Dose radiotherapy optimization system suitable for subtotal liver diffuse tumor thrombus liver cancer

Abstract

Provided is a dose radiotherapy optimization system suitable for subtotal liver diffuse tumor thrombus liver cancer. The system consists of the following modules: a data acquisition module; an image analysis module; an image processing module; a solution optimization module. The data acquisition module is used for acquiring original radiotherapy plan data, the image analysis module is used for obtaining original radiation dose distribution data from an original radiotherapy plan image, the image processing module is used for performing image processing on an original medical image to obtain a target medical image, and the solution optimization module is used for optimizing the original radiation dose distribution data by combining a plan optimization model with the target medical image, to obtain a radiation dose distribution optimization result. The system enables a radiation dose line of a radiotherapy plan to be more densely distributed and reasonable, and to satisfy actual application requirements, so as to solve the current international problem that radiotherapy cannot be performed on subtotal liver diffuse tumor thrombus liver cancer by means of existing radiotherapy plans.

Description

Optimized system for medium-dose radiotherapy of liver cancer with diffuse tumor thrombus in subtotal liver Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a medium-dose radiotherapy optimization system suitable for treating subtotal liver diffuse tumor thrombus liver cancer. Background Art

[0002] Radiation therapy is one of the most important cancer treatments. For inoperable liver cancer, it is the most effective local treatment. The implementation of radiation therapy relies on the radiation treatment plan, specifically the planning of the radiation treatment area and dose distribution. The rationality of the radiation treatment plan is crucial for both treatment effectiveness and safety. Currently, radiation treatment plan design relies on commercial software, with physicists manually adjusting and optimizing parameters and structures to optimize the radiation treatment plan.

[0003] However, existing methods for generating radiotherapy plans rely on the experience of physicists and are susceptible to subjective influences, resulting in low efficiency in generating optimized radiotherapy plans. Furthermore, they are unable to generate targeted radiotherapy plans when faced with specific cancer conditions.

[0004] When faced with diffuse liver cancer with tumor thrombi that involves nearly the entire liver (or subtotal liver), as shown in Figure 1, there is currently a lack of effective treatment options. Commonly used treatments are extremely ineffective. For example, targeted therapy can easily cause upper gastrointestinal bleeding and jaundice in such patients, while single-agent immunotherapy has an efficacy rate of only 10-15%. Interventional therapy is prone to liver failure and has an efficacy rate of less than 20%. Liver cancer is sensitive to radiotherapy, but previously, patients with a liver volume (normal residual liver volume, Liver-GTV) less than 700ml and a radiotherapy volume less than 300ml below 5Gy could not receive radiotherapy. Radiotherapy is highly effective for diffuse liver cancer with tumor thrombi. If a reasonable treatment plan can be designed and optimized and safely implemented, it can significantly improve treatment efficacy and prolong patient survival. However, the radiation dose lines of radiotherapy plans generated using traditional methods are too sparse, far from meeting the international guidelines for liver radiotherapy dose limits, and cannot be used in real-world scenarios.

[0005] Summary of the Invention

[0006] In response to the above-mentioned limitations, the present invention proposes a medium-dose radiotherapy optimization system for liver cancer with diffuse tumor thrombus formation in subtotal liver. The system is applied to situations where the normal residual liver volume area (Liver-GTV) is less than 700 ml and the volume for radiotherapy below 500 cGy is less than 300 ml. The system optimizes radiotherapy plans through a deep learning algorithm model, and through innovative dose limitation conditions and dose irradiation patterns, improves the usability of radiotherapy plans generated by commercial software for patients with liver cancer with diffuse tumor thrombus formation in subtotal liver and invading the left and right lobes of the liver.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A medium-dose radiotherapy optimization system for subtotal liver diffuse tumor thrombus liver cancer, the system comprising the following modules:

[0009] Data acquisition module; image analysis module; image processing module; solution optimization module;

[0010] The data acquisition module is used to acquire the target radiotherapy plan and obtain original radiotherapy plan data; the original radiotherapy plan data includes original radiotherapy plan images and original medical images;

[0011] The image analysis module is used to obtain original radiotherapy dose distribution data from the original radiotherapy plan image, and to perform key area detection on the target medical image to obtain core target area distribution data;

[0012] The image processing module is used to perform image processing on the original medical image to obtain the target medical image;

[0013] The scheme optimization module is used to optimize the original radiotherapy dose distribution data with the help of the plan optimization model in combination with the target medical image, obtain the radiation dose distribution optimization result, and generate the radiotherapy dose scheme.

