To generate multiple possible treatment plans for multi-criterion optimization.

Abstract

A method for generating a plurality of possible treatment plans, each treatment plan specifying a distribution of radiation to a target volume, is provided, the method being performed by a treatment planning system and comprising generating a plurality of possible treatment plans (12) for multi-criteria optimization (MCO) based on a set of optimization functions, at least one of which depends on the distribution of the product of the radiation dose and linear energy transfer (LET) in a specified region of the patient, resulting in a plurality of treatment plans.

Description

Technical Field

[0001] The present disclosure relates to the field of treatment planning for the distribution of radiation to a target volume, and more particularly to multi-criteria optimization in this context.

Background Art

[0002] In ion beam therapy, a beam of ions (e.g., protons or heavier ions) is directed towards a target volume. The target volume can represent, for example, a cancerous tumor. These particles penetrate tissue and deliver a dose of energy to induce cell death. An advantage of ion beam therapy is the presence of a pronounced peak in the dose distribution known as the Bragg peak. The Bragg peak is a peak in dose delivery that occurs at a specific depth, after which the dose delivery rapidly decreases. This can be compared to electron beam therapy or X-ray (photon) therapy where the maximum dose always occurs at a shallow depth and the dose reduction distally cannot be controlled by the same sharp decrease as in the case of ion therapy.

[0003] The depth of the Bragg peak in a patient can be controlled by adjusting the kinetic energy of the particles. The lateral position can be controlled using electromagnets to deflect the focused beam. This enables the delivery of a very localized dose to a well-controlled position within the patient's body. The dose delivered by a specific combination of kinetic energy and lateral deflection of the beam is called a spot. The number of particles delivered in a spot is generally called the spot weight. By providing spots at many different positions in three-dimensional space, the target volume can be covered with a desired dose distribution. The kinetic energy of the spots is often, but not necessarily, distributed for many discrete energies. A group of spots having the same kinetic energy but different lateral deflections is often called an energy layer. This procedure is called active scanning ion beam therapy, also known as pencil beam scanning.

[0004] The treatment planning system determines how the spots should be delivered, thereby obtaining the treatment plan. The treatment planning system determines the energy layers to be used, as well as the distribution and weighting of the spots within them; however, the treatment planning system does not deliver the ion beam. Ion beam delivery is carried out by the radiation delivery system provided with the treatment plan.

[0005] Treatment planning can be performed in many different ways. One method is to use Multi-Criteria Optimization (MCO). In MCO, multiple possible treatment plans are generated, and each is optimized in a different way. The planning operator can then linearly combine these possible treatment plans in real time, and this combination is generated and visualized in real time along with performance metrics.

[0006] Giantsoudi Drosoula et al. published "Linear Energy Transfer-Guided Optimization in Intensity Modulated Proton Therapy: Feasibility Study and Clinical Potential" in the International Journal of Radiation: Oncology, Biology, Physics, Pergamon Press, USA, vol. 87, no. 1, June 19, 2013, pp. 216-222. However, any improvements to the method of generating treatment plans would be greatly appreciated. [Overview of the Initiative]

[0007] One objective is to find ways to improve optimization and to improve MCO in relation to the relative biological effectiveness ratio (RBE).

[0008] According to a first aspect, a method is provided for generating a plurality of possible treatment plans, each treatment plan specifying the distribution of radiation to a target volume, the method being performed by a treatment planning system and including the step of generating a plurality of possible treatment plans (12) for multi-criterion optimization (MCO) based on a set of optimization functions. At least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer, i.e., LET, in a specified area of ​​the patient, thereby yielding a plurality of treatment plans.

[0009] At least one optimization function may be specified as the objective.

[0010] The objective may be to minimize or maximize at least one optimization function value.

[0011] At least one optimization function may be specified as a constraint.

[0012] The constraint may also be that at least one optimization function value must not exceed a specified value, or at least one optimization function value must not be lower than a specified value.

[0013] This method may further include the step of providing multiple treatment plans to the MCO module.

[0014] According to a second embodiment, a treatment planning system is provided for generating a plurality of possible treatment plans, each treatment plan specifying the distribution of radiation to a target volume. The treatment planning system comprises a processor and a memory that stores instructions causing the treatment planning system, when executed by the processor, to perform a step of generating a plurality of possible treatment plans (12) for multi-criterion optimization (MCO) based on a set of optimization functions, wherein at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified area of ​​the patient, thereby obtaining a plurality of treatment plans.

[0015] At least one optimization function may be specified as the objective.

[0016] The objective may be to minimize or maximize at least one optimization function value.

