Method and apparatus for converting radiation treatment plan

Abstract

A method for converting a radiation treatment plan, according to an embodiment, may comprise the steps of: obtaining a common parameter of a first radiation treatment apparatus including treatment plan information of the first radiation treatment apparatus; determining an individual parameter of a second radiation treatment apparatus on the basis of the treatment plan information; transmitting the common parameter of the first radiation treatment apparatus and the determined individual parameter to the second radiation treatment apparatus; obtaining a first quality indicator of the first radiation treatment apparatus; obtaining a second quality indicator of the second radiation treatment apparatus; and comparing the first quality indicator with the second quality indicator to provide a result of the comparison.

Description

Radiation therapy plan switching method and device

[0001] The following embodiments relate to a method and apparatus for switching radiation therapy plans.

[0002]

[0003] Radiation therapy is a widely used method in tumor treatment that uses radiation to destroy tumor cells. To maximize the effectiveness of radiation therapy, a precise treatment plan tailored to the patient's individual anatomical structure and the location of the tumor is required. This treatment plan is primarily established based on the radiation dose distribution, beam path, and characteristics of the treatment equipment, and is provided as a customized plan for each patient.

[0004] However, there are differences in physical characteristics and dose distribution among radiation therapy devices. For example, to achieve the same effect on different devices, the treatment plan must be adjusted to suit the physical characteristics of each piece of equipment. In particular, due to differences in beam energy, output, and mechanical properties between devices, the same treatment plan may be implemented differently on different equipment. To address this issue, technology is required to determine optimized parameters for each device and automatically switch the treatment plan accordingly.

[0005] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art related to the present disclosure.

[0006]

[0007] A method for switching a radiation therapy plan according to one embodiment may include: a step of obtaining common parameters of a first radiation therapy device including treatment plan information of a first radiation therapy device; a step of determining individual parameters of a second radiation therapy device based on the treatment plan information; a step of transmitting the common parameters of the first radiation therapy device and the determined individual parameters to a second radiation therapy device; a step of obtaining a first quality indicator of the first radiation therapy device; a step of obtaining a second quality indicator of the second radiation therapy device; and a step of comparing the first quality indicator and the second quality indicator to provide a comparison result.

[0008] The step of determining the individual parameters may include the step of determining the individual parameters such that the difference between the treatment information of the second radiation therapy device and the treatment information of the first radiation therapy device is minimized.

[0009] The step of obtaining the above common parameters may include the step of obtaining at least one of the prescribed dose, optimization conditions, and clinical goals of the first radiation therapy device.

[0010] The step of determining the individual parameters may include determining at least one of the energy and beam path of the radiation beam of the second radiation therapy device based on the treatment plan information.

[0011] The step of determining the individual parameters above may include: a step of determining dosimetric characteristics including at least one of percent-depth dose, lateral profiles, and output among the physical characteristics of the second radiation therapy device; and a step of determining mechanical characteristics including at least one of a linear accelerator, helical therapy, and volumetric modulated arc therapy (VMAT).

[0012] The step of providing the above comparison result may include a step of providing a difference by comparing the dose-volume histogram (DVH) between the first quality indicator and the second quality indicator.

[0013] The step of determining the individual parameters may include the step of determining the individual parameters of the second radiation therapy device based on at least one of a learning-based auto-planning system, a correction value based on the physical or mechanical characteristics of the radiation therapy device, and a correction value based on the characteristics of the treatment plan.

[0014] The step of obtaining the above common parameters may include obtaining the above common parameters of the first radiation therapy device from the treatment planning system (TPS) of the first radiation therapy device.

[0015] The step of determining the individual parameters may include the step of determining the individual parameters that are automatically converted and optimized to suit the characteristics of the second radiation therapy device using a script within the treatment planning system of the first radiation therapy device.

[0016] An electronic device according to one embodiment may include a memory for storing at least one instruction; and a processor that, by executing the instruction stored in the memory, obtains a common parameter of the first radiation therapy device including treatment plan information of the first radiation therapy device, determines an individual parameter of the second radiation therapy device based on the treatment plan information, transmits the common parameter of the first radiation therapy device and the determined individual parameter to the second radiation therapy device, obtains a first quality indicator of the first radiation therapy device, obtains a second quality indicator of the second radiation therapy device, and compares the first quality indicator and the second quality indicator to provide a comparison result.