[0014] The system implements the radiotherapy plan optimization function according to the following steps:

[0015] Step 1: The data acquisition module obtains and reads the original radiation plan data to obtain the original radiation therapy plan data;

[0016] Step 2: The image analysis module performs radiation dose distribution detection on the original radiotherapy plan image to obtain original radiation dose distribution data;

[0017] Step 3: The image processing module performs image enhancement on the original medical image to obtain the target medical image;

[0018] Step 4: The image analysis module performs key area detection on the target medical image to obtain core target area distribution data and normal residual liver area distribution data;

[0019] The core target area includes the GTV area, the PGTV area and the GTVboost area;

[0020] The core target area distribution data includes GTV distribution data, PGTV distribution data and GTVboost distribution area;

[0021] Step 5: The target medical image, original radiation dose distribution data, normal residual liver area distribution data, and core target area distribution data are input into the plan optimization module, and the radiation dose distribution optimization result is obtained with the help of the plan optimization model;

[0022] The planning optimization model is a model obtained by training with a deep learning algorithm.

[0023] Furthermore, the radiation dose distribution optimization result has the following characteristics:

[0024] The normal remaining liver area irradiated with less than 500 cGy has a volume greater than 100 ml;

[0025] At least 95% of the volume of GTVboost received a total dose of 30 Gy or 35 Gy;

[0026] At least 95% of the GTV volume received a total dose of 25 Gy;

[0027] At least 95% of the PGTV volume received a total dose product of 20 Gy.

[0028] Compared with the prior art, the present invention has the following advantages:

[0029] Compared with directly using existing commercial software to design radiotherapy plans, the present invention optimizes radiotherapy plans using a deep learning algorithm model, making the radiotherapy dose lines of the radiotherapy plan denser and more reasonably distributed, meeting the requirements of practical safety application. In addition, for specific tumor conditions, namely diffuse tumor thrombus liver cancer invading the left and right lobes of the liver, the method of the present invention can generate a more targeted radiotherapy plan, ensuring that the normal remaining liver area is irradiated with a volume greater than 100 ml with a dose of less than 500 cGy, ensuring that at least 95% of the volume of the core target GTVboost reaches a dose of 30 Gy / 6 Gy / 5f or 35 Gy / 7 Gy / 5f, at least 95% of the volume of the GTV reaches a dose of 25 Gy / 5 Gy / 5f, and at least 95% of the volume of the PGTV reaches a dose of 20 Gy / 4 Gy / 5f. This ensures that the radiotherapy plan can be safely implemented in clinical practice and safely combined with systemic treatment.

[0030] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] FIG1 is a schematic diagram of a subtotal liver diffuse tumor thrombus liver cancer provided by an embodiment of the present invention.

[0032] FIG2 is a structural diagram of a medium-dose radiotherapy optimization system for subtotal liver diffuse tumor thrombus liver cancer provided by an embodiment of the present invention.

[0033] FIG3 is an example diagram of an original radiotherapy planning image provided by an embodiment of the present invention.

[0034] FIG4 is a flow chart of a medium-dose radiotherapy optimization system for subtotal liver diffuse tumor thrombus liver cancer provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following specific embodiments illustrate the embodiments of the present invention. Those skilled in the art can easily understand the other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. In order to further understand the present invention, the present invention is further described in detail below in conjunction with the best embodiment.

