A decision-making support system for improving radiotherapy effects

Abstract

The present invention discloses an auxiliary decision-making system for improving the effect of radiotherapy, and relates to the field of radiotherapy technology. It includes an image data collection module, an analysis module, an initial radiotherapy dose distribution map generation submodule, a dose acquisition submodule, a dose optimization submodule, a radiotherapy mode formulation module, a tumor position determination submodule, a normal tissue position determination submodule, a distance calculation submodule, an optimal dose value calculation submodule, and a radiotherapy mode determination submodule. Based on the existing system generating a dose distribution map, the dose value is optimized to obtain an optimized dose. At the same time, the patient's tumor position and the distance between the tumor and the adjacent normal tissue are analyzed to calculate the optimal dose. Dose optimization ensures that the tumor area is effectively irradiated, while minimizing the damage to the surrounding normal tissues, realizing a dynamic adjustment mechanism, and ensuring that the accuracy of treatment is improved while protecting key organs.

Description

Technical Field

[0001] The present invention relates to the technical field of radiotherapy, and in particular to an auxiliary decision-making system for improving radiotherapy effects. Background Art

[0002] In modern medicine, radiotherapy is one of the important means of treating malignant tumors. However, how to accurately control the radiotherapy dose to ensure that the tumor area is effectively irradiated while minimizing the impact on surrounding healthy tissues has always been a difficult problem in clinical practice. Traditional radiotherapy planning relies on the doctor's experience and manual operation. This method is not only time-consuming and laborious, but also due to the lack of individualized considerations, it is often difficult to achieve the ideal treatment effect. In recent years, with the rapid development of artificial intelligence technology, especially the application of deep learning algorithms, a new way to solve the above problems has been provided. In the existing technology, an artificial intelligence-assisted decision-making system that combines individual patient information with historical case data has been proposed and applied in clinical practice. The workflow of the system is as follows:

[0003] 1. Data collection: First, collect basic information such as the patient's imaging data (such as CT, MRI images), pathology reports, and medical history. These data form the basis of system input.

[0004] 2. Deep learning analysis: The collected data is input into a pre-trained deep neural network, which has been trained on a large number of historical cases and has the ability to identify different tumor characteristics and recommend corresponding treatment plans.

[0005] 3. Personalized treatment recommendations: The deep neural network selects the most appropriate radiotherapy plan from existing treatment models based on the patient's specific situation and provides detailed dose distribution recommendations.

[0006] 4. Doctor decision support: Ultimately, doctors develop personalized radiotherapy plans based on the recommendations provided by the system and their own professional knowledge and experience. This process not only improves the accuracy of treatment, but also significantly shortens the time for planning.

[0007] Although the existing technology has improved the efficiency and effectiveness of radiotherapy to a certain extent, there are still some shortcomings in the actual application process: the existing technology predicts a dose distribution map through deep learning. According to the dose value recommended by the dose distribution map and combined with the pathological conditions of the patient and the tumor, the doctor makes appropriate adjustments to the dose value based on his own diagnosis and treatment experience to generate a radiotherapy plan. Different doctors have different experiences and judgment criteria, which may affect the consistency and accuracy of the treatment plan. Moreover, the generated dose distribution map is static and cannot be dynamically adjusted according to patient response and treatment progress. There is a lack of a mechanism for real-time adjustment according to actual conditions, which affects the treatment effect and the radiotherapy effect is not obvious. Summary of the invention

[0008] The purpose of the present invention is to provide an auxiliary decision-making system for improving the effect of radiotherapy, so as to solve the problem that the dose value cannot be adjusted in real time.

[0009] To achieve the above object, the present invention provides the following technical solution: an auxiliary decision-making system for improving radiotherapy effect, comprising the following modules:

[0010] Image data collection module: used to collect patient image data;

[0011] Analysis module: connected to the image data collection module, used to analyze the image data using a deep neural network to obtain the tumor area and normal tissue area;

[0012] Radiotherapy dose distribution map generation module: connected with the analysis module, used to generate a radiotherapy dose optimization distribution map according to the segmentation results;

[0013] The radiotherapy dose optimization distribution map generation module includes the following submodules:

[0014] Initial radiotherapy dose distribution map generation submodule: used to generate initial radiotherapy dose distribution map;

[0015] Dose acquisition submodule: used to obtain dose contributions in tumor areas and normal tissue areas;

[0016] Dose optimization submodule: used to determine the optimized dose based on dose contribution;

[0017] Radiotherapy mode formulation module: connected to the radiotherapy dose distribution map generation module, used to formulate radiotherapy mode;

[0018] The radiotherapy mode formulation module includes the following sub-modules:

[0019] Tumor location determination submodule: used to determine the tumor location;

[0020] Normal tissue position determination submodule: used to determine the normal tissue position;

[0021] Distance calculation submodule: used to calculate the distance between the tumor location and the normal tissue location;

[0022] Optimal dose value calculation submodule: used to calculate the optimal dose value according to the distance between the tumor position and the normal tissue position;

[0023] Radiotherapy mode determination submodule: used to determine the radiotherapy mode according to the optimal dose value.