[0017] The processor can determine the individual parameters such that the difference between the treatment information of the second radiation therapy device and the treatment information of the first radiation therapy device is minimized.

[0018] The above processor can obtain at least one of the prescribed dose, optimization conditions, and clinical goals of the first radiation therapy device.

[0019] The processor can determine at least one of the energy and beam path of the radiation beam of the second radiation therapy device based on the treatment plan information.

[0020] The processor can determine dosing characteristics including at least one of a penetration depth dose, lateral profiles, and output among the physical characteristics of the second radiation therapy device, and determine mechanical characteristics including at least one of a linear accelerator, helical therapy, and volumetric modulated arc therapy (VMAT).

[0021] The processor can provide a difference by comparing the dose-volume histogram (DVH) between the first quality indicator and the second quality indicator.

[0022] The processor can determine individual parameters of the second radiation therapy device based on at least one of a deep learning-based auto-planning system, a correction value based on the physical or mechanical characteristics of the radiation therapy device, and a correction value based on the characteristics of the treatment plan.

[0023] The above processor can obtain the common parameters of the first radiation therapy device from the treatment planning system (TPS) of the first radiation therapy device.

[0024] The above processor can determine the individual parameters that are automatically converted and optimized to suit the characteristics of the second radiation therapy device by using a script within the treatment planning system of the first radiation therapy device.

[0025] The step of determining the individual parameters may include: extracting optimized dose distribution data of the first radiation therapy device; automatically predicting the required dose distribution of the second radiation therapy device using the auto-planning system based on the optimized dose distribution data; and adjusting the individual parameters of the second radiation therapy device based on the predicted dose distribution.

[0026]

[0027] FIG. 1 is a drawing for illustrating a radiation therapy plan switching system according to one embodiment.

[0028] FIG. 2 is a diagram illustrating a method for switching radiation therapy plans according to one embodiment.

[0029] FIG. 3 is a diagram illustrating a method for converting parameters between radiation therapy devices according to one embodiment.

[0030] FIG. 4 is a diagram illustrating the integration process of a radiation therapy planning switching system and a deep learning-based Auto-Planning system according to one embodiment.

[0031] FIG. 5 is a diagram illustrating the configuration of common parameters and equipment-specific individual parameters applied in a radiation therapy planning switching system according to one embodiment.

[0032] FIG. 6 is a diagram specifically illustrating the process of switching treatment plans between different radiation therapy devices through a radiation therapy plan switching system according to one embodiment.

[0033] FIG. 7 is a block diagram of an electronic device according to one embodiment.

[0034]

[0035] The specific structural or functional descriptions disclosed in this specification are illustrative of embodiments according to technical concepts only, and the actual implemented form may take various other forms and is not limited to the embodiments described in this specification.

[0036] Terms such as "first" or "second" may be used to describe various components, but these terms should be understood solely for the purpose of distinguishing one component from another. For example, the first component may be named the second component, and similarly, the second component may be named the first component.

[0037] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. Conversely, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. Expressions describing the relationships between components, such as "between" and "directly between," or "adjacent to" and "directly adjacent to," should be interpreted in the same way.

[0038] The singular expression includes the plural expression unless the context clearly indicates otherwise. In this specification, terms such as “comprising” or “having” are intended to specify the existence of the implemented features, numbers, steps, actions, components, parts, or combinations thereof, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0039] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this specification.

[0040] The embodiments can be implemented in various forms of products, such as personal computers, laptop computers, tablet computers, smartphones, televisions, smart home appliances, intelligent vehicles, kiosks, and wearable devices. The embodiments will be described in detail below with reference to the attached drawings. Identical reference numerals in each drawing indicate identical components.

[0041] FIG. 1 is a drawing for illustrating a radiation therapy plan switching system according to one embodiment.