[0017] At least one optimization function may be specified as a constraint.

[0018] The constraint may also be that at least one optimization function value must not exceed a specified value, or at least one optimization function value must not be lower than a specified value.

[0019] According to a third aspect, a computer program is provided for generating a plurality of possible treatment plans, each treatment plan specifying the distribution of radiation to a target volume. The computer program includes computer program code that, when run on a treatment planning system, causes the treatment planning system to perform a multi-criterion optimization based on a set of optimization functions, where at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified area of ​​the patient, thereby obtaining a plurality of treatment plans.

[0020] According to the fourth aspect, a computer program product is provided comprising a computer program according to the third aspect and computer-readable means in which the computer program is stored.

[0021] In general, all terms used in the claims should be interpreted according to their ordinary meanings in the art unless otherwise explicitly defined herein. Every reference to “one / one / its element, apparatus, component, means, process, etc.” should be interpreted as referring publicly to at least one example of such element, apparatus, component, means, process, etc. unless otherwise explicitly stated. The steps of any method disclosed herein do not need to be performed in the exact order disclosed unless otherwise explicitly stated.

[0022] Next, embodiments and implementations will be described by way of example while referring to the accompanying drawings.

Brief Description of the Drawings

[0023] [Figure 1] It is a schematic diagram illustrating an environment to which the embodiments shown in this specification can be applied. [Figure 2] It is a schematic graph illustrating dose delivery using ions, particularly Bragg peaks, with the radiation delivery system of FIG. 1. [Figure 3] It is a schematic chart illustrating the functional modules of the treatment planning system of FIG. 1. [Figure 4] It is a flowchart illustrating an embodiment of a method for generating a plurality of possible treatment plans performed in the treatment planning system of FIG. 1. [Figure 5] It is a schematic chart illustrating the components of the treatment planning system of FIG. 1 according to an embodiment. [Figure 6] An example of a computer program product including computer-readable means is shown.

Modes for Carrying Out the Invention

[0024] Next, the aspects of the present disclosure will be more fully described below while referring to the accompanying drawings showing specific embodiments of the present invention. However, these aspects can be embodied in many different forms and should not be construed as limiting. On the contrary, these embodiments are provided as examples to make the present disclosure thorough and complete and to fully convey the scope of all aspects of the present invention to those skilled in the art. Like numbers refer to like elements throughout this specification.

[0025] FIG. 1 is a schematic diagram illustrating an environment to which the embodiments shown in this specification can be applied. The treatment planning system 1 determines the distribution of radiation for radiotherapy. This is transmitted as a treatment plan 12 to the radiation delivery system 2. Based on the treatment plan, the radiation delivery system 2 generates a beam 7 to deliver radiation to the target volume 3 of the patient while avoiding radiation to the risk organ 5.

[0026] The radiation delivery system 2 may be, for example, an ion beam system for pencil beam scanning. In such a case, the radiation is delivered at a number of spots, where each spot has a position in a three-dimensional coordinate system. The position of the maximum dose (Bragg peak) of the spots along the depth direction, i.e., the z-axis, is controlled by the kinetic energy of the ions, and the higher the energy, the deeper the position where the maximum dose occurs. Further, the lateral position is controlled using an electromagnet for deflecting the beam 7. In this way, a dose distribution can be achieved that three-dimensionally covers the target volume 3 while keeping the radiation to the risk organ 5 low.

[0027] Alternatively, the radiation delivery system 2 is based on tomotherapy, where the radiation is delivered during a circular or helical movement around the patient.

[0028] Other types of radiation delivery systems are also possible.

[0029] FIG. 2 is a schematic graph illustrating dose delivery using the radiation delivery system 2 of FIG. 1 with ions, particularly the Bragg peak. The horizontal axis represents the depth in water, and the vertical axis represents the dose. Curve 20 shows the distribution of the dose with respect to the depth. The Bragg peak 22 is the peak of the curve, which appears just before the sharp decrease in dose.

[0030] When radiation is delivered to a patient, ions produce different biological effects in terms of the cells destroyed compared to photons, which produce the same amount of absorbed dose (measured in Gray). To allow comparison between dose levels from ion radiation and dose levels from photon radiation, ion doses are often converted to photon dose distributions that produce equivalent biological effects in the patient. This ratio of the photon equivalent dose to the physical absorbed dose from ion radiation is called the relative biological effectiveness ratio (RBE). Therefore, the conversion between physical absorbed dose and photon equivalent dose is performed using an RBE model. Several RBE models are available, ranging from the simplest ones with only a constant conversion factor to very complex ones determined by, for example, the type of ion, ion energy, and target elemental composition.

