91 results about "Post radiotherapy" patented technology

The invention discloses a tumor radiotherapy effect evaluation method and system based on machine learning. The method comprises the following steps: collecting clinical information of a patient; performing data preprocessing on the clinical information of the patient to obtain standard patient data; according to the standard patient data, a chi-square test model is adopted for screening, and patient data features related to prediction indexes are obtained; inputting the patient data features into a pre-constructed multi-task deep learning model, inputting screened features, and predicting the survival state of the patient after radiotherapy to obtain a tumor volume estimation result; wherein the tumor volume estimation result comprises tumor prediction results of a first period, a second period and a third period after treatment; wherein the multi-task deep learning model is a model obtained by training according to an original sample. According to the method, the labeling quantity of the medical image is increased, and the labeling efficiency is improved.

The embodiment of the invention provides a tumor radiotherapy reaction prediction method and system based on multi-instance learning. The method comprises the following steps: constructing a radiotherapy image data set; extracting multi-dimensional features from the radiotherapy image data set and combining and splicing the multi-dimensional features to obtain embedded representation of the multi-dimensional features; the multi-dimensional features comprise two-dimensional convolution features and three-dimensional convolution features; a multi-instance learning model based on a Transform is constructed; taking the embedded representation of the multi-dimensional features as model input, and performing iterative training on the multi-instance learning model to obtain a trained multi-instance learning model; and predicting the radiotherapy metaphase reaction of the patient by using the trained multi-instance learning model, analyzing and evaluating the model prediction process in combination with interpretable analysis, and performing model evaluation on the prediction result of the tumor area of the patient. According to the method provided by the invention, the reaction condition after tumor radiotherapy can be predicted, so that a doctor can be intelligently assisted to carry out precise radiotherapy decision on a patient, and the prognosis risk and harm of the patient are reduced.

The invention discloses a recurrence-resistant cell strain for colorectal cancer large segmentation radiotherapy and application, and relates to the technical field of cell biology. The colorectal cancer large segmentation radiotherapy recurrence-resistant cell strain is preserved in the China Center for Type Culture Collection on January 2, 2025, the preservation address is Wuhan University, Wuhan, China, and the preservation number is CCTCC NO: C202533. By constructing the colorectal cancer large segmentation radiotherapy recurrence-resistant cell strain, a new biological material is provided for studying the radiotherapy recurrence-resistant colorectal cancer. The cell strain is screened in a mouse body with a sound immune system, has better physiological correlation, and is beneficial to research on the influence of an immune microenvironment on tumor cell radiotherapy resistance and the remodeling effect of radiotherapy resistant cells on the immune microenvironment. The cell strain can also monitor tumor recurrence after radiotherapy in real time through fluorescence imaging.

The invention provides a model and a system for predicting the lifetime of a non-small cell lung cancer patient after radiotherapy. The system comprises an image processing module; a feature extraction module; the tumor radiomics model is trained on the basis of important tumor radiomics characteristics; the lung radiomics model is trained based on the important lung radiomics features; integrating a radiomics model, fusing the important tumor radiomics features and the important lung radiomics features, and screening out the most critical features; and finally fusing the model, and constructing and training to obtain a lifetime prediction model based on the most critical features and the clinical features. Tumor and lung radiomics characteristics and clinical parameters are integrated into the machine learning model and can be used for accurately predicting the overall lifetime of a lung cancer patient receiving radical radiotherapy, and the model is high in prediction precision, high in flooding ability and high in application value.

The invention relates to the technical field of clinical medicine, and discloses an AI prediction system for relapse after radiotherapy of local advanced rectal cancer, and the system comprises a multi-modal biological image database building module which is used for collecting clinical data, image data, radiotherapy target region and dose distribution data, radiobiological data and genomics information of a patient, integrating the data to establish a database; a feature extraction and screening module; a prediction model construction module; a model verification and optimization module; and a clinical application evaluation module. According to the invention, information of patients in multiple aspects such as clinic, image, radiotherapy target area and dose distribution, radiobiology and genomics is widely collected, and the multi-modal biological image database is integrated and established, so that various factors influencing rectum cancer relapse after radiotherapy can be comprehensively covered; a rich, accurate and comprehensive data basis is provided for subsequent accurate analysis and prediction, and the accuracy and reliability of prediction of relapse conditions after radiotherapy are improved.

The invention discloses a post-radiotherapy patient nursing auxiliary decision-making system based on artificial intelligence, and relates to the field of medical rehabilitation nursing, and the system comprises a management and control module which serves as a core management end of each function module and carries out the editing and sending of a control instruction; the data archive library is used for storing all the collected user personal data and analysis data thereof; the data acquisition module is used for acquiring basic physique data, illness state related data and radiotherapy plans of the patient through a preset period; individual data of patients are analyzed through a prediction model, personalized nursing suggestions are generated based on medical history, health indexes and radiotherapy plans, personalized nursing schemes are provided for each patient according to curative effect analysis of similar patients, customization is carried out according to unique illness states, physiques and treatment plans of the patients, professional knowledge and data in the medical field are combined, and the patient health management system is established. Potential abnormal changes are output, and patients and medical staff are helped to make timely decisions.