[0042] Referring to FIG. 1, a first radiation therapy device (110) and a second radiation therapy device (120) according to one embodiment are each devices for performing radiation therapy, and these two devices may have different physical and mechanical characteristics. For example, if the first radiation therapy device is a linear accelerator-based device, the second radiation therapy device may be a device that operates in a manner such as helical therapy or volume modulation arc therapy (VMAT).

[0043] A radiation therapy plan switching system (100) according to one embodiment can overcome the differences between these devices and perform the role of switching the treatment plan so that the treatment plan established in the first radiation therapy device (110) can be applied as is in the second radiation therapy device (120). Individual parameters can be automatically converted and adjusted so that the treatment plan optimized in the first radiation therapy device can be implemented identically in the second radiation therapy device. Through this, the radiation therapy plan switching system (100) can enable accurate and safe switching of the radiation therapy plan during remote radiation therapy. Alternatively, the radiation therapy plan switching system (100) can enable safe and rapid switching of the radiation therapy plan to other equipment in the same hospital or equipment in another hospital in the event of a failure or abnormality of the radiation therapy equipment.

[0044] A radiation therapy plan conversion system (100) according to one embodiment can automatically perform conversion and optimization of a treatment plan through two approaches. The first approach is a script-based approach, which extracts optimization-related conditions used in an existing treatment planning system and applies them to a homogeneous or heterogeneous treatment planning system. In this approach, an existing treatment plan is converted to fit a new system by using a script that automatically converts and applies conditions according to the optimization module of each treatment planning system. A script-based system can overcome differences between systems and provide a consistent treatment plan even with different equipment. This is described in detail below with reference to FIG. 3.

[0045] The second method is a deep learning-based auto-planning method. In this method, optimized dose distribution data generated from existing treatment planning systems is utilized in a deep learning model to automatically generate an optimized plan tailored to the new treatment equipment. The deep learning-based system learns from a vast amount of existing data to help efficiently establish new treatment plans. In particular, the model trained based on existing dose distributions supports the achievement of optimal treatment results even with new radiation therapy devices. This is explained in detail below with reference to Figure 4.

[0046] The first script-based method has the advantage of being able to quickly and consistently switch optimization conditions between each treatment device, while the deep learning-based auto-planning method has the advantage of being able to automatically generate an optimized treatment plan even in new situations by learning from existing data. By combining these two methods, the present invention can overcome physical differences between radiation therapy devices and expand the automation of treatment planning.

[0047] This system enables the rapid and accurate switching of radiation therapy plans, allowing the achievement of consistent clinical objectives across various equipment. By verifying the switched treatment plans using two methods through a quality validation process and granting final approval, medical staff can provide consistent and safe radiation therapy to patients.

[0048] The first radiation therapy device (110) can transmit treatment planning information and common parameters of the device to the radiation therapy planning conversion system (100). The parameters transmitted at this time include prescribed dose, clinical goals, optimization conditions, etc., and this information includes characteristics that cannot be directly applied to the second radiation therapy device.

[0049] The radiation therapy planning switching system (100) can analyze the treatment planning information received from the first radiation therapy device and determine individual parameters (e.g., energy of the radiation beam, output, beam path) suitable for the second radiation therapy device (120). For example, if the energy of the radiation beam used in the first radiation therapy device is 6 MV, this value may be different in the second radiation therapy device, so the optimal parameters can be automatically adjusted to reflect this difference.

[0050] Additionally, the radiation therapy plan switching system (100) can compare quality indicators between the two devices to evaluate whether the switched treatment plan can achieve the same treatment goal as the original treatment plan. Quality indicators may include a dose-volume histogram (DVH) and gamma evaluation. Through this, it can be verified that the goal set in the first radiation therapy device can be accurately achieved in the second radiation therapy device as well.

[0051] FIG. 2 is a diagram illustrating a method for switching radiation therapy plans according to one embodiment. The description with reference to FIG. 1 can be applied in the same way to FIG. 2.

[0052] For convenience of explanation, steps (210 to 260) are described as being performed using the radiation therapy planning switching system (100) illustrated in FIG. 1. However, these steps (210 to 260) may be performed through any other suitable electronic device and within any suitable system.