[0031] The most common RBE model used for photoiontotherapy is the constant-coefficient model, where RBE = 1.1 is commonly used for protons. However, it is well known that the RBE can be substantially higher in some parts of the dose distribution, potentially producing undesirable effects. In ion beam therapy, high linear energy transfer (LET), which is the distance derivative of the absorbed dose (i.e., the derivative of curve 20 in Figure 2), is known to have a strong correlation with a high RBE. Due to this effect, it is desirable to control the distribution of LET in patients when a constant-coefficient RBE model is used.

[0032] According to the embodiments described herein, multi-criterion optimization (MCO) is performed, where the optimization function depends on the distribution of the product of radiation dose and LET (expressed herein as D×LET) in a specified area of ​​the patient, or the optimization function uses other RBE models compared to an RBE model used for other purposes and / or constraints of the MCO problem. This is described in more detail below.

[0033] Figure 3 is a schematic diagram illustrating a functional module of the treatment planning system shown in Figure 1. This module is implemented using software instructions, such as a computer program, executed in the treatment planning system 1. Alternatively, or additionally, this module may be implemented using hardware such as one or more of the following: ASIC (Application-Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or discrete logic circuits.

[0034] The optimization module 10 is a module that performs optimization to arrive at possible treatment plans. As will be described in more detail below, the optimization module 10 can determine several different treatment plans for multi-criterion optimization (MCO). Each treatment plan is optimized based on one or more optimization functions. Each treatment plan can be restricted by constraints that define the optimization function must have a value above a certain level or a value below a certain level. Alternatively or additionally, each treatment plan may have the objective of defining the optimization function must have the highest possible value or the lowest possible value.

[0035] The optimization module outputs multiple treatment plans, each optimized based on a specific set of one or more optimization functions as constraints and / or objectives. Multiple plans include a single objective plan, which is optimized for a single optimization function. Further multiple plans may include additional plans that combine multiple optimization functions.

[0036] Since many optimization functions are contradictory, the MCO module 11 is used to find a balance between the optimization functions. The MCO module 11 allows the planning operator (i.e., the user of the treatment planning system) to generate a treatment plan that is navigated based on multiple treatment plans using multi-criterion optimization. The planning operator can freely adjust any one or more priorities of the treatment plans. When the treatment plan is adjusted, the navigated treatment plan is regenerated as a concave linear combination of the multiple treatment plans, for example, using slider control in a graphical user interface (GUI).

[0037] The system calculates several performance metrics of the navigated treatment plan and presents them to the planning operator in real time. These performance metrics include a graphical representation of dose distribution (2D and / or 3D) and numerical values, as well as binary indicators such as whether a specific clinical objective is being met.

[0038] This allows the planning operator to adjust the weights of different possible treatment plans and evaluate different treatment plans in real time. When the planning operator is satisfied with the performance metrics, the navigated treatment plan is used as the basis for treatment by the radiation delivery system.

[0039] Figure 4 is a flowchart illustrating an embodiment of a method for generating multiple possible treatment plans in the treatment planning system of Figure 1. Each treatment plan specifies the distribution of radiation to the target volume. This method can be performed in the optimization module 10 of the treatment planning system 1.

[0040] In step 40, which generates the plan, the treatment planning system generates multiple possible plans. These plans are intended for use for MCO (as described above). The plans are generated based on a set of optimization functions. At least one optimization function depends on the distribution of the product of radiation dose and LET (D×LET) in a specified area of ​​the patient. This step yields multiple treatment plans.

[0041] The specified region may be any suitable region defined in three dimensions, such as a target volume, a risk organ, or a subregion thereof.

[0042] Optimization is based on a set of optimization functions obtained by iteratively adjusting a set of optimization variables. The composition of the optimization variables determines the value of the optimization function.

[0043] In one embodiment, at least one optimization function is designated as an objective. This objective may be to minimize or maximize at least one optimization function value.

[0044] In one embodiment, at least one optimization function is specified as a constraint. For example, this constraint may be that at least one optimization function value must not exceed a specified value, or at least one optimization function value must not be lower than a specified value.

[0045] In step 42, which provides a treatment plan for any MCO, the treatment plan is provided to the MCO module 11 after optimization, at which point the planning operator can navigate the plan as described above. In other words, step 40 corresponds to an optimization stage (at least partially optimized for D×LET) for generating multiple plans that are provided to the MCO module in this step (step 42). These plans can then be combined linearly in real time by the operator of the MCO module.

[0046] By including an optimization function based on the D×LET distribution, optimization based on D×LET has already been performed. Since the D×LET distribution is closely related to RBE, this significantly improves the plan in terms of RBE.