[0024] In another embodiment, an expected dose and a peak dose are constructed for a tumor region;

[0025] An optimization function is constructed based on the dose contribution in the tumor area, the dose contribution in the normal tissue area, the expected dose, and the peak dose. The dose in the initial radiotherapy dose distribution map is adjusted to the optimized dose through an iterative optimization algorithm.

[0026] In another embodiment, the optimization function is:

[0027] ;

[0028] Where F is the optimization function, is the dose contribution within the tumor region, is the dose contribution to the normal tissue region, The expected dose, is the peak dose, , are weight coefficients respectively.

[0029] In another embodiment, the tumor location includes: the center coordinates and radius of the tumor region.

[0030] In another embodiment, the normal tissue position is the coordinates of the boundary pixel points of the normal tissue area.

[0031] In another embodiment, the optimal dosage value is calculated as follows:

[0032] ;

[0033] In the formula, Pixels in the tumor area The optimal dose value, Pixels in the tumor area The optimal dose value, k is the adjustment factor, is the center coordinate of the tumor area, and R is the radius of the tumor area.

[0034] In another embodiment, a safety distance is preset;

[0035] The adjacent normal tissue area around the tumor area is obtained, the distance between the center coordinates of the tumor area and the coordinates of the boundary pixel points of the adjacent normal tissue area is calculated, and the minimum distance is screened out from the distances; based on the minimum distance and the safety distance, the adjustment factor is dynamically adjusted: when the minimum distance is less than the safety distance, the adjustment factor is reduced; when the minimum distance is greater than or equal to the safety distance, the adjustment factor is increased.

[0036] In another embodiment, the minimum distance is:

[0037] ;

[0038] In the formula, is the minimum distance, min is the minimum value operation, are the coordinates of the boundary pixels adjacent to the normal tissue area, and O is the boundary of the normal tissue area.

[0039] In another embodiment, the adjustment factor is:

[0040] ;

[0041] ;

[0042] In the formula, is the adjustment factor when the minimum distance is less than the safety distance, is the adjustment factor when the minimum distance is greater than or equal to the safety distance, For a safe distance, is the baseline value.

[0043] Technical effects and advantages of the present invention:

[0044] The present invention optimizes the dose value to obtain the optimized dose based on the dose distribution map generated by the existing system. At the same time, the patient's tumor position and the distance between the tumor and the adjacent normal tissue are analyzed to calculate the optimal dose. In this way, the present invention can significantly improve the radiotherapy effect, which is specifically reflected in the following aspects: First, dose optimization ensures that the tumor area is effectively irradiated while minimizing damage to surrounding normal tissues; secondly, the dose distribution is adjusted in real time according to the distance between the tumor and normal tissue to realize a dynamic adjustment mechanism, ensuring that the accuracy of treatment is improved while protecting key organs; in addition, the automated process greatly reduces the need for manual intervention and improves the efficiency and consistency of treatment planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of the structure of an auxiliary decision-making system for improving radiotherapy effects provided in this embodiment;

[0046] Figure 2 A structural block diagram of a radiotherapy dose optimization distribution map generation module of an auxiliary decision system for improving radiotherapy effects provided in this embodiment;

[0047] Figure 3 This is a structural block diagram of a radiotherapy mode formulation module of an auxiliary decision system for improving radiotherapy effects provided in this embodiment. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clarify the technical solutions in the embodiments of the present invention; it is clearly described in detail. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The present invention proposes an auxiliary decision-making system for improving the effect of radiotherapy. By integrating multiple modules such as image data collection, deep learning analysis, dose distribution map generation and radiotherapy mode formulation, high-precision tumor segmentation and personalized dose optimization are achieved. The system can effectively handle complex tumor structures, reduce the impact on surrounding important organs, and significantly improve the efficiency and effect of radiotherapy. By dynamically adjusting the dose value in the dose distribution map, it is ensured that the tumor area receives sufficient dose while protecting the surrounding normal tissues, thereby providing a more accurate and effective radiotherapy auxiliary decision-making tool.

[0050] like Figure 1 As shown, this embodiment discloses an auxiliary decision system for improving radiotherapy effect, including the following modules:

[0051] Image data collection module: used to collect patient image data.