[0053] Furthermore, the operations of FIG. 2 may be performed in the illustrated order and manner, but the order of some operations may be changed or some operations may be omitted without departing from the spirit and scope of the illustrated embodiment. Multiple operations illustrated in FIG. 2 may be performed in parallel or simultaneously.

[0054] Referring to FIG. 2, in step (210), a radiation therapy plan switching system (100) according to one embodiment may obtain common parameters of a first radiation therapy device including treatment plan information of a first radiation therapy device. The radiation therapy plan switching system (100) may obtain at least one of a prescribed dose, optimization conditions, and clinical goals of a first radiation therapy device.

[0055] In step (220), a radiation therapy planning switching system (100) according to one embodiment can determine individual parameters of a second radiation therapy device based on treatment planning information. The radiation therapy planning switching system (100) according to the embodiment can calculate optimal parameters by considering the physical characteristics of the second radiation therapy device (e.g., beam energy, output, beam path, etc.).

[0056] In step (230), the radiation therapy plan switching system (100) can transmit common parameters and determined individual parameters of the first radiation therapy device to the second radiation therapy device (120). In this process, optimized parameters tailored to each device are transmitted so that the second radiation therapy device can perform a new treatment plan.

[0057] In step (240), the radiation therapy planning switching system (100) can obtain a first quality indicator of the first radiation therapy device. This quality indicator is an indicator of the treatment results in the first radiation therapy device and may include dose distribution, DVH (Dose Volume Histogram) analysis results, etc.

[0058] In step (250), the radiation therapy plan switching system (100) can obtain a second quality indicator of the second radiation therapy device (120). This is a process of evaluating whether the switched treatment plan has been successfully applied in the second radiation therapy device. This quality indicator is used to verify the accuracy and effectiveness of the switched treatment plan.

[0059] In step (260), the radiation therapy plan switching system (100) can compare the first quality indicator and the second quality indicator and provide a comparison result. Through this comparison, it can be verified whether the treatment performance in the first radiation therapy device and the second radiation therapy device is the same, or whether the switched plan satisfies the clinical goal.

[0060] The radiation therapy planning switching system (100) can extract optimized dose distribution data of the first radiation therapy device, and based on the optimized dose distribution data, use an auto-planning system to automatically predict the required dose distribution of the second radiation therapy device, and adjust individual parameters of the second radiation therapy device based on the predicted dose distribution.

[0061] More specifically, the radiation therapy planning switching system (100) can extract optimized dose distribution data of the first radiation therapy device and, based on this, utilize an auto-planning system to establish an optimal treatment plan suitable for the second radiation therapy device. The dose distribution data used in the first radiation therapy device indicates how radiation was delivered to the patient's tumor site and surrounding tissues, and is important information for providing an optimized treatment plan.

[0062] The system (100) first extracts optimized dose distribution data from the first radiation therapy device. The extracted data is stored in a form such as a dose-volume histogram (DVH), gamma evaluation, etc., through which the detailed dose distribution of the treatment plan is verified. The dose distribution includes results optimized to ensure that accurate radiation is delivered to the treatment target site (tumor) and radiation exposure to surrounding normal tissue is minimized.

[0063] Based on the extracted dose distribution data, the auto-planning system can automatically predict the dose distribution required for the second radiation therapy device using deep learning models or machine learning algorithms. The auto-planning system learns from the data in the first device and establishes an optimized plan to achieve the same clinical goals in the second device. For example, the dose profile in the first device is automatically adjusted to match the physical characteristics (beam energy, output, mechanical properties, etc.) of the second device, and the same dose distribution can be implemented in the new device.

[0064] In this process, individual parameters of the second radiation therapy device (e.g., beam energy, beam path, output, etc.) are automatically adjusted. This is to ensure that the same treatment goal can be achieved in the second radiation therapy device as the dose distribution used in the first radiation therapy device is converted to suit the characteristics of the new equipment. For example, if the energy of the radiation beam used in the first device was 6 MV, this value may be different in the second device, so the parameters optimized for the new equipment are automatically determined.