[0047] Figure 5 is a schematic diagram illustrating the components of the treatment planning system 1 of Figure 1 according to one embodiment. The processor 60 is provided using one or more of any combination of preferred central processing units (CPUs), multiprocessors, microcontrollers, digital signal processors (DSPs), application-specific integrated circuits, etc., which are stored in memory 64 and are therefore capable of executing software instructions 67, which may be computer program products. The processor 60 can be configured to perform the method described with reference to Figure 4 above.

[0048] Memory 64 may be any combination of random access memory (RAM) and read-only memory (ROM). Memory 64 also includes persistent storage, which may be any one or a combination of magnetic memory, optical memory, solid-state memory, or even remotely mounted memory.

[0049] A data memory 66 is also provided for reading and / or storing data during the execution of software instructions in the processor 60.

[0050] The treatment planning system 1 further includes an I / O interface 62 for communication with other external entities. Optionally, the I / O interface 62 also includes a user interface.

[0051] Other components of the treatment planning system 1 are omitted in order to avoid ambiguity of the concepts presented herein.

[0052] Figure 6 shows an example of a computer program product including computer-readable means. This computer-readable means can store a computer program 91, which can cause a processor to execute methods according to the embodiments described herein. In this example, the computer program product is an optical disc such as a CD (Compact Disc), DVD (Digital Multipurpose Disc), or Blu-ray Disc. As described above, computer program products such as the computer program product 64 in Figure 5 can also be embodied in the device's memory. While the computer program 91 is schematically shown here as a track on the depicted optical disc, the computer program can be stored in any method suitable for the computer program product, such as removable solid-state memory, for example, a Universal Serial Bus (USB) drive.

[0053] Next, embodiments are listed below along with Roman numerals.

[0054] i. A method for verifying the quality of a treatment plan, wherein the treatment plan specifies the distribution of radiation and thereby delivers radiation to a planned target volume, and the method is performed by a quality assurance device, A process of obtaining a treatment plan calculated in a treatment planning system and a corresponding first dose, which is the predicted dose to be accumulated in the patient using the treatment plan. The process of starting the calculation of the secondary dose, which is the dose accumulated by the treatment plan, using a secondary dose calculation algorithm. The process of repeatedly calculating the confidence interval of comparative statistical measurements by comparing the first dose and secondary dose over a defined geometric volume, and The process of suspending the calculation of secondary doses when the confidence interval is better than at least one predetermined criterion (in which case the treatment plan is considered to have passed quality assurance). Methods that include...

[0055] ii. The method according to embodiment i, wherein each voxel in the secondary dose includes an estimate of the current confidence interval for the voxel.

[0056] iii. The method according to embodiment i or ii, wherein the defined geometric volume is the planned target volume.

[0057] iv. The method according to embodiment i or ii, wherein the defined geometric volume is a risk organ.

[0058] v. The method according to embodiment i or ii, wherein the defined geometric volume includes the planned target volume and the organ at risk.

[0059] vi. The method according to any one of the prior embodiments, wherein the step of calculating the confidence interval of the comparative statistical measurement is based on the available confidence interval for the voxel in the secondary dose and the possible spread of the secondary dose.

[0060] vii. The method according to embodiment vi, wherein the comparative statistical measurement is based on calculating similarity by accumulating the difference measurements between corresponding voxels at the first dose and the second dose.

[0061] viii. The comparative statistical measurement is based on calculating similarity by finding the difference between a third value and a fourth value, where the third value is obtained by accumulating the dose values ​​of a first dose in all voxels within a defined geometric volume, and the fourth value is obtained by accumulating the dose values ​​of a second dose in all voxels within a defined geometric volume, according to embodiment vi.

[0062] ix. A quality assurance device for verifying the quality of a treatment plan, wherein the treatment plan specifies the distribution of radiation and thereby delivers radiation to the planned target volume, and the quality assurance device is Processor and When executed by the processor, the quality assurance device, A process of obtaining a treatment plan calculated in a treatment planning system and a corresponding first dose, which is the predicted dose to be accumulated in the patient using the treatment plan. The process of starting the calculation of the secondary dose, which is the dose accumulated by the treatment plan, using a secondary dose calculation algorithm. A process of repeatedly calculating the confidence interval of comparative statistical measurements by comparing the first dose and the secondary dose over a defined geometric volume, and The process of suspending the calculation of the secondary dose when the confidence interval is better than at least one predetermined criterion (in which case the treatment plan is considered to have passed quality assurance). Memory that stores the command to perform the action, A quality assurance device equipped with the following features.

