the following steps.

[0007] Using a simulation platform, time-varying distribution data of nanodosimetrics within a mixed heavy ion beam radiation field are obtained. This mixed heavy ion beam radiation field corresponds to a candidate radiotherapy plan at an ultra-high dose rate for the target patient. The ultra-high dose rate is a dose rate greater than a target threshold. Based on the time-varying distribution data of the nanodosimetrics, a dose correction factor corresponding to the candidate radiotherapy plan is obtained using a cell survival fraction model. The cell survival fraction model is used to obtain a cell survival curve based on the time-varying distribution data of the nanodosimetrics. The candidate radiotherapy plan is adjusted according to the dose correction factor to obtain a target radiotherapy plan for the target patient. The cell survival fraction model includes a first sub-term and a second sub-term. The first sub-term quantifies the influence of time-varying data of oxygen enhancement ratio and radiation dose on the cell survival fraction. The second sub-term quantifies the influence of time-varying data of intracellular reactive oxygen species on the cell survival fraction. The time-varying data of oxygen enhancement ratio is obtained based on the time-varying distribution data of the nanodosimetrics.

[0008] According to the present invention, a radiotherapy planning method based on nanodosimetry is provided, wherein the first sub-item is constructed in the following manner: Based on the first and second coefficients, a linear transformation is performed on the time-varying data of radiation dose to obtain the linear transformation result; based on the linear transformation result, the time derivative of radiation dose, and the time infinitesimal element, the first sub-term is established; wherein, the first coefficient is the linear coefficient in the linear square model after correction of the time-varying data of oxygen enhancement ratio; and the second coefficient is the square coefficient in the linear square model after correction of the time-varying data of oxygen enhancement ratio.

[0009] According to the present invention, a radiotherapy planning method based on nanodosimetry is provided, wherein the second sub-item is constructed in the following manner: A nonlinear transformation is performed on the time-varying data of reactive oxygen species to obtain the nonlinear transformation result; based on the nonlinear transformation result and the time infinitesimal element, the second sub-item is constructed.

[0010] According to the present invention, a method for determining radiotherapy planning based on nanodosimetry is provided, wherein the cell survival fraction model is expressed by the following formula: ; in, This represents time-varying data on radiation dose; For time infinitesimal elements; This is time-varying data for reactive oxygen species; , and All of these are parameters of a nonlinear function; This represents the differential change in the cell survival fraction; Radiation dose rate; in, The first coefficient is calculated using the following formula: ; in, These are the linear coefficients in the linear square model; It is a location r Time-varying data of oxygen enhancement ratio at the location; in, The second coefficient is calculated using the following formula: ; in, These are the square coefficients in the linear square model.

[0011] According to a radiotherapy planning method based on nanodosimetry provided by the present invention, the time-varying data of oxygen enhancement ratio are determined based on the time-varying distribution data of the nanodosimetry in the following manner: Based on the correspondence between the time-varying distribution data of the nanodositoids and the dynamic distribution data of oxygen partial pressure, the time-varying distribution data of oxygen partial pressure is obtained according to the time-varying distribution data of the nanodositoids; and based on the time-varying distribution data of oxygen partial pressure, the time-varying data of oxygen enhancement ratio is obtained through the calculation rules of oxygen enhancement ratio.

[0012] According to the present invention, a method for determining radiotherapy planning based on nanodosimetry is provided, wherein obtaining time-varying distribution data of nanodosimetry within a mixed heavy ion beam radiation field using a simulation platform includes: Using a simulation platform, time-varying distribution data of nanodosimetry in the radiation fields of each monoenergetic ion beam included in the heavy ion beam are obtained, as well as the flux and energy deposition spectra of each monoenergetic ion beam. Based on the flux and energy deposition spectra of each monoenergetic ion beam, the flux weight or energy deposition weight of each monoenergetic ion beam is determined. Based on the flux weight or energy deposition weight of each monoenergetic ion beam, the time-varying distribution data of nanodosimetry in the radiation fields of each monoenergetic ion beam are weighted and fused to obtain the time-varying distribution data of nanodosimetry in the mixed radiation field of the heavy ion beam.

[0013] The present invention also provides a radiotherapy planning device based on nanodosimetric doses, comprising: The first acquisition module is used to obtain time-varying distribution data of nanodosimetrics within a heavy ion beam mixed radiation field using a simulation platform; wherein the heavy ion beam mixed radiation field corresponds to a candidate radiotherapy plan at an ultra-high dose rate for the target patient; the ultra-high dose rate is a dose rate greater than a target threshold. The second acquisition module is used to obtain a dose correction factor corresponding to the candidate radiotherapy plan based on the time-varying distribution data of the nanodosimetrics using a cell survival fraction model; wherein the cell survival fraction model is used to obtain a cell survival curve based on the time-varying distribution data of the nanodosimetrics. The determination module is used to adjust the candidate radiotherapy plan according to the dose correction factor corresponding to the candidate radiotherapy plan to obtain a target radiotherapy plan for the target patient; wherein the cell survival fraction model includes a first sub-item and a second sub-item; the first sub-item is used to quantify the influence of time-varying data of oxygen enhancement ratio and radiation dose on the cell survival fraction; the second sub-item is used to quantify the influence of time-varying data of intracellular reactive oxygen species on the cell survival fraction; the time-varying data of oxygen enhancement ratio is obtained based on the time-varying distribution data of the nanodosimetrics.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the radiotherapy planning determination method based on nanodosimetry as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the radiotherapy planning determination method based on nanodosimetry as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the radiotherapy planning determination method based on nanodosimetry as described above.