[0065] In addition, the auto-planning system can automatically predict optimized treatment plans in various clinical scenarios based on dose distribution patterns learned from existing data using a deep learning model. In this process, radiation therapy planning data used in the past is learned, enabling the rapid generation of treatment plans tailored to new patients or equipment. The deep learning model learns the optimized dose distribution data generated from the first device and is used to automatically set the parameters necessary to achieve the same dose distribution and treatment effect in the second device.

[0066] The key feature of this system (100) is that it can provide a consistent radiation therapy plan while minimizing medical intervention during the automatic adjustment of parameters and the prediction of dose distribution. This helps overcome physical and mechanical differences between two devices and supports the achievement of the same clinical goals. In addition, by automating the optimization and conversion processes that were previously performed manually, the speed and accuracy of establishing a treatment plan can be significantly improved.

[0067] Finally, the converted treatment plan is verified through a Quality Assurance (QA) procedure. This process verifies whether the dose distribution of the treatment plans generated by the first and second devices remains identical, or whether the treatment objectives are consistently achieved. This verification process is performed using analytical tools such as Dose-Volume Histograms (DVH) and **Gamma Evaluation**, thereby ensuring the accuracy and reliability of the converted treatment plan.

[0068] In this way, the radiation therapy plan switching system (100) can automatically establish an optimized treatment plan suitable for the second device by utilizing a deep learning-based auto-planning system based on optimized dose distribution data extracted from the first radiation therapy device, thereby overcoming the difference between the two devices and providing a consistent radiation therapy effect.

[0069] FIG. 3 is a diagram illustrating a method for converting parameters between radiation therapy devices according to one embodiment. The content described with reference to FIG. 1 and FIG. 2 can be applied in the same way to FIG. 3.

[0070] For convenience of explanation, steps (310 to 350) are described as being performed using the radiation therapy planning switching system (100) illustrated in FIG. 1. However, these steps (310 to 350) may be performed through any other suitable electronic device and within any suitable system.

[0071] Furthermore, the operations of FIG. 3 may be performed in the illustrated order and manner, but the order of some operations may be changed or some operations may be omitted without departing from the spirit and scope of the illustrated embodiment. Multiple operations illustrated in FIG. 3 may be performed in parallel or simultaneously.

[0072] In step (310), the system (100) (e.g., a script of the system (100)) can perform parameter extraction to obtain treatment parameters included in the radiation therapy plan of the first radiation therapy device (110). These parameters may include the prescribed dose used in the first radiation therapy device, the intensity of the radiation beam, the angle, and optimization conditions, etc.

[0073] In step (320), the system (100) calculates the difference in physical characteristics between the equipment. In this step, criteria for appropriate conversion are established by taking into account the difference in physical characteristics between the first radiation therapy device (110) and the second radiation therapy device (120), such as beam energy, mechanical range of motion, and radiation output. This calculation is essential because the physical characteristics of the two devices are different.

[0074] In step (330), the system (100) applies an optimization algorithm to convert parameters extracted from the first device into a form suitable for the second device. This optimization algorithm may be machine learning or a rule-based algorithm developed by a radiation therapy expert. This algorithm serves to correct the difference between the two devices and generate an optimal treatment plan tailored to the second radiation therapy device.

[0075] In step (340), the system (100) transmits the converted parameters to a new device. In this step, parameters suitable for the second radiation therapy device (120) are transmitted so that the device can perform radiation therapy according to the plan.

[0076] In step (350), the system (100) verifies the quality generated by the second radiation therapy device. This verification step is a process of confirming whether the treatment plan has been successfully converted and matches the actual clinical goal. For example, it can evaluate whether the dose distribution of the treatment plan generated by the first device and the plan applied by the second device remain the same.

[0077] Through this process, parameter conversion using scripts is possible, allowing consistent treatment plans to be applied even between different radiation therapy devices.

[0078] FIG. 4 is a diagram illustrating the integration process of a radiation therapy planning switching system and a deep learning-based Auto-Planning system according to one embodiment. The description with reference to FIG. 1 to 3 can be applied in the same way to FIG. 4.