[0063] x. A quality assurance apparatus according to embodiment ix, wherein each voxel in the secondary dose includes an estimate of the current confidence interval for the voxel.

[0064] xi. The specified geometric volume is the planned target volume, quality assurance apparatus as described in embodiment ix or x.

[0065] xii. A quality assurance device according to embodiment ix or x, wherein the defined geometric volume is a risk organ.

[0066] xiii. A quality assurance device according to embodiment ix or x, wherein the defined geometric volume includes the planned target volume and the organ at risk.

[0067] xiv. A computer program for verifying the quality of a treatment plan, wherein the treatment plan specifies the distribution of radiation and thereby delivers radiation to the planned target volume, and the computer program, when run on a quality assurance device, A process of obtaining a treatment plan calculated in a treatment planning system and a corresponding first dose, which is the predicted dose to be accumulated in the patient using the treatment plan. The process of starting the calculation of the secondary dose, which is the dose accumulated by the treatment plan, using a secondary dose calculation algorithm. A process of repeatedly calculating the confidence interval of comparative statistical measurements by comparing the first dose and the secondary dose over a defined geometric volume, and The process of suspending the calculation of secondary doses when the confidence interval is better than at least one predetermined criterion (in which case the treatment plan is considered to have passed quality assurance). A computer program that includes computer program code to perform a certain action.

[0068] xv. A computer program according to embodiment xiv and a computer program product comprising computer-readable means in which the computer program is stored.

[0069] The aspects of this disclosure have been described above, primarily with reference to several embodiments. However, as will be readily apparent to those skilled in the art, other embodiments not disclosed above are equally possible within the scope of the invention as defined by the appended claims. Thus, although various aspects and embodiments have been disclosed herein, other aspects and embodiments will be obvious to those skilled in the art. The various aspects and embodiments disclosed herein are illustrative and not limiting, and the true scope and spirit are shown in the following claims.

Claims

1. A method for generating multiple possible treatment plans (12), wherein each treatment plan specifies the distribution of radiation to a target volume (3), and the method involves a treatment planning system (1), A step (40) for multi-criterion optimization (MCO) to generate multiple possible treatment plans (12) optimized based on a set of optimization functions, wherein at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified area of ​​the patient. A method that includes performing the following.

2. The method according to claim 1, wherein the at least one optimization function is specified as an objective.

3. The method according to claim 2, wherein the objective is to minimize or maximize the value of the at least one optimization function.

4. The method according to claim 1, wherein the at least one optimization function is specified as a constraint.

5. The method according to claim 4, wherein the constraint is that the value of the at least one optimization function shall not exceed a specified value, or the value of the at least one optimization function shall not be lower than a specified value.

6. The process (42) of providing the MCO module (11) with the aforementioned multiple possible treatment plans. The method according to any one of claims 1 to 5, further comprising:

7. A treatment planning system (1) for the process of generating multiple possible treatment plans (12), wherein each treatment plan specifies the distribution of radiation to a target volume (3), and the treatment planning system, Processor (60), When executed by the aforementioned processor, the treatment planning system (1) A step (40) for multi-criterion optimization (MCO) to generate multiple possible treatment plans (12) optimized based on a set of optimization functions, wherein at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified area of ​​the patient. A memory (64) that stores the instruction (67) to perform the action, A treatment planning system equipped with the following features.

8. The treatment planning system (1) according to claim 7, wherein the at least one optimization function is specified as an objective.

9. The treatment planning system (1) according to claim 8, wherein the objective is to minimize or maximize the value of the at least one optimization function.

10. The treatment planning system (1) according to claim 7, wherein the at least one optimization function is specified as a constraint.

11. The treatment planning system (1) according to claim 10, wherein the constraint is that the value of the at least one optimization function shall not exceed a specified value, or the value of the at least one optimization function shall not be lower than a specified value.

12. When executed by the aforementioned processor, the treatment planning system (1) The process of providing the aforementioned multiple treatment plans to the MCO module (11) A treatment planning system (1) according to any one of claims 7 to 11, further comprising an instruction (67) to perform the following:

13. A computer program (67, 91) for generating multiple possible treatment plans (12), each treatment plan specifying the distribution of radiation to a target volume (3), and when the computer program is executed on the treatment planning system (1), it provides the treatment planning system (1) with: A step (40) for multi-criterion optimization (MCO) to generate multiple possible treatment plans (12) optimized based on a set of optimization functions, wherein at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified area of ​​the patient. A computer program that includes computer program code to perform a certain action.

14. A computer-readable medium storing the computer program described in claim 13.

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