[0017] This invention provides a method, apparatus, and electronic device for determining radiotherapy plans based on nanodosimetry. It utilizes a simulation platform to acquire time-varying distribution data of nanodosimetry within a mixed heavy ion beam radiation field corresponding to candidate radiotherapy plans at ultra-high dose rates for the target patient. This time-varying distribution data accurately reflects the dose at different locations within the radiation field at different times, providing a precise basis for in-depth analysis of radiation field characteristics. Based on the obtained time-varying distribution data of nanodosimetry, a cell survival fraction model is used. This model fully considers the impact of nanodosimetry changes over time on cell survival, thereby obtaining a dose correction factor corresponding to the candidate radiotherapy plan. Adjusting the candidate radiotherapy plan according to this dose correction factor makes the radiotherapy plan more closely match the actual radiation conditions, ultimately resulting in a target radiotherapy plan for the target patient. This significantly improves the accuracy of the radiotherapy plan, maximizing the protection of normal tissues while more effectively destroying tumor tissue, thus enhancing the therapeutic effect of ultra-high dose rate heavy ion radiotherapy and promoting the development of FLASH radiotherapy in clinical applications. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the radiotherapy planning determination method based on nanodosimetrics provided by the present invention.

[0020] Figure 2 This is a flowchart illustrating the method for determining the time-varying data of the oxygen enhancement ratio based on the time-varying distribution data of nanodosimetric quantities provided by the present invention.

[0021] Figure 3 This is a schematic diagram illustrating the process of establishing a cell survival fraction model provided by the present invention.

[0022] Figure 4 This is a schematic diagram of dynamic oxygen partial pressure distribution calculated based on a two-dimensional random capillary distribution model.

[0023] Figure 5 This is a schematic diagram of the radiotherapy planning device based on nanodosimetrics provided by the present invention.

[0024] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] The following is combined Figures 1-4 The present invention describes a radiotherapy planning method based on nanodosimetry.

[0027] Figure 1 This is a schematic flowchart of the radiotherapy planning determination method based on nanodosimetry provided by the present invention. Figure 1 As shown, the method includes the following: Step 101: Using a simulation platform, obtain time-varying distribution data of nanodosimetric quantities within the mixed radiation field of heavy ion beams.

[0028] The mixed radiation field of heavy ion beams corresponds to the candidate radiotherapy plan at ultra-high dose rate for the target patient.

[0029] A candidate radiotherapy plan is a pre-designed potential treatment plan that takes into account the specific condition of the target patient (such as tumor type, location, size, etc.), and includes factors such as specific radiation type (such as heavy ion beam), dose rate requirements (dose rate under ultra-high dose rate), radiation field composition, and expected therapeutic effect (such as maximizing destruction of tumor tissue and minimizing damage to normal tissue).

[0030] A simulation platform is computer software or a system used to simulate the behavior of heavy ion beams in a mixed radiation field.

[0031] A mixed heavy ion beam radiation field refers to a radiation environment composed of multiple heavy ion beams of different energies and types.

[0032] Ultra-high dose rate refers to radiotherapy conditions where the dose rate exceeds a preset target threshold. For example, in some FLASH (ultra-high dose rate) radiotherapy, the dose rate can reach tens or even hundreds of Gy per second.

[0033] Nanodosimetrics are quantitative parameters that describe the deposition of ionizing radiation energy at the nanoscale, such as the number of ionization events of DNA molecules per unit volume.

[0034] The time-varying distribution data of nanodosimetrics refers to the distribution of quantitative parameters (such as specific nanodosimetric indices) related to ionization events within a nanoscale volume element in a mixed radiation field of heavy ion beams at different time points over time.

[0035] In some embodiments, a simulation platform is used to obtain time-varying distribution data of nanodosimetry in the radiation fields of each monoenergetic ion beam included in the heavy ion beam, as well as the flux and energy deposition spectra of each monoenergetic ion beam; based on the flux and energy deposition spectra of each monoenergetic ion beam, the flux weight or energy deposition weight of each monoenergetic ion beam is determined; based on the flux weight or energy deposition weight of each monoenergetic ion beam, the time-varying distribution data of nanodosimetry in the radiation fields of each monoenergetic ion beam are weighted and fused to obtain the time-varying distribution data of nanodosimetry in the mixed radiation field of the heavy ion beam.

[0036] In the specific implementation process, the time-varying distribution data of nanodosimetrics in the mixed radiation field of heavy ion beams at ultra-high dose rates can be obtained through the following methods, so as to accurately characterize the radiation quality of heavy ion beams based on the time-varying distribution data of nanodosimetrics.

[0037] Step S1: Simulation calculation of oxide seed yield and distribution under ultra-high dose rate.

[0038] The Independent Reaction Time (IRT) method implemented in the Monte Carlo software TOPAS-nBio framework was used to simulate the G value (the amount of chemical substances generated or lost per 100 eV of deposition energy) of oxide species produced by carbon ion FLASH as a function of time. The simulation process is as follows: The types of ion beams involved and their energies are as follows: 12C (120-400 MeV / u).

[0039] Single pulse dose and dose rate: The FLASH parameters involved have an absorbed dose of 8-20 Gy in the field and an average dose rate of >40 Gy / s in the field, with a maximum of 200 Gy / s.