[0052] Image data include CT images and MRI images. Extract the patient's CT images and MRI images from the hospital information system to ensure that the image format is unified (such as DICOM format). De-noise the image to remove noise interference and normalize the image to ensure that the pixel value is within a unified range. Perform image registration to ensure spatial consistency between images of different modalities. Store the pre-processed images in the database for easy access by subsequent modules.

[0053] Analysis module: connected to the image data collection module, used to analyze the image data using a deep neural network to obtain the tumor area and normal tissue area.

[0054] Load preprocessed CT images and MRI images from the database. Initialize a deep learning-based convolutional neural network (CNN), such as the U-Net architecture. Load pretrained weights to accelerate convergence and improve segmentation accuracy. Use convolutional layers and pooling layers to extract low-level to high-level features of the image step by step. Apply attention mechanisms at each level to enhance the feature representation of important areas. Use multi-scale feature fusion modules to integrate feature information at different levels, merge high-level and low-level features, and improve segmentation details and accuracy. Input the image into the deep neural network to generate preliminary segmentation results. The output result is a binary mask that identifies the tumor area. Apply morphological operations (such as dilation and erosion) to optimize the segmentation results, perform boundary smoothing, reduce the jagged effect, and display the final segmentation results for use by the subsequent dose distribution map generation module.

[0055] like Figure 2 As shown, the radiotherapy dose distribution map generation module is connected to the analysis module and is used to generate a radiotherapy dose optimization distribution map based on the segmentation results.

[0056] It includes three submodules: initial radiotherapy dose distribution map generation submodule, dose acquisition submodule and dose optimization submodule. These submodules generate high-precision radiotherapy dose distribution maps to ensure that the tumor area is effectively irradiated while minimizing damage to surrounding normal tissues.

[0057] The radiotherapy dose optimization distribution map generation module includes the following submodules:

[0058] Initial radiotherapy dose distribution map generation submodule: used to generate the initial radiotherapy dose distribution map.

[0059] Load high-precision tumor segmentation results from the analysis module and set the target dose and maximum allowable dose. These parameters can be set according to clinical standards. Use a pre-trained deep learning system (such as U-Net) to generate an initial dose distribution map: the input includes the tumor segmentation results and the normal tissue segmentation results, and the output is the initial dose distribution map. The initial dose distribution map shows the dose value of each pixel.

[0060] Dose acquisition submodule: used to obtain dose contributions in tumor areas and normal tissue areas.

[0061] ;

[0062] ;

[0063] In the formula, is the dose contribution within the tumor region, is the dose contribution to the normal tissue region, is the pixel point in the tumor area, is the pixel point in the normal tissue area, T is the tumor area, and N is the normal tissue area.

[0064] Dose optimization submodule: used to determine the optimized dose based on dose contribution.

[0065] Construct the expected dose and peak dose for the tumor area; construct an optimization function based on the dose contribution in the tumor area, the dose contribution in the normal tissue area, the expected dose, and the peak dose to balance the dose in the tumor area and the dose in the normal tissue. Adjust the dose in the initial radiotherapy dose distribution map to the optimized dose through an iterative optimization algorithm.

[0066] Further, the dose values ​​in the dose distribution map are adjusted using a gradient descent method or other optimization algorithms, with the goal of finding a set of dose values ​​in the dose distribution map that minimizes the entire optimization function and obtains an optimized dose. The dose distribution map is updated according to the optimized dose values ​​to obtain an optimized dose distribution map.

[0067] ;

[0068] Where F is the optimization function, is the dose contribution within the tumor region, is the dose contribution to the normal tissue region, is the target dose, is the peak dose, , are weight coefficients respectively.

[0069] Ensure that the dose contribution of the tumor area is close to the target dose. When the dose contribution is close to the target dose, this item is close to 0, indicating that the tumor area has received an appropriate dose.

[0070] Ensure that the dose contribution of surrounding normal tissue does not exceed the maximum permissible dose. , which is close to 0, indicating that the surrounding normal tissues are not exposed to excessive radiation.

[0071] Radiotherapy mode formulation module: connected to the radiotherapy dose distribution map generation module, used to formulate radiotherapy mode;

[0072] like Figure 3 As shown, the radiotherapy mode formulation module includes the following sub-modules:

[0073] Tumor location determination submodule; used to determine the tumor location.

[0074] The tumor location includes: the center coordinates of the tumor area and radius R.

[0075] , ; .

[0076] i is the x-coordinate of the pixel point in the tumor area, j is the y-coordinate of the pixel point in the tumor area, is the number of pixels in the tumor area.