[0036] The following are explanations and definitions of terms involved in the embodiments of the present invention:

[0037] GTV: tumor area, which simulates the primary lesion of liver cancer and the tumor thrombus area with diffuse tumor thrombus in the subtotal liver shown by CT and MR images.

[0038] PGTV: To account for positioning errors and organ motion, the tumor planning area is defined as the three-dimensional expansion of the GTV. Specifically, the PGTV is an area that is 0.5 cm to 1.5 cm in the head-to-foot direction based on the respiratory motion amplitude shown on the 4D-CT image, and 0.5 cm in all other directions.

[0039] GTVboost is the hypoxic area within the tumor, defined as a 1 cm three-dimensional GTV retraction or a solid tumor area with strong activity shown on MR images.

[0040] Core target area: including GTV, PGTV and GTVboost areas.

[0041] The following describes the implementation of the present invention in detail with reference to the aforementioned terms:

[0042] The present invention provides a medium-dose radiotherapy optimization system for subtotal liver diffuse tumor thrombus liver cancer. Referring to FIG2 , the system is composed of the following modules:

[0043] Data acquisition module; image analysis module; image processing module; solution optimization module; system control module.

[0044] The data acquisition module is used to acquire the radiation plan and read the data to obtain original radiation therapy plan data; the original radiation therapy plan data includes original radiation therapy plan images and original medical images.

[0045] The image analysis module is used to obtain original radiation dose distribution data from the original radiation therapy plan image of the original radiation therapy plan data, and to obtain core target area distribution data by performing key area detection on the target medical image;

[0046] The original radiotherapy plan image is shown in reference FIG3 ;

[0047] The radiation dose distribution data consists of a number of radiation therapy doses and corresponding radiation position coordinate data; the radiation therapy dose is the total irradiation dose.

[0048] The image processing module is used to perform image processing on the original medical image to obtain the target medical image.

[0049] The scheme optimization module is used to optimize the original radiation dose distribution data with the help of the plan optimization model in combination with the target medical image, obtain the radiation dose distribution optimization result, and generate a radiotherapy dose scheme.

[0050] The system control module is used to call other modules of the system to implement the radiotherapy plan optimization function.

[0051] Referring to FIG4 , the system implements the radiotherapy plan optimization function according to the following steps:

[0052] Step 1: The data acquisition module obtains and reads the original radiation plan data to obtain the original radiation therapy plan data;

[0053] Step 2: The image analysis module performs radiation dose distribution detection on the original radiotherapy plan image to obtain original radiation dose distribution data;

[0054] Step 3: The image processing module performs image enhancement on the original medical image to obtain the target medical image;

[0055] Step 4: The image analysis module performs key area detection on the target medical image to obtain core target area distribution data and normal residual liver area distribution data;

[0056] The core target area includes the GTV area, the PGTV area and the GTVboost area;

[0057] The core target area distribution data includes GTV distribution data, PGTV distribution data and GTVboost distribution area;

[0058] Step 5: The target medical image, original radiation dose distribution data, normal residual liver area distribution data, and core target area distribution data are input into the plan optimization module, and the radiation dose distribution optimization result is obtained with the help of the plan optimization model;

[0059] The planning optimization model is a model obtained by training with a deep learning algorithm.

[0060] Furthermore, the radiation dose distribution optimization result has the following characteristics:

[0061] The normal remaining liver area irradiated with less than 500 cGy has a volume greater than 100 ml;

[0062] At least 95% of the volume of GTVboost received a total dose of 30 Gy or 35 Gy;

[0063] At least 95% of the GTV volume received a total dose of 25 Gy;

[0064] At least 95% of the PGTV volume received a total dose product of 20 Gy.

[0065] It can be understood that the plan optimization model will optimize the input data according to the characteristics of the above-mentioned radiation dose distribution optimization results.