[0079] Referring to FIG. 4, a radiation therapy planning switching system (100) according to one embodiment can be combined with a deep learning-based Auto-Planning system by utilizing dose distribution data from an existing treatment planning system. Specifically, the radiation therapy planning switching system (100) utilizes dose distribution data generated from an existing treatment planning system and optimized planning data as input data.

[0080] In addition, the deep learning-based Auto-Planning system learns from vast amounts of clinical data to train a model capable of automatically generating optimized radiation therapy plans. Through this process, data-driven model training can automatically propose plans suitable for various clinical scenarios.

[0081] Meanwhile, the radiation therapy planning switching system (100) integrates this deep learning-based Auto-Planning system with the existing system to provide a consistent treatment plan between different treatment devices. This integration process is carried out by comparing and verifying existing dose distribution data with the plan generated by the deep learning system, and automatically expanding a better treatment plan based on this.

[0082] Through the expansion of automation, the radiation therapy planning switching system (100) can increase the efficiency of radiation therapy and provide a consistent and optimized treatment plan by minimizing human intervention.

[0083] FIG. 5 is a diagram illustrating the configuration of common parameters and equipment-specific individual parameters applied in a radiation therapy planning switching system according to one embodiment. The content described with reference to FIG. 1 to 4 can be applied in the same way to FIG. 5.

[0084] Referring to FIG. 5, the radiation therapy plan switching system distinguishes and processes common parameters (510) that are commonly applied between the first radiation therapy device and the second radiation therapy device, and individual parameters (520) that are set differently according to each device.

[0085] First, the equally applied parameters (510) include parameters that are applied commonly regardless of each radiation therapy device. For example, these include clinical goals of treatment or optimization constraints / objectives used when optimizing the treatment plan. These parameters play an important role in maintaining consistency between the two devices.

[0086] On the other hand, individual parameters (520) that differ for each piece of equipment are applied individually according to the physical or mechanical characteristics of each radiation therapy device. For example, physical / dose characteristics of the device include Percent-depth dose, Lateral profiles, Output, etc., and mechanical characteristics include LINAC (Linear Accelerator), Helical therapy, VMAT (Volumetric Modulated Arc Therapy), etc. These parameters are set to optimally utilize the unique performance of each piece of equipment.

[0087] According to one embodiment, individual parameters may be determined based on at least one of a deep learning-based auto-planning system, a correction value based on the physical or mechanical characteristics of a radiation therapy device, and a correction value based on the characteristics of a treatment plan.

[0088] FIG. 6 is a diagram specifically illustrating the process of switching treatment plans between different radiation therapy devices through a radiation therapy plan switching system according to one embodiment. The description with reference to FIG. 1 to 5 can be applied in the same way to FIG. 6.

[0089] Referring to FIG. 6, equipment A (610) and equipment B (620) are radiation therapy devices having different physical and mechanical characteristics, and a system for automatically processing the switching of treatment plans between the two devices is proposed by the present invention. Since equipment A (610) and equipment B (620) differ in their respective physical / dose characteristics (Percent-depth dose, Lateral profiles, Output, etc.) and mechanical characteristics (e.g., LINAC, Helical therapy, VMAT), it may be difficult to apply the same radiation therapy plan.

[0090] The radiation therapy planning conversion system (600) converts the treatment plan to overcome these physical characteristic differences and achieve the same clinical goals between the two devices. First, the system extracts common parameters from the Treatment Planning System (TPS) of device A (610). Here, the common parameters are parameters that can be applied equally across all devices, such as prescribed dose, optimization conditions, and clinical goals. For example, they include clinical goals and optimization constraints / objectives.

[0091] Afterward, the system switches the treatment plan according to the difference in physical characteristics between equipment A (610) and equipment B (620), and in this process, transmits the treatment plan using DICOM-RT files (RTPLAN, RTSTRUCTURE, RTDOSE). DICOM-RT files are a standard file format containing all data related to the radiation treatment plan, which allows parameters to be transmitted while maintaining compatibility between each piece of equipment. These files contain information about the path of the radiation beam, the dose distribution, and the target area to be irradiated.