[0040] Geometric setup: The simulation setup consists of two concentric cubic water models with side lengths of 20 μm and 5 μm, respectively, uniformly irradiated with carbon ions. The smaller cube serves as the scoring region, while the larger cube ensures the contribution of backscattered electrons near the boundary of the smaller cube.

[0041] Ion source setup: Due to the uncertainty in the direction and position of ion tracks, an ion source designed by Bueno et al. was used to make the simulation process closer to reality. A monoenergetic carbon ion source is uniformly distributed on the surface of a small cube, with its initial direction randomly pointing towards the interior region of the small cube. This irradiation setup is similar to the random direction of incident radiation on a cell nucleus. This ion source structure fully guarantees the random distribution of ions.

[0042] Material settings: The filling material for all the above geometries is water.

[0043] Physicochemical process setup: The "TsEmDNAphysics" physics module and the "TsEmDNAChemistry" chemistry module are used, and diffusion coefficients and free radical reaction rates are added to the chemistry module to simulate the free radical yield of FLASH; for elastic scattering, the CPA100 method "G4DNACPA100ElasticModel" can be used.

[0044] S2: Establishment of a monoenergetic ion beam nanodosimetry dataset at ultra-high dose rates.

[0045] Monoenergetic ion beams include protons (0.5-100 MeV), 4He (1-100 MeV / u), and 12C (1-400 MeV / u), involving nanodosimetric quantities including: (The first moment of the conditional probability density distribution for ionized clusters with a size greater than or equal to 1). (The first moment of the conditional probability density distribution for ionized clusters with a size greater than or equal to 2). (The cumulative probability that the size of the ionized cluster is greater than or equal to 2). (Cumulative probability of ionized cluster size greater than or equal to 3). The TOPAS-nbio microtrack Monte Carlo simulation platform can be used to simulate the nanodosimetric quantities of various monoenergetic ion beams over time at different dose rates in a simplified DNA chromatin filament model. The specific simulation settings are as follows: Geometric setup: 1800 non-overlapping, uniformly distributed smaller cylinders are constructed within a large cylinder. The large cylinder has a base diameter of 30.4 nm and a height of 161 nm, corresponding to a single chromatin filament. The smaller cylinders represent the aforementioned nanovolume elements, each corresponding to 10 base pairs. During the simulation, the large cylinder containing the 1800 nanovolume elements is placed at the center of a cube with a side length of 200 nm. This cube is introduced to provide electronic equilibrium conditions.

[0046] Material settings: The filling material for all the above geometries is water.

[0047] Ion source setup: To make the simulation process closer to reality, the starting points of the ions are randomly distributed on the surface of the large cylinder, and the direction vector of the ions points to random points inside the large cylinder. This ion source setup can fully guarantee the random distribution of ions.

[0048] Physics settings: Monoenergetic ion beam nanodositology simulations utilize the G4EmDNAPhysics physics list. The physical models for electron interactions include: Rutherford elastic scattering model, plane wave first Born approximation (describing electron ionization and excitation from 11 eV to 1 MeV), Moller-Bhabha ionization model (for ionization processes greater than 1 MeV), Sanche model (describing vibrational excitation), and Melton model (describing molecular attachment). Furthermore, the physics list includes energy thresholds for electrons, positrons, and photons. These have no impact on the Geant4-DNA physics results, as the Geant4-DNA simulation process does not use any energy thresholds.

[0049] ICSD simulations were performed for each specific type, energy, and dose rate of monoenergetic ion beam, and the corresponding nanodosimetric quantities were calculated. , , , By summarizing the results, we can obtain a dataset of nanodosimetric quantities of various monoenergetic ion beams as a function of time at different dose rates (time-dependent distribution of nanodosimetric quantities).

[0050] S3: Calculate the time-dependent distribution of nanodosimetric quantities in a mixed radiation field of heavy ion beams at ultra-high dose rates.

[0051] During heavy ion irradiation, the radiation field at different penetration depths is a mixed radiation field, containing primary ions and various types of secondary ions. Monte Carlo simulations were used to obtain the flux and energy deposition spectra of various monoenergetic ion beams over time at different dose rates. Based on the established dataset of monoenergetic ion beam nanodosimetry over time at different dose rates, the time-dependent distribution of nanodosimetry in the mixed radiation field of heavy ion beams at ultra-high dose rates was obtained by superimposing the data according to the flux weights or energy deposition weights of each ion.

[0052] in, and The flux weights of the various ions contained in the mixed radiation field can be superimposed using the following formula: (1); (2); in, It is the first moment of the conditional probability density distribution for ionized clusters with a size greater than or equal to 1. It is the cumulative probability that the size of the ionized cluster is greater than or equal to 2. Both represent the combined effect after considering all ion types and energies in the mixed radiation field. , These represent the monoenergetic ion beams at ion type Z and energy E, respectively. and ; This represents the ion flux (or flux rate-related quantity) under ion type Z and energy E, i.e., the number of ions passing through a unit area per unit time.

[0053] in, and The following formula can be used to calculate the superposition of the energy deposition weights of various ions contained in the mixed radiation field: (3); (4); in, It is the first moment of the conditional probability density distribution of the ionized cluster size greater than or equal to 2. It is the cumulative probability that the size of the ionized cluster is greater than or equal to 3. Both represent the combined effect after considering all ion types and energies in the mixed radiation field. and Representing ion type Z and energy respectively E The monoenergetic ion beam and ; For ion type Z at energy E The energy of deposition is the energy lost and deposited by ions in a substance.