[0066] Furthermore, after obtaining the radiation dose distribution optimization result, a radiotherapy dose plan will be generated according to the preset number of irradiations and the radiation dose distribution optimization result;

[0067] The radiotherapy dose plan includes a number of radiation position coordinates and the radiotherapy doses corresponding to the positions;

[0068] The radiation therapy dose is composed of three parameters: total radiation dose, single radiation dose, and number of radiation doses; the format is "total radiation dose / single radiation dose / number of radiation doses";

[0069] In the radiotherapy dose plan, the value of the total irradiation dose parameter corresponds to the radiotherapy dose in the radiation dose distribution optimization result;

[0070] The value of the irradiation times parameter is the preset irradiation times;

[0071] The single irradiation dose is the ratio of the total irradiation dose to the number of irradiations.

[0072] The preset number of irradiation times is 5f (f is the unit of irradiation times).

[0073] It is understood that a radiation therapy dose regimen should have the following characteristics:

[0074] The normal remaining liver area irradiated with less than 500 cGy has a volume greater than 100 ml;

[0075] At least 95% of the volume of GTVboost should reach a dose of 30Gy / 6Gy / 5f or 35Gy / 7Gy / 5f;

[0076] At least 95% of the GTV volume should receive a dose of 25 Gy / 5 Gy / 5 fractions;

[0077] At least 95% of the PGTV volume should receive a dose of 20 Gy / 4 Gy / 5 fractions.

[0078] As an embodiment, step 2 specifically includes:

[0079] Step 21: performing image preprocessing on the original radiotherapy plan image to obtain a first image to be identified;

[0080] The image preprocessing specifically includes: performing image filtering on the original radiotherapy plan image; converting the color space of the original radiotherapy plan image into the HSV space;

[0081] Step 22: Segment the first image to be identified using a threshold segmentation method according to preset radiotherapy dose distribution color features to obtain a plurality of dose distribution regions;

[0082] Step 23: Use the connected region analysis algorithm to find the continuous dose distribution area;

[0083] Step 24: Extract edge information of each dose distribution area with the help of edge detection algorithm, and obtain original radiotherapy dose distribution data after screening all edge information.

[0084] As an embodiment, performing image enhancement in step 3 specifically includes:

[0085] Step 31: Obtain the radiation position corresponding to the minimum radiation therapy dose from the original radiation therapy dose distribution data to obtain the minimum dose area;

[0086] Step 32: intercepting a first key image region from the original medical image; the first key image region is a rectangular region, the first key image region should include the minimum dose region, and the distance between the edge of the first key image region and the minimum dose region is a preset retention length;

[0087] Step 33: Use an image enhancement algorithm to perform image enhancement on the first key image area to obtain a target medical image.

[0088] As an embodiment, the key area detection in step 4 is implemented by the following steps:

[0089] Step 41: identifying the normal residual liver area and the GTV area from the target medical image using a target recognition algorithm to obtain normal residual liver area distribution data and GTV distribution data;

[0090] Step 42: Expand the GTV area outward to obtain the PGTV area and obtain PGTV distribution data;

[0091] Step 43: The GTV region is shrunk inward by a preset distance to the GTVboost region to obtain GTVboost distribution data.

[0092] Furthermore, the target recognition algorithm is implemented using a deep learning-based target detection method, specifically any one of the YOLO algorithm, the SSD algorithm, and the Faster-CNN algorithm. The above algorithms are all mature technical solutions, and those skilled in the art can successfully implement them based on the description of the above embodiments, and will not be repeated here.

[0093] The specific methods of expanding the GTV area to obtain the PGTV area include:

[0094] Step 421: Obtain a 4D-CT image and obtain a respiratory motion amplitude value therefrom;

[0095] Step 422: Expand the GTV area outward in the head-to-foot direction by a distance equal to the respiratory movement amplitude; at the same time, expand outward in directions other than the head-to-foot direction by a preset distance to obtain the PGTV area.

[0096] Furthermore, the range of the respiratory movement amplitude value is 0.5-1.5 cm; the preset outward expansion distance is 0.5 cm; and the preset inward contraction distance is 1 cm.