[0092] The radiation therapy plan conversion system (600) uses a script to automatically convert individual parameters between two devices (e.g., dose distribution based on physical characteristics, beam path, etc.). During this conversion process, a treatment plan optimized for the characteristics of device B (620) is generated. For example, the dose profile from device A is automatically adjusted to the characteristics of device B. This ensures that the original treatment plan can be applied identically to the converted device.

[0093] The converted treatment plan undergoes a Quality Assurance (QA) procedure. In this quality assurance stage, it is verified whether the treatment plan established in the original equipment A (610) and the treatment plan in the converted equipment B (620) meet the same clinical objectives. Important evaluation indicators include the clinical dose and the target values ​​on the dose-volume histogram (DVH), and in particular, the planned target volume (PTV), clinical target volume (CTV), and dose distributions of organs at risk (OARs) are evaluated importantly.

[0094] During the quality verification process, Gamma Evaluation is used to quantitatively assess the differences between the two plans and verify whether those differences fall within an acceptable range. These verification results are provided as comparative indicators, enabling medical staff to confirm the quality of the switched treatment plan and grant final approval.

[0095] Ultimately, a radiation therapy plan switching system (600) according to one embodiment automatically adjusts the differences between different equipment and enables consistent radiation therapy. This system switches the treatment plan to achieve the same clinical goal by taking into account the differences between equipment A (610) and equipment B (620), thereby supporting medical staff in establishing a fast and accurate radiation therapy plan.

[0096] FIG. 7 is a block diagram of an electronic device according to one embodiment. The contents described with reference to FIG. 1 to 6 can be applied in the same way to FIG. 7.

[0097] Referring to FIG. 7, an electronic device (700) according to one embodiment may include a processor (730) and a memory (710). However, not all of the illustrated components are essential components. The electronic device (700) may be implemented with more components than illustrated, or with fewer components. For example, the electronic device (700) may further include a sensor unit.

[0098] A memory (710) according to one embodiment may store instructions that can be read by a computer. When instructions stored in the memory (710) are executed by a processor (730), the processor (730) may process operations defined by the instructions. The memory (710) may include, for example, random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), or other forms of non-volatile memory known in the art. The memory (710) may store a previously trained artificial neural network model.

[0099] One or more processors (730) according to one embodiment control the overall operation of the electronic device (700). The processor (730) may be a hardware-implemented device having a circuit having a physical structure for executing desired operations. The desired operations may include code or instructions included in a program. The hardware-implemented device may include a microprocessor, a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), a processor core, a multi-core processor, a multiprocessor, an Application-Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Neural Processing Unit (NPU), etc.

[0100] A processor (730) according to one embodiment may obtain common parameters of a first radiation therapy device including treatment plan information of a first radiation therapy device, determine individual parameters of a second radiation therapy device based on the treatment plan information, transmit the common parameters of the first radiation therapy device and the determined individual parameters to the second radiation therapy device, obtain a first quality indicator of the first radiation therapy device, obtain a second quality indicator of the second radiation therapy device, compare the first quality indicator and the second quality indicator, and provide a comparison result.

[0101] The embodiments described above may be implemented as hardware components, software components, and / or combinations of hardware and software components. For example, the devices, methods, and components described in the embodiments may be implemented using a general-purpose computer or a special-purpose computer, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0102] Software may include computer programs, code, instructions, or a combination thereof, and may configure a processing unit to operate as desired or command the processing unit independently or collectively. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave in order to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on computer-readable recording media.

[0103] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

[0104] Although the embodiments have been described above with reference to the limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, suitable results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0105] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