[0054] In some embodiments, nanodosimetric quantities that can effectively characterize the radiation quality of a heavy ion beam can be selected from the nanodosimetric dataset obtained above.

[0055] In practice, the above screening can be performed by calculating the dose-response relationship between radiation damage and nanodosimetric dose at different dose rates.

[0056] At conventional dose rates, LQ model parameters were calculated based on the LNDM model. ), and RBE value and nanodosage ( , , , To investigate the dependence of radiation damage on nanodosimetric quantities, and to analyze nanodosimetric quantities that can be used to characterize the radiation quality of carbon ion beams.

[0057] In some embodiments, at ultra-high dose rates, the dose-response relationship between DNA single-strand breaks and double-strand breaks and nanodosimetric doses can be investigated using a simulation platform (e.g., the TOPAS-nbio microtrack Monte Carlo simulation platform), establishing a reaction kinetic equation. By inputting the nanodosimetric dose into this reaction kinetic equation, the number of DNA single-strand breaks and double-strand breaks at the nanoscale is obtained, achieving precise characterization of the carbon ion FLASH radiation field at the DNA nanoscale. Based on the number of DNA single-strand breaks and double-strand breaks at the nanoscale output of the reaction kinetic equation, the radiation damage under different nanodosimetric dose inputs can be compared to determine whether the input nanodosimetric dose leads to radiation loss, thereby characterizing the radiation quality of the ion beam.

[0058] Step 102: Based on the time-varying distribution data of nanodosimetric doses, use the cell survival fraction model to obtain the dose correction factor corresponding to the candidate radiotherapy plan.

[0059] The cell survival fraction model is used to obtain cell survival curves based on time-varying distribution data of nanodosimetric doses.

[0060] The cell survival fraction model predicts cell survival under specific radiation conditions based on time-varying distribution data of nanodosimetric doses, thereby obtaining cell survival curves.

[0061] In some embodiments, the cell survival fraction model includes a first sub-term and a second sub-term; wherein the first sub-term is used to quantify the effect of time-varying data of oxygen enhancement ratio and time-varying data of radiation dose on cell survival fraction; the second sub-term is used to quantify the effect of time-varying data of intracellular reactive oxygen species on cell survival fraction; the time-varying data of oxygen enhancement ratio is obtained based on the time-varying distribution data of nanodosimetric amounts.

[0062] For an example of determining the oxygen enhancement ratio based on the time-varying distribution data of nanodosimetric doses, see [link to example]. Figure 2 The relevant content will not be repeated here.

[0063] In the specific implementation process, the first sub-item is constructed in the following way: Based on the first and second coefficients, a linear transformation is performed on the time-varying data of radiation dose to obtain the linear transformation result; based on the linear transformation result, the time derivative of radiation dose, and the time infinitesimal element, the first sub-term is established; wherein, the first coefficient is the linear coefficient in the linear square model after correction of the time-varying data of oxygen enhancement ratio; the second coefficient is the square coefficient in the linear square model after correction of the time-varying data of oxygen enhancement ratio.

[0064] Oxygen enhancement ratio (OER) is a parameter that describes the effect of oxygen concentration on the biological effects of radiotherapy. It is defined as the ratio of the dose required to produce the same biological effect under aerobic conditions to that required under hypoxic conditions. For example, OER=3 means that three times the dose is required under hypoxic conditions to achieve the same therapeutic effect as under aerobic conditions.

[0065] In practice, the time-varying radiation dose data can be linearly transformed in various ways based on the first and second coefficients, without being limited by the description in this manual.

[0066] In the specific implementation process, the product of the linear transformation result, the radiation dose time derivative, and the time infinitesimal can be used as the first sub-term.

[0067] In the specific implementation process, the second sub-item is constructed in the following way: A nonlinear transformation is performed on the time-varying data of reactive oxygen species to obtain the nonlinear transformation result; based on the nonlinear transformation result and the time infinitesimal element, a second sub-term is constructed.

[0068] In practice, time-varying data of reactive oxygen species can be nonlinearly transformed in various ways, without being limited by the description in this specification. For example, the logistic-type formula or the S-curve formula, or variations thereof, can be used to perform nonlinear transformations on the time-varying data of reactive oxygen species.

[0069] By way of example only, in some embodiments, the cell survival fraction model is represented by the following formula: (5); in, This represents time-varying data on radiation dose; For time infinitesimal elements; This is time-varying data for reactive oxygen species; , and All of these are parameters of a nonlinear function; This represents the differential change in the cell survival fraction; This represents the radiation dose rate.

[0070] As the first sub-item, This represents time-varying data on radiation dose; As the second sub-item, This is time-varying data for reactive oxygen species; in, The first coefficient is calculated using the following formula: (6); in, These are the linear coefficients in the Linear-Quadratic Model (LQ). It is a location r For time-varying data on the oxygen enhancement ratio at [location], see [reference]. Figure 2 The relevant descriptions in the text will not be repeated here.

[0071] in, The second coefficient is calculated using the following formula: (7); in, These are the square coefficients in the linear square model.