[0097] As an embodiment, the planning optimization model is trained by the following steps:

[0098] Step 501: Obtain a model training data set;

[0099] Step 502: Perform model training using an adversarial training method using a generative adversarial network structure, wherein the generative adversarial network structure is composed of a generator and a discriminator.

[0100] The adversarial training method includes:

[0101] Step 5021: Initialize the generative adversarial network structure;

[0102] Step 5022: Fix the parameters of the discriminator and train the generator so that the generator reaches the first training goal;

[0103] Step 5023: Fix the parameters of the generator and train the discriminator so that the discriminator reaches the second training goal;

[0104] Step 5024: Evaluate the model performance at this time. If the model performance does not meet the preset model performance conditions, return to step 5022;

[0105] Step 503: When the performance of the trained model meets the preset model performance conditions, a trained planning optimization model is obtained.

[0106] Furthermore, the model training data set is composed of a number of image optimization data pairs; the image optimization data pairs are the radiographic image data to be optimized and the corresponding optimized radiographic image;

[0107] The radiological image data to be optimized includes radiological medical images and corresponding radiation dose distribution data;

[0108] The radiation dose distribution data is obtained through the method of steps 21-24 based on the radiation treatment plan data corresponding to the radiation medical image.

[0109] Furthermore, the generator of the generative adversarial network structure consists of an encoding unit and a decoding unit.

[0110] The encoding unit is composed of the following structures in series: a first encoding convolution layer, a second encoding convolution layer, a first encoding maximum pooling layer, a third encoding convolution layer, a fourth encoding convolution layer, a second encoding maximum pooling layer, a fifth encoding convolution layer, a sixth encoding convolution layer, a third encoding maximum pooling layer, a seventh encoding convolution layer, an eighth encoding convolution layer, and a fourth encoding maximum pooling layer.

[0111] The input data size of the encoding unit is [H, W, C1+C2], where H and W are the height and width of the radiological medical image, respectively; C1 is the number of channels of the radiological medical image, and C2 is the number of channels of the radiation dose distribution data.

[0112] The first coding convolution layer, the second coding convolution layer, the third coding convolution layer, the fourth coding convolution layer, the fifth coding convolution layer, the sixth coding convolution layer, the seventh coding convolution layer, and the eighth coding convolution layer are all 3*3 convolution layers.

[0113] The first coding maximum pooling layer, the second coding maximum pooling layer, the third coding maximum pooling layer, and the fourth coding maximum pooling layer are all MaxPool layers with a pooling size of 2*2.

[0114] The decoding unit consists of the following structure: a first deconvolution layer, a first merging layer, a first decoding convolution layer, a second decoding convolution layer, a first maximum anti-pooling layer, a second deconvolution layer, a second merging layer, a third decoding convolution layer, a fourth decoding convolution layer, a second maximum anti-pooling layer, a third deconvolution layer, a third merging layer, a fifth decoding convolution layer, a sixth decoding convolution layer, a third maximum anti-pooling layer, a seventh decoding convolution layer, and an eighth decoding convolution layer.

[0115] The input of the first deconvolution layer is the output of the encoding unit, the input of the first merging layer is the first deconvolution layer and the eighth encoding convolution layer; the input of the first decoding convolution layer is the output of the first merging layer; the first decoding convolution layer, the second decoding convolution layer, the first maximum depooling layer and the second deconvolution layer are connected in series;

[0116] The input of the second merging layer is the second deconvolution layer and the sixth encoding convolution layer; the input of the third decoding convolution layer is the output of the second merging layer; the third decoding convolution layer, the fourth decoding convolution layer, the second maximum depooling layer and the third deconvolution layer are connected in series;

[0117] The input of the third merging layer is the third deconvolution layer and the fourth encoding convolution layer; the input of the fifth decoding convolution layer is the output of the third merging layer; the fifth decoding convolution layer, the sixth decoding convolution layer, the third maximum depooling layer, the seventh decoding convolution layer, and the eighth decoding convolution layer are connected in series.