1. A step of obtaining common parameters of the first radiation therapy device including treatment plan information of the first radiation therapy device; A step of determining individual parameters of a second radiation therapy device based on the above treatment plan information; A step of transmitting the common parameters of the first radiation therapy device and the determined individual parameters to the second radiation therapy device; A step of obtaining a first quality indicator of the above-mentioned first radiation therapy device; A step of obtaining a second quality indicator of the second radiation therapy device; and A step of comparing the first quality indicator and the second quality indicator and providing a comparison result. A method for switching radiation therapy plans including 2. In Paragraph 1, The step of determining the above individual parameters is A step of determining the individual parameters such that the difference between the treatment information of the second radiation therapy device and the treatment information of the first radiation therapy device is minimized A method for switching radiation therapy plans, including 3. In Paragraph 1, The step of obtaining the above common parameters A step of obtaining at least one of the prescribed dose, optimization conditions, and clinical goals of the first radiation therapy device. A method for switching radiation therapy plans, including 4. In Paragraph 1, The step of determining the above individual parameters is A step of determining at least one of the energy and beam path of the radiation beam of the second radiation therapy device based on the above treatment plan information A method for switching radiation therapy plans, including 5. In Paragraph 1, The step of determining the above individual parameters is A step of determining dosimetric characteristics including at least one of a penetration depth dose, lateral profiles, and output among the physical characteristics of the second radiation therapy device; and A step of determining mechanical properties including at least one of a linear accelerator, helical therapy, and volumetric modulated arc therapy (VMAT). A method for switching radiation therapy plans, including 6. In Paragraph 1, The step of providing the above comparison results A step of providing a difference by comparing the dose-volume histogram (DVH) between the first quality indicator and the second quality indicator. A method for switching radiation therapy plans, including 7. In Paragraph 1, The step of determining the above individual parameters is A step of determining individual parameters of the second radiation therapy device based on at least one of a deep learning-based auto-planning system, a correction value according to the physical or mechanical characteristics of the radiation therapy device, and a correction value according to the characteristics of the treatment plan. A method for switching radiation therapy plans, including 8. In Paragraph 1, The step of obtaining the above common parameters A step of obtaining the common parameters of the first radiation therapy device from the treatment planning system (TPS) of the first radiation therapy device. A method for switching radiation therapy plans, including 9. In Paragraph 1, The step of determining the above individual parameters is A step of determining the individual parameters that are automatically converted and optimized to suit the characteristics of the second radiation therapy device using a script within the treatment planning system of the first radiation therapy device. A method for switching radiation therapy plans, including 10. A computer program stored on a medium in combination with hardware to execute the method of any one of claims 1 through 9.

11. Memory storing at least one instruction; and By executing the instruction stored in the above memory, Acquiring common parameters of the first radiation therapy device including treatment plan information of the first radiation therapy device, and Based on the above treatment plan information, individual parameters of the second radiation therapy device are determined, and The common parameters of the first radiation therapy device and the determined individual parameters are transmitted to the second radiation therapy device, and Acquiring the first quality indicator of the above-mentioned first radiation therapy device, and Acquiring the second quality indicator of the above-mentioned second radiation therapy device, A processor that compares the first quality indicator and the second quality indicator and provides a comparison result An electronic device including 12. In Paragraph 11, The above processor An electronic device that determines the individual parameters such that the difference between the treatment information of the second radiation therapy device and the treatment information of the first radiation therapy device is minimized.

13. In Paragraph 11, The above processor An electronic device for obtaining at least one of the prescribed dose, optimization conditions, and clinical goals of the first radiation therapy device.

14. In Paragraph 11, The above processor An electronic device that determines at least one of the energy and beam path of the radiation beam of the second radiation therapy device based on the above treatment plan information.

15. In Paragraph 11, The above processor Determining dostic characteristics including at least one of the penetration depth dose, lateral profiles, and output among the physical characteristics of the second radiation therapy device, and An electronic device that determines mechanical properties, comprising at least one of a linear accelerator, helical therapy, and volumetric modulated arc therapy (VMAT).

16. In Paragraph 11, The above processor An electronic device that provides a difference by comparing the dose-volume histogram (DVH) between the first quality indicator and the second quality indicator.

17. In Paragraph 11, The above processor An electronic device that determines individual parameters of a second radiation therapy device based on at least one of a deep learning-based auto-planning system, a correction value based on the physical or mechanical characteristics of the radiation therapy device, and a correction value based on the characteristics of the treatment plan.

18. In Paragraph 11, The above processor An electronic device that obtains the common parameters of the first radiation therapy device from the treatment planning system (TPS) of the first radiation therapy device.

19. In Paragraph 11, The above processor An electronic device that determines the individual parameters automatically converted and optimized to suit the characteristics of the second radiation therapy device using a script within the treatment planning system of the first radiation therapy device.

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