[0072] In the specific implementation process, time-varying data of reactive oxygen species can be obtained through the following methods. : The dependency between ROS changes and time-varying oxygen partial pressure distribution data is established beforehand, as shown in the following formula: (8); in, It represents the rate of change of the amount of reactive oxygen species over time, that is, the change in the amount of ROS per unit time, reflecting the dynamic changes of ROS over time. It is the rate of change of ROS under FLASH radiotherapy; The rate of change of ROS under conventional radiotherapy; It is a location-dependent coefficient that reflects the degree to which changes in oxygen partial pressure at different locations affect changes in ROS. It is a location r Location, Time t The partial pressure of oxygen at that time; It is a location r Oxygen partial pressure at location and time 0; It represents the change in reactive oxygen species (ROS), that is, the difference between the amount of ROS at a certain moment and the amount of ROS at the initial moment or other reference moments. This is a location-related ROS reduction rate coefficient, which differs between normal and tumor tissues. By introducing the differences in baseline ROS levels and clearance capacity between tumor cells and normal cells, the different responses of tumors and normal tissues during FLASH radiotherapy can be assessed.

[0073] By substituting the time-varying distribution data of oxygen partial pressure into the above formula (8), the change data of ROS can be calculated.

[0074] Step 103: Adjust the candidate radiotherapy plan according to the dose correction factor corresponding to the candidate radiotherapy plan to obtain the target radiotherapy plan for the target patient.

[0075] The Dose Modifying Factor (DMF) is a parameter used to quantify the ratio of the dose required for ultra-high dose rate (FLASH) radiotherapy to the dose required for conventional dose rate (CONV) radiotherapy under the same biological effect. This ratio reflects the impact of the FLASH effect on the dose, thereby allowing for adjustments to the dose in the radiotherapy plan.

[0076] The cell survival fraction model provided by this invention, based on nanodosimetry and comprehensively considering factors such as oxygen enhancement ratio and reactive oxygen species, can accurately assess the impact of FLASH radiotherapy on cell survival. Furthermore, based on this prediction result, a dose correction factor that accurately reflects the effect of FLASH radiotherapy is calculated.

[0077] Based on the dose correction factor, the radiation dose in the candidate radiotherapy plans is adjusted accordingly. By comparing the impact of different adjusted radiotherapy plans on cell survival (i.e., comparing cell survival fractions or survival curves under different radiotherapy plans), the most suitable radiotherapy plan for the target patient is selected as the target radiotherapy plan. Through the evaluation and adjustment of candidate radiotherapy plans, it can be ensured that the final determined target radiotherapy plan can minimize damage to normal tissues while maximizing the destruction of tumor tissue.

[0078] In the embodiments provided by the present invention, such as Figure 3As shown, considering the combined effects of different physiological parameters (including oxygen diffusivity, cellular oxygen consumption rate, microvessel density, oxygen depletion rate, and reactive oxygen species (ROS) reduction rate) and beam structure parameters (such as radiation dose, dose rate, and pulse segmentation scheme) on cell survival, a cell survival fraction model is constructed: oxygen diffusivity and microvessel density jointly determine the tissue oxygenation level and directly affect the spatiotemporal distribution of oxygen partial pressure, while cellular oxygen consumption rate and oxygen depletion rate further alter the local oxygen environment through dynamic oxygen consumption. Both jointly regulate the time-varying characteristics of the oxygen enhancement ratio. Intracellular ROS, as a key mediator of radiation damage, have their generation and clearance rates regulated by dose rate and pulse segmentation scheme. High dose rates (e.g., >40 Gy / s) and short pulse intervals can inhibit the enzymatic clearance of intracellular ROS, leading to transient ROS accumulation and thus enhancing the cell killing effect. This cell survival fraction model uses time-varying distribution data of nanodosimetric doses as input. By integrating the dynamic changes in OER with the real-time concentration of ROS (combining generation rate, clearance rate, and dose rate effect), it can accurately quantify cell survival fractions under different parameter combinations. Furthermore, by comparing cell survival fractions of conventional radiotherapy and FLASH radiotherapy, a dose correction factor is calculated. This dose correction factor accurately reflects the synergistic effect of "ultra-high-speed irradiation" and "hypoxia protection" in FLASH radiotherapy, providing a theoretical basis for optimizing pulse fractionation schemes, dose rate selection, and oxygenation status regulation. Ultimately, it enables precise prediction of the biological effects of FLASH radiotherapy and individualized dose planning.

[0079] Figure 2 This is a flowchart illustrating the method for determining the time-varying oxygen enhancement ratio based on the time-varying distribution data of nanodosimetric quantities provided by the present invention. Figure 2 As shown, the method includes the following: Step 201: Based on the correspondence between the time-varying distribution data of nanodositometrics and the dynamic distribution data of oxygen partial pressure, the time-varying distribution data of oxygen partial pressure is obtained according to the time-varying distribution data of nanodositometrics.

[0080] In the specific implementation process, time-varying distribution data of nanodosimetric quantities can be obtained through a simulation platform; dynamic distribution data of oxygen partial pressure can also be obtained; and the correspondence between the two can be established through data fitting and other methods.

[0081] For a detailed description of obtaining the time-varying distribution data of nanodosimetric quantities, please refer to the relevant content in step 101, which will not be repeated here.

[0082] Oxygen partial pressure dynamic distribution data refers to the distribution of oxygen concentration in tissues over time, such as the spatiotemporal changes in oxygen partial pressure in the tumor area before and after FLASH radiotherapy.

[0083] Oxygen depletion is one of the important biological mechanisms of FLASH radiotherapy. The survival rate of normal tissues and cancer cells after radiotherapy is exponentially related to dose and oxygen level. Even if the proportion of tissues with low oxygen levels is very small, it may still determine the response to radiotherapy. However, the changes in oxygen content caused by radiation-induced oxygen depletion (ROD) can explain the protective effect of FLASH radiotherapy on normal tissues, but it cannot explain the different responses of tumors. Therefore, a new FLASH model is needed to explain the different responses of tumors and normal tissues during FLASH radiotherapy.