The invention discloses a tumor radiotherapy prognosis prediction simulation method and system based on big data and a medium, and relates to the technical field of tumor radiotherapy prognosis, and the method comprises the following steps: collecting basic physiological information of a tumor patient, and collecting a color image and a temperature image of skin of a radiotherapy part of the tumor patient; skin color feature extraction and skin temperature feature extraction are carried out to obtain skin color feature data and skin temperature feature data; a radiodermatitis prediction model is constructed; performing prediction simulation on the skin of the radiotherapy part of the tumor patient based on the radiodermatitis prediction model, and performing prediction simulation correction to obtain a radiodermatitis prediction result of the tumor patient; the method is used for solving the problem that when an existing tumor radiotherapy prognosis technology is used for predicting and simulating disease development, the risk and time of radiodermatitis of a tumor patient cannot be objectively and accurately predicted and simulated according to physiological characteristics of skin of the tumor patient after radiotherapy.

The invention belongs to the technical field of data processing, and particularly discloses a method and system for predicting the lifetime of a non-small cell lung cancer patient after radiotherapy, and the method comprises the steps: collecting multi-time-point imaging and hematology follow-up visit data of the patient after radiotherapy, building a longitudinal follow-up visit sequence, and mapping the longitudinal follow-up visit sequence to a unified time axis; determining a bimodal first-time significant improvement point through preset rule judgment, and constructing and standardizing an asynchronous index according to the bimodal first-time significant improvement point; then inputting a feature vector at a follow-up time point, encoding a bimodal sequence through a Transform time sequence encoder, and generating joint representation through cross-modal interaction; and finally, inputting the standardized asynchronous index, the joint representation and the clinical and radiotherapy characteristics into a DeepHit model, and outputting a patient survival distribution prediction result. According to the method, bimodal indexes can be accurately captured, time dislocation is improved, prediction accuracy and individualization degree are improved, and a reliable basis is provided for clinical prognosis evaluation.

The invention provides a method and system for analyzing lung cancer brain metastasis organoid cell subpopulation and functional characteristics, and the method comprises a series of steps: data quality control, batch effect removal, cell grouping, copy number variation inference, quasi-time sequence analysis, function enrichment analysis, and drug sensitivity analysis before and after radiotherapy. Single cell transcriptome sequencing data obtained after organoid culture is subjected to personalized analysis, heterogeneity of tumor cells and sensitivity of the tumor cells to drugs after radiotherapy are described, and the characteristics of all cell subgroups in the lung cancer brain metastasis organoid and the effect of the cell subgroups in the tumor microenvironment can be accurately analyzed; powerful support is provided for mechanism research and precise medicine of lung cancer brain metastasis.

The invention discloses a tumor radiotherapy rehabilitation nursing intervention system based on artificial intelligence, and relates to the technical field of tumor rehabilitation nursing. By integrating multi-dimensional data and combining clinical standards to output quantitative evaluation levels and key weak dimensions, rehabilitation weakness of a patient is accurately positioned, a targeted target is provided for the nursing intervention strategy generation module, a personalized scheme is generated, and pertinence and performability of intervention are ensured; policy execution data are captured in real time and a scheme is dynamically adjusted, so that the intervention process always adapts to the rehabilitation state change of a patient, and meanwhile, active management and control on high-risk groups are enhanced by periodically collecting core indexes and linking risk upgrading and priority intervention, so that the tumor radiotherapy rehabilitation nursing is changed from passive response to active pre-judgment, and the rehabilitation nursing efficiency is improved. And the overall quality of rehabilitation nursing after radiotherapy and the prognosis effect of a patient are optimized.

The invention discloses an X-ray thermoacoustic image reconstruction method based on self-supervised multi-view fusion, and relates to the technical field of medical imaging and radiotherapy dose monitoring. The method comprises the following steps: firstly, acquiring initial sound pressure distribution after radiotherapy simulation by utilizing a tissue HU value and related physical attributes, and simulating X-ray induced ultrasonic signals under different sampling conditions under a K-Wave platform so as to construct a diversified training data set, further, providing a multi-view fusion and artifact decoupling network model MFDRNet based on self-supervision, according to the model, sparse acoustic signals and image feature information can be extracted and fused at the same time, reconstructed global and local detail expressions are enhanced through a multi-scale attention mechanism, and separation of a real structure and artifact components is achieved by means of an artifact decoupling structure; in the training process, a self-supervision loss mechanism is adopted, and stable training can still be completed under the condition of lack of high-quality full-sampling labels.