[0118] The first deconvolution layer, the second deconvolution layer, and the third deconvolution layer are all 3*3 deconvolution layers;

[0119] The first decoding convolution layer, the second decoding convolution layer, the third decoding convolution layer, the fourth decoding convolution layer, the fifth decoding convolution layer, the sixth decoding convolution layer, the seventh decoding convolution layer, and the eighth decoding convolution layer are all 3*3 convolution layers;

[0120] The first maximum anti-pooling layer, the second maximum anti-pooling layer, and the third maximum anti-pooling layer are all 2*2 maximum anti-pooling layers.

[0121] Furthermore, the first training objective is to minimize the value of the mean square error loss function; and the second training objective is to minimize the value of the cross entropy loss function.

[0122] As an embodiment, the system further includes a system optimization module, which is used to evaluate the radiation dose distribution optimization result in combination with the actual radiation therapy plan and optimize the radiation therapy plan optimization function.

[0123] The system optimization module optimizes the radiotherapy plan optimization function including:

[0124] Step 61: obtaining an actual radiotherapy plan;

[0125] Step 62: comparing the difference in radiation dose distribution data between the actual radiation therapy plan and the corresponding radiation dose distribution optimization result, and calculating the similarity of the results;

[0126] Step 63: If the result similarity is greater than a preset result similarity threshold, the radiation dose distribution optimization result is considered valid; otherwise, the radiation dose distribution optimization result is invalid;

[0127] Step 64: Count the invalid ratios of the radiation dose distribution optimization results and calculate the failure rate of the optimization results;

[0128] Step 65: When the failure rate of the optimization result is greater than a preset optimization failure threshold, the actual radiotherapy plan is used to retrain the plan optimization model.

[0129] As an embodiment, the method of the present invention may be implemented in software and / or a combination of software and hardware, for example, by using an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device.

[0130] The method of the present invention can be implemented in the form of a software program that can be executed by a processor to perform the steps or functions described above. Similarly, the software program (including related data structures) can be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive, a floppy disk, or the like.

[0131] In addition, some steps or functions of the method of the present invention may be implemented using hardware, for example, as a circuit that cooperates with a processor to execute each step or function.

[0132] In addition, a portion of the method described in the present invention may be implemented as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the method and / or technical solution according to the present application through the operation of the computer. The program instructions for invoking the method described in the present invention may be stored in a fixed or removable recording medium, and / or transmitted via a data stream in a broadcast or other signal-carrying medium, and / or stored in a working memory of a computer device that operates according to the program instructions.

[0133] As an embodiment, the present invention also provides a device comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, the device is triggered to run the methods and / or technical solutions based on the aforementioned multiple embodiments.

[0134] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0135] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0136] In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0137] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment, it is not intended to limit the present invention. Any technician familiar with the present profession can make slight changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A medium-dose radiotherapy optimization system for sub-total liver diffuse tumor thrombus hepatocellular carcinoma, characterized in that the system consists of the following modules: Data acquisition module; Image analysis module; Image processing module; Plan optimization module; The data acquisition module is used to obtain the target radiotherapy plan and obtain the original radiotherapy plan data; the original radiotherapy plan data includes the original radiotherapy plan image and the original medical image; The image analysis module is used to obtain the original radiotherapy dose distribution data from the original radiotherapy plan image, and perform key area detection on the target medical image to obtain the core target area distribution data; The image processing module is used to perform image processing on the original medical image to obtain the target medical image; The plan optimization module is used to optimize the original radiotherapy dose distribution data by means of the plan optimization model combined with the target medical image, obtain the optimized result of the radiotherapy dose distribution, and generate a radiotherapy dose plan; The radiotherapy dose plan should have the following characteristics: The volume of the normal remaining liver area irradiated below 500 cGy is greater than 100 ml; At least 95% of the volume of GTVboost should reach a dose of 30 Gy / 6 Gy / 5f or 35 Gy / 7 Gy / 5f; At least 95% of the volume of GTV should reach a dose of 25 Gy / 5 Gy / 5f; At least 95% of the volume of PGTV should reach a dose of 20 Gy / 4 Gy / 5f.