[0084] Different reactions to FLASH radiation therapy are due to reactive oxygen species (ROS). Although FLASH radiation therapy rapidly depletes oxygen, it also reduces the amount of ROS produced intracellularly, which is another explanation for its protective effect. On the other hand, because tumor cells have high basal ROS levels and lack enzymes that can rapidly clear ROS, ROS in tumor cells are more likely to cause cell death.

[0085] Oxygen partial pressure (OPP) is a crucial indicator for measuring oxygen levels, and dynamic OPP distribution data can directly reflect the changes in oxygen content over time in different tissues and regions. For example, OPP distribution data can quantify the specific degree and range of oxygen content changes after ROD (Reactive Oxygen Demand) occurs, clarifying how OPP changes with ROD at different time points and tissue depths, thus providing a deeper understanding of the mechanism of ROD in FLASH radiotherapy. Furthermore, OPP distribution data can quantify the generation of ROS (Reactive Oxygen Species) in normal and tumor tissues under different OPP environments, further exploring the impact of ROS on different responses in tumor and normal tissues during FLASH radiotherapy, as well as its interaction with factors such as oxygen consumption. Therefore, dynamic OPP distribution data provides a more accurate understanding of the real-time dynamic changes in oxygen levels in normal and tumor tissues during FLASH radiotherapy, offering data support for studying the impact of oxygen consumption on radiotherapy responses.

[0086] In the specific implementation process, the dynamic distribution data of oxygen partial pressure is obtained through the following methods.

[0087] Considering the spatial heterogeneity of oxygenation in tissues, a randomly distributed two-dimensional finite element model at the capillary level is created. Using this finite element model, the initial oxygen partial pressure is determined; based on the initial oxygen partial pressure, the oxygen diffusion dynamics are calculated, thus obtaining the diffuse oxygen partial pressure.

[0088] In the specific implementation process, the classic microvessel-tissue Krogh model can be used to simulate capillaries. The diffusion equation can be established and solved through the Krogh model to calculate the dynamic distribution of oxygen concentration in the tissue, and finally the diffusion equation represented by the following formula is obtained.

[0089] (9); in, Indicates the location r and time t The oxygen partial pressure p at that point versus time t The partial derivative reflects the rate of change of oxygen partial pressure over time; The diffusion coefficient of oxygen describes how easily oxygen diffuses in tissues and is related to the characteristics of the tissue and the physical properties of oxygen. The Laplace operator is used to calculate the spatial second derivative of the oxygen partial pressure in two-dimensional or three-dimensional space, reflecting the spatial distribution of the oxygen partial pressure and its diffusion trend. It is a location r Location, Time t The partial pressure of oxygen at that time; It is the largest parameter related to oxygen metabolism; k These are metabolic constants related to oxygen metabolism. D These are parameters related to the characteristics of capillaries (such as their diffusion capacity). T These are parameters related to the time scale; This is an initial or baseline parameter. It is a constant related to oxygen depletion, reflecting a certain resistance or depletion characteristic in the process of oxygen diffusion from capillaries to tissues.

[0090] The above diffusion equations consider the effects of oxygen diffusion, metabolism, and oxygen depletion processes on oxygen partial pressure. Capillaries are randomly distributed in... Within the domain, the microvascular density can be altered by changing the number of capillaries.

[0091] In practical implementation, the diffusion equation can be solved using the finite element method in Mathematica software. First, the steady-state oxygen distribution needs to be solved, and then the solution is used as the initial condition for the FLASH calculation. (Location r (Oxygen partial pressure at time 0), at the edge of the domain boundary Applying zero-flux Neumann boundary conditions and Dirichlet boundary conditions to each capillary, the diffusion equation (9) is solved to obtain the dynamic distribution of oxygen partial pressure under FLASH conditions. For conventional radiotherapy, the formula is... Set it to 0. The calculation result is as follows: Figure 4 As shown.

[0092] Figure 4 Image (a) shows a spatial distribution cloud map of oxygen partial pressure in a two-dimensional tissue section from conventional radiotherapy. Figure 4Figure (b) shows a spatial distribution cloud map of oxygen partial pressure in a two-dimensional tissue section after FLASH radiotherapy. The circles in the figure represent the cross-sectional locations of capillaries, and the color distribution reflects the concentration gradient of oxygen after diffusion from the capillaries to the surrounding tissues. The color bars in (a) and (b) represent the numerical values ​​of oxygen partial pressure (in millimeters of mercury). Red / yellow (high values, approximately 30-40) represents high oxygen partial pressure areas near capillaries, while blue (low values, close to 0) represents low oxygen partial pressure areas (hypoxic areas) farther from capillaries or where oxygen is drastically consumed. In (b), the number "15" points to a high oxygen partial pressure area near capillaries.

[0093] As an example, after obtaining the time-varying distribution data of nanodosimetry and the dynamic distribution data of oxygen partial pressure, the raw data are first preprocessed: the two distribution data are aligned according to a preset time resolution (e.g., 1 ms) to generate a paired dataset, and outliers (e.g., Q>150 or PO2<0) are removed. Based on the biological mechanism of high dose rate radiation accelerating oxygen consumption, it is assumed that the two satisfy an exponential decay correlation, and an exponential decay model is used to fit the dynamic data of nanodosimetry and oxygen partial pressure. The model parameters are optimized using algorithms such as the least squares method, and finally a mathematical model quantifying the dynamic correspondence between the two is obtained. Substituting the time-varying distribution data of nanodosimetry into the mathematical model obtained above, the time-varying distribution data of oxygen partial pressure can be obtained.