[0077] Normal tissue position determination submodule: used to determine the normal tissue position.

[0078] The normal tissue position is the coordinates of the boundary pixel points of the normal tissue area, which are obtained through edge detection algorithm.

[0079] Distance calculation submodule: used to calculate the distance between the tumor location and the normal tissue location.

[0080] The distance between the center coordinates of the tumor area and the coordinates of the boundary pixels of the adjacent normal tissue area is calculated, and the minimum distance is selected from the distances. The purpose is to select the normal tissue closest to the tumor to ensure that the radiotherapy process does not inadvertently cause damage to key organs. The normal tissue selected by the minimum distance is preferably the heart, lungs and other key organs.

[0081] ;

[0082] In the formula, is the minimum distance, min is the minimum value operation, are the coordinates of the boundary pixels adjacent to the normal tissue area, and O is the boundary of the normal tissue area.

[0083] Optimal dose value calculation submodule: used to calculate the optimal dose value according to the distance between the tumor position and the normal tissue position;

[0084] The optimal dose value is calculated as follows:

[0085] ;

[0086] In the formula, Pixels in the tumor area The optimal dose value, Pixels in the tumor area The optimal dose value, k is the adjustment factor, is the center coordinate of the tumor area, and R is the radius of the tumor area.

[0087] Furthermore, the present embodiment preferably selects the heart and lungs as key organs, which also need to be protected in the present embodiment, to avoid overdose of key organs during radiotherapy. Therefore, a safety distance is preset; the safety distance of each important organ is determined with reference to medical literature and clinical standards, the safety distance for the heart is 30 mm, and the safety distance for the lungs is 20 mm.

[0088] The adjustment factor is dynamically adjusted based on the minimum distance and the safety distance: when the minimum distance is less than the safety distance, the adjustment factor is reduced to reduce the dose to critical organs; when the minimum distance is greater than or equal to the safety distance, the adjustment factor is increased to increase the dose to the tumor.

[0089] ;

[0090] ;

[0091] In the formula, is the adjustment factor when the minimum distance is less than the safety distance, is the adjustment factor when the minimum distance is greater than or equal to the safety distance, For a safe distance, is the baseline value.

[0092] Using the exponential function The adjustment factor can be reduced rapidly as the distance decreases, thereby effectively protecting key organs. The adjustment factor can be gradually increased as the distance increases, thereby improving the radiotherapy effect.

[0093] Benchmark value It is the basic value of the adjustment factor, which is usually set to a reasonable initial value, referring to medical literature and clinical standards. .

[0094] This embodiment calculates the optimal dose value corresponding to the important organs based on the important organs, and generates the optimal dose distribution map corresponding to the important organs based on the optimal dose value.

[0095] Radiotherapy mode determination submodule: used to determine the radiotherapy mode according to the optimal dose value.

[0096] Determine the beam angle, dose rate, number of irradiations and other parameters based on the optimal dose distribution map. Use inverse planning technology to generate a specific radiotherapy plan based on the optimal dose distribution map. Input the optimal dose distribution map and use an optimization algorithm (such as gradient descent method or other optimization algorithms) to determine the beam angle, dose rate, number of irradiations and other parameters. For example:

[0097] Multi-angle irradiation is achieved using a multi-leaf collimator (MLC) and a rotating gantry. Multiple beam angles (such as 6 or more) are usually selected to cover the entire tumor area. The dose rate is set according to the capabilities of the treatment equipment and the specific conditions of the patient. For example, the dose rate is 1 Gy / min. The number of irradiations is determined based on the total treatment dose and the single irradiation dose. For example, if the total treatment dose is 60 Gy and the single irradiation dose is 2 Gy, the number of irradiations is 30.

[0098] For example, the tumor in this embodiment occurs in the chest, and the heart and lungs need to be protected. The following is a specific radiotherapy plan:

[0099] Beam Angle: Six beam angles are used: 0°, 60°, 120°, 180°, 240°, 300°; each angle covers the entire tumor area and avoids direct irradiation to the heart and lungs.

[0100] Dose rate: 1 Gy / min.

[0101] Irradiation times: The total treatment dose is 60 Gy, and the single irradiation dose is 2 Gy, so the number of irradiation times is 30. Irradiation is performed 5 days a week, 2 Gy each time, and the entire treatment is completed in 6 weeks. A three-dimensional dose distribution map is generated to show the high-dose area of ​​the tumor area and the low-dose area of ​​the normal tissue.

[0102] This embodiment also generates a detailed report to help doctors understand the following:

[0103] Distance between tumor and vital organs: Clarify the actual distance between the tumor and key organs.