2. The system according to claim 1, characterized in that the system realizes the radiotherapy plan optimization function according to the following steps: Step 1, the data acquisition module obtains and reads the original radiotherapy plan data to obtain the original radiotherapy plan data; Step 2, the image analysis module performs radiotherapy dose distribution detection on the original radiotherapy plan image to obtain the original radiotherapy dose distribution data; Step 3, the image processing module performs image enhancement on the original medical image to obtain the target medical image; Step 4, the image analysis module performs key area detection on the target medical image to obtain the core target area distribution data and the normal remaining liver area distribution data; The core target area includes the GTV area, the PGTV area and the GTVboost area; the core target area distribution data includes the GTV distribution data, the PGTV distribution data and the GTVboost distribution area; Step 5, the target medical image and the original radiotherapy dose distribution data are transmitted into the plan optimization module, and the optimized result of the radiotherapy dose distribution is obtained by means of the plan optimization model; The plan optimization model is a model trained by a deep learning algorithm.

3. The system according to claim 2, characterized in that Step 2 specifically includes: Step 21, perform image preprocessing on the original radiotherapy plan image to obtain the first image to be recognized; The image preprocessing specifically includes: performing image filtering on the original radiotherapy plan image; converting the color space of the original radiotherapy plan image to the HSV space; Step 22, perform image segmentation on the first image to be recognized by using the threshold segmentation method according to the preset radiotherapy dose distribution color feature to obtain a number of dose distribution regions; Step 23, using a connected region analysis algorithm to find a continuous dose distribution region; Step 24: extract edge information of each dose distribution area with the help of edge detection algorithm, and obtain original radiation dose distribution data after screening all edge information.

4. The system according to claim 2, characterized in that Image enhancement in step 3 specifically includes: Step 31, obtaining the position corresponding to the minimum radiation dose from the original radiation therapy dose distribution data to obtain the minimum dose area; Step 32: intercepting a first key image region in the original medical image; Step 33: Use an image enhancement algorithm to perform image enhancement on the first key image area to obtain a target medical image.

5. The system according to claim 4, characterized in that The first key image area is a rectangular area, the first key image area should include the minimum dose area, and the distance between the edge of the first key image area and the minimum dose area is a preset retention length.

6. The system according to claim 2, characterized in that Step 4 specifically includes: Step 41, identifying the GTV region from the target medical image with the aid of a target recognition algorithm, and obtaining GTV distribution data; Step 42: Expand the GTV area outward to obtain the PGTV area and obtain the PGTV distribution data; Step 43: Based on the GTV area, the GTVboost area is obtained by shrinking the GTV area inward by a preset shrinkage distance. GTVboost distribution data; wherein the preset retraction distance is 1 cm.

7. The system according to claim 1, characterized in that The planning optimization model is trained by the following steps: Step 501: Obtain a model training data set; Step 502: Perform model training using an adversarial training method with the help of a generative adversarial network structure; Step 503: When the performance of the trained model meets the preset model performance conditions, a trained planning optimization model is obtained.

8. The system according to claim 7, characterized in that The model training data set is composed of a number of image optimization data pairs; the image optimization data pairs are the radiological image data to be optimized and the corresponding optimized radiotherapy images; The radiotherapy image data to be optimized includes radiotherapy medical images and corresponding radiotherapy dose distribution data; The radiotherapy dose distribution data is obtained through the method of steps 21-24 based on the radiotherapy plan data corresponding to the radiological medical image.

9. The system according to claim 7, characterized in that The generative adversarial network structure consists of a generator and a discriminator; The generator of the generative adversarial network structure consists of an encoding unit and a decoding unit.

10. The system according to claim 1, characterized in that The system also includes a system optimization module, which is used to evaluate the radiation dose distribution optimization result in combination with the actual radiation therapy plan and optimize the radiation therapy plan optimization function.

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