[0094] Step 202: Based on the time-varying distribution data of oxygen partial pressure, the time-varying data of oxygen enhancement ratio are obtained through the calculation rules of oxygen enhancement ratio.

[0095] In the specific implementation process, after obtaining the time-varying distribution data of oxygen partial pressure, the time-varying data of oxygen partial pressure is used as input, and the calculation is performed according to the oxygen enhancement ratio calculation method and rules to obtain the data of oxygen enhancement ratio changing with time, that is, the time-varying data of oxygen enhancement ratio.

[0096] The radiotherapy planning device based on nanodosimetry provided by the present invention is described below. The radiotherapy planning device based on nanodosimetry described below and the radiotherapy planning method based on nanodosimetry described above can be referred to in correspondence.

[0097] Figure 5 This is a schematic diagram of the radiotherapy planning device based on nanodosimetry provided by the present invention. Figure 5 As shown, the radiotherapy planning device 500 based on nanodosimetric doses includes the following modules: The first acquisition module 510 is used to obtain time-varying distribution data of nanodosimetry in a heavy ion beam mixed radiation field using a simulation platform; wherein the heavy ion beam mixed radiation field corresponds to a candidate radiotherapy plan at an ultra-high dose rate for the target patient; the ultra-high dose rate is a dose rate greater than a target threshold.

[0098] The second acquisition module 520 is used to obtain the dose correction factor corresponding to the candidate radiotherapy plan based on the time-varying distribution data of the nanodosimetric amount and the cell survival fraction model; wherein, the cell survival fraction model is used to obtain the cell survival curve based on the time-varying distribution data of the nanodosimetric amount.

[0099] The determination module 530 is used to adjust the candidate radiotherapy plan according to the dose correction factor corresponding to the candidate radiotherapy plan to obtain the target radiotherapy plan for the target patient.

[0100] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640, wherein the processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute a radiotherapy planning method based on nanodosimetry. This method includes: using a simulation platform to obtain time-varying distribution data of nanodosimetry within a mixed heavy ion beam radiation field; wherein the mixed heavy ion beam radiation field corresponds to a candidate radiotherapy plan at an ultra-high dose rate for the target patient; the ultra-high dose rate is a dose rate greater than a target threshold; based on the time-varying distribution data of nanodosimetry, using a cell survival fraction model to obtain a dose correction factor corresponding to the candidate radiotherapy plan; wherein the cell survival fraction model is used to obtain a cell survival curve based on the time-varying distribution data of nanodosimetry; and adjusting the candidate radiotherapy plan according to the dose correction factor corresponding to the candidate radiotherapy plan to obtain a target radiotherapy plan for the target patient.

[0101] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0102] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the radiotherapy planning determination method based on nanodosimetry provided by the above methods. The method includes: using a simulation platform to obtain time-varying distribution data of nanodosimetry in a mixed heavy ion beam radiation field; wherein the mixed heavy ion beam radiation field corresponds to a candidate radiotherapy plan at an ultra-high dose rate for a target patient; the ultra-high dose rate is a dose rate greater than a target threshold; based on the time-varying distribution data of nanodosimetry, using a cell survival fraction model to obtain a dose correction factor corresponding to the candidate radiotherapy plan; wherein the cell survival fraction model is used to obtain a cell survival curve based on the time-varying distribution data of nanodosimetry; and adjusting the candidate radiotherapy plan according to the dose correction factor corresponding to the candidate radiotherapy plan to obtain a target radiotherapy plan for the target patient.

[0103] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a radiotherapy planning method based on nanodosimetry provided by the methods described above. This method includes obtaining time-varying distribution data of nanodosimetry within a mixed heavy ion beam radiation field using a simulation platform; wherein the mixed heavy ion beam radiation field corresponds to a candidate radiotherapy plan at an ultra-high dose rate for a target patient; the ultra-high dose rate is a dose rate greater than a target threshold; obtaining a dose correction factor corresponding to the candidate radiotherapy plan using a cell survival fraction model based on the time-varying distribution data of nanodosimetry; wherein the cell survival fraction model is used to obtain a cell survival curve based on the time-varying distribution data of nanodosimetry; and adjusting the candidate radiotherapy plan according to the dose correction factor corresponding to the candidate radiotherapy plan to obtain a target radiotherapy plan for the target patient.