The invention discloses a system for predicting the risk of myelosuppression after radiotherapy of a nephroblastoma patient. Belongs to the technical field of probability prediction, and particularly relates to the field of prediction of myelosuppression risks after radiotherapy of nephroblastoma patients. The technical problem that related technologies related to prediction of the myelosuppression risk after radiotherapy of a nephroblastoma patient are lacked at present is solved. The system comprises a data acquisition module, a data preprocessing module, a data set division module and a prediction module. The data preprocessing module is used for sequentially carrying out sample quality control processing, data missing value processing and feature conversion processing on the nephroblastoma patient data and then inputting the preprocessed nephroblastoma patient data into the data set division module; and the prediction module performs prediction model building and prediction model optimization in sequence, performs model training and testing by using the training set and the testing set, finally obtains a model capable of performing bone marrow suppression risk prediction after radiotherapy of a nephroblastoma patient, and applies the model to actual prediction.

The invention discloses a traditional Chinese medicine compound composition with an effect of preventing and treating radioactive injury as well as a preparation method and application thereof. The traditional Chinese medicine compound composition is prepared from 5-15 parts of American ginseng, 3-6 parts of sea-buckthorn and 10-30 parts of rhizoma polygonati. The composition is proportioned and optimized according to the theory of treatment based on syndrome differentiation, and the medicines are combined to achieve the effects of tonifying qi, activating blood and nourishing yin. Clinical experiment results show that the traditional Chinese medicine compound composition provided by the invention has a remarkable effect of preventing and treating radioactive injury, particularly has a remarkable curative effect in the aspects of improving oral mucositis, dry mouth, weakness, sleep disorder and the like after radiotherapy, and is safe in clinical medication.

The invention discloses a radiotherapy activated self-assembled polypeptide magnetic resonance contrast agent as well as a preparation method and application thereof. The magnetic resonance contrast agent is prepared by sequentially connecting a tumor targeting peptide module, a radiotherapy activation peptide module, an assembly peptide module and a contrast molecule fragment module through solid-phase synthesis. The magnetic resonance contrast agent can perform targeted recognition in tumor-bearing mice and penetrate tumor cells, and can perform self-assembly under the action of apoptosis protease generated by induction after radiotherapy to form nanofibers, so that the intracellular residence time is prolonged, T1 weighted magnetic resonance imaging signals are enhanced, and the imaging contrast ratio of tumor parts after radiotherapy is further improved. The magnetic resonance contrast agent provides higher accuracy and safety for tumor radiotherapy guided by magnetic resonance imaging, can be used as a novel molecular probe for monitoring the tumor radiotherapy process and effect in real time, and has a good clinical application prospect.

This invention discloses an artificial intelligence-based intervention system for tumor radiotherapy rehabilitation nursing, belonging to the field of tumor rehabilitation nursing technology. By integrating multi-dimensional data and combining clinical standard output quantitative assessment levels and key weakness dimensions, this invention accurately identifies patients' rehabilitation shortcomings, providing targeted objectives for the nursing intervention strategy generation module and generating personalized plans to ensure the intervention's relevance and feasibility. It captures strategy execution data in real time and dynamically adjusts the plan, ensuring the intervention process always adapts to changes in the patient's rehabilitation status. Simultaneously, by regularly collecting core indicators and linking risk escalation and priority intervention, it strengthens proactive management of high-risk groups, realizing a shift from passive response to proactive prediction in tumor radiotherapy rehabilitation nursing, thus optimizing the overall quality of post-radiotherapy rehabilitation nursing and patient prognosis.

The utility model relates to the technical field of rehabilitation training, in particular to a lower limb rehabilitation training device for radiotherapy nursing, which comprises a base and a mounting seat fixed above the base, a pedal training device is arranged on the outer side of the mounting seat to provide riding training for a patient, a backrest seat is arranged above the mounting seat, and the pedal training device is connected with the backrest seat. A supporting frame is arranged on the left side of the backrest seat, a handle is fixed to the upper portion of the supporting frame, a translation pushing mechanism is arranged on the right side of the backrest seat and provides training resistance for horizontal movement of the backrest seat, and a pedal is arranged on the right side of the supporting frame and provides lower limb bending training support for a patient. According to the lower limb rehabilitation training device for radiotherapy nursing, pedal training is provided for a patient through the pedal training device, bending and straightening training of legs of the patient can be achieved through cooperation of the pedal plate and the backrest seat, two different training modes are provided for the legs of the patient, training diversity is improved, and recovery of the patient after radiotherapy is facilitated.

The present invention discloses a method, system, device and storage medium for processing radiomics data of recurrent nasopharyngeal carcinoma. The radiomics data processing method comprises: obtaining MRI image data of