[0104] Adjustment Factor: Displays the dynamically adjusted adjustment factor to help doctors understand the reasons and extent of dosage adjustment.

[0105] Optimal dose distribution map: Visually display the optimal dose distribution and intuitively present the distribution of dose in different areas.

[0106] Recommended radiotherapy mode: The optimal beam angle, dose rate, number of irradiations and other parameters are recommended for the doctor's reference.

[0107] Risk assessment: Analyze potential risks and side effects and provide preventive measures and monitoring plans.

[0108] The radiotherapy mode determination submodule loads the segmentation results and the optimal dose value, calculates the minimum distance and dynamically adjusts the adjustment factor to finally determine the optimal dose value. The radiotherapy mode is determined according to the optimal dose value, including parameters such as beam angle, dose rate, and number of irradiations. The specific radiotherapy plan includes multiple beam angles, reasonable dose rates, and number of irradiations, and a detailed radiotherapy plan is generated by combining inverse planning technology. These measures assist doctors in making radiotherapy decisions, enhance radiotherapy response, improve radiotherapy results, and reduce the harm of radiotherapy to patients.

Claims

1. An auxiliary decision-making system for improving radiotherapy effects, characterized in that: Includes the following modules: Image data collection module: used to collect patient image data; Analysis module: connected to the image data collection module, used to analyze the image data using a deep neural network to obtain the tumor area and normal tissue area; Radiotherapy dose distribution map generation module: connected with the analysis module, used to generate a radiotherapy dose optimization distribution map according to the segmentation results; The radiotherapy dose optimization distribution map generation module includes the following submodules: Initial radiotherapy dose distribution map generation submodule: used to generate initial radiotherapy dose distribution map; Dose acquisition submodule: used to obtain dose contributions in tumor areas and normal tissue areas; Dose optimization submodule: used to determine the optimized dose based on dose contribution; Construct expected dose and peak dose for the tumor region; According to the dose contribution in the tumor area, the dose contribution in the normal tissue area, the expected dose, and the peak dose, an optimization function is constructed, and the dose in the initial radiotherapy dose distribution map is adjusted to the optimized dose through an iterative optimization algorithm; The optimization function is: ； Where F is the optimization function, is the dose contribution within the tumor region, is the dose contribution to the normal tissue region, is the expected dose, is the peak dose, , are weight coefficients respectively; Radiotherapy mode formulation module: connected to the radiotherapy dose distribution map generation module, used to formulate radiotherapy mode; The radiotherapy mode formulation module includes the following sub-modules: Tumor location determination submodule: used to determine the tumor location; Normal tissue position determination submodule: used to determine the normal tissue position; Distance calculation submodule: used to calculate the distance between the tumor location and the normal tissue location; Optimal dose value calculation submodule: used to calculate the optimal dose value according to the distance between the tumor position and the normal tissue position; The optimal dose value is calculated as follows: ； In the formula, Pixels in the tumor area The optimal dose value, Pixels in the tumor area The optimal dose value, k is the adjustment factor, is the center coordinate of the tumor area, and R is the radius of the tumor area; Radiotherapy mode determination submodule: used to determine the radiotherapy mode according to the optimal dose value.

2. The auxiliary decision-making system for improving radiotherapy effect according to claim 1, characterized in that: The tumor position includes: the center coordinates and radius of the tumor area.

3. The auxiliary decision-making system for improving radiotherapy effect according to claim 1, characterized in that: The normal tissue position is the coordinates of the boundary pixel points of the normal tissue area.

4. The auxiliary decision-making system for improving radiotherapy effect according to claim 1, characterized in that: Preset safety distance; The adjacent normal tissue area around the tumor area is obtained, the distance between the center coordinates of the tumor area and the coordinates of the boundary pixel points of the adjacent normal tissue area is calculated, and the minimum distance is screened out from the distances; based on the minimum distance and the safety distance, the adjustment factor is dynamically adjusted: when the minimum distance is less than the safety distance, the adjustment factor is reduced; when the minimum distance is greater than or equal to the safety distance, the adjustment factor is increased.

5. The auxiliary decision-making system for improving radiotherapy effect according to claim 4, characterized in that: The minimum distance is: ； In the formula, is the minimum distance, min is the minimum value operation, are the coordinates of the boundary pixels adjacent to the normal tissue area, and O is the boundary of the normal tissue area.

6. The auxiliary decision-making system for improving radiotherapy effect according to claim 5, characterized in that: The adjustment factor is: ； ； In the formula, is the adjustment factor when the minimum distance is less than the safety distance, is the adjustment factor when the minimum distance is greater than or equal to the safety distance, For a safe distance, is the benchmark value.

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