[0104] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0105] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining radiotherapy planning based on nanodosimetry, characterized in that, include: Using a simulation platform, time-varying distribution data of nanodosimetry within a mixed heavy ion beam radiation field were obtained; wherein, the mixed heavy ion beam radiation field corresponds to a candidate radiotherapy plan at an ultra-high dose rate for the target patient; the ultra-high dose rate is a dose rate greater than the target threshold. Based on the time-varying distribution data of the nanodosimetric dose, the dose correction factor corresponding to the candidate radiotherapy plan is obtained using the cell survival fraction model; wherein, the cell survival fraction model is used to obtain the cell survival curve based on the time-varying distribution data of the nanodosimetric dose; The candidate radiotherapy plan is adjusted according to the dose correction factor corresponding to the candidate radiotherapy plan to obtain the target radiotherapy plan for the target patient; The cell survival fraction model includes a first sub-term and a second sub-term. The first sub-term is used to quantify the effects of time-varying data on oxygen enhancement ratio and radiation dose on cell survival fraction. The second sub-term is used to quantify the effects of time-varying data on intracellular reactive oxygen species on cell survival fraction. The time-varying data on oxygen enhancement ratio is obtained based on the time-varying distribution data of the nanodosimetric amount. The first sub-item is constructed in the following manner: Based on the first and second coefficients, a linear transformation is performed on the time-varying data of radiation dose to obtain the linear transformation result; The product of the linear transformation result, the radiation dose time derivative, and the time infinitesimal element is taken as the first sub-term; Wherein, the first coefficient is the linear coefficient in the linear square model after correction with time-varying data of oxygen enhancement ratio; the second coefficient is the square coefficient in the linear square model after correction with time-varying data of oxygen enhancement ratio. The second sub-item is constructed in the following manner: The time-varying data of reactive oxygen species are nonlinearly transformed using a logistic formula to obtain the nonlinear transformation results. Based on the nonlinear transformation result and the time infinitesimal element, the second sub-item is constructed.

2. The method for determining radiotherapy planning based on nanodosimetry according to claim 1, characterized in that, The cell survival fraction model is expressed by the following formula: ； in, This represents time-varying data on radiation dose; For time infinitesimal elements; This is time-varying data for reactive oxygen species; , and All of these are parameters of a nonlinear function; This represents the differential change in the cell survival fraction; Radiation dose rate; in, The first coefficient is calculated using the following formula: ； in, These are the linear coefficients in the linear square model; It is a location r Time-varying data of oxygen enhancement ratio at the location; in, The second coefficient is calculated using the following formula: ； in, These are the square coefficients in the linear square model.

3. The method for determining radiotherapy planning based on nanodosimetry according to any one of claims 1 to 2, characterized in that, The time-varying data of the oxygen enhancement ratio were determined based on the time-varying distribution data of the nanodoscientific amount in the following manner: Based on the correspondence between the time-varying distribution data of the nanodositometer and the dynamic distribution data of oxygen partial pressure, the time-varying distribution data of oxygen partial pressure is obtained according to the time-varying distribution data of the nanodositometer. Based on the time-varying distribution data of the oxygen partial pressure, the time-varying data of the oxygen enhancement ratio are obtained through the calculation rules of the oxygen enhancement ratio.

4. The method for determining radiotherapy planning based on nanodosimetry according to claim 1, characterized in that, The time-varying distribution data of nanodosimetric quantities within the mixed radiation field of heavy ion beams obtained using the simulation platform include: Using a simulation platform, time-varying distribution data of nanodosimetrics in the radiation field of each monoenergetic ion beam contained in the heavy ion beam, as well as flux and energy deposition spectra of each monoenergetic ion beam, were obtained. Based on the flux and energy deposition spectrum of each monoenergetic ion beam, determine the flux weight or energy deposition weight of each monoenergetic ion beam. Based on the flux weights or energy deposition weights of various monoenergetic ion beams, the time-varying distribution data of nanodosimetry in the radiation field of each monoenergetic ion beam are weighted and fused to obtain the time-varying distribution data of nanodosimetry in the mixed radiation field of the heavy ion beam.

5. A radiotherapy planning device based on nanodosimetry, characterized in that, include: The first acquisition module is used to obtain time-varying distribution data of nanodosimetry within a heavy ion beam mixed radiation field using a simulation platform; wherein the heavy ion beam mixed radiation field corresponds to a candidate radiotherapy plan at an ultra-high dose rate for the target patient; the ultra-high dose rate is a dose rate greater than a target threshold. The second acquisition module is used to obtain the dose correction factor corresponding to the candidate radiotherapy plan based on the time-varying distribution data of the nanodosimetric amount and the cell survival fraction model; wherein, the cell survival fraction model is used to obtain the cell survival curve based on the time-varying distribution data of the nanodosimetric amount; The determination module is used to adjust the candidate radiotherapy plan according to the dose correction factor corresponding to the candidate radiotherapy plan to obtain the target radiotherapy plan for the target patient; The cell survival fraction model includes a first sub-term and a second sub-term. The first sub-term is used to quantify the effects of time-varying data on oxygen enhancement ratio and radiation dose on cell survival fraction. The second sub-term is used to quantify the effects of time-varying data on intracellular reactive oxygen species on cell survival fraction. The time-varying data on oxygen enhancement ratio is obtained based on the time-varying distribution data of the nanodosimetric amount. The first sub-item is constructed in the following manner: Based on the first and second coefficients, a linear transformation is performed on the time-varying data of radiation dose to obtain the linear transformation result; The product of the linear transformation result, the radiation dose time derivative, and the time infinitesimal element is taken as the first sub-term; Wherein, the first coefficient is the linear coefficient in the linear square model after correction with time-varying data of oxygen enhancement ratio; the second coefficient is the square coefficient in the linear square model after correction with time-varying data of oxygen enhancement ratio. The second sub-item is constructed in the following manner: The time-varying data of reactive oxygen species are nonlinearly transformed using a logistic formula to obtain the nonlinear transformation results. Based on the nonlinear transformation result and the time infinitesimal element, the second sub-item is constructed.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the radiotherapy planning determination method based on nanodosimetry as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the radiotherapy planning determination method based on nanodosimetry as described in any one of claims 1 to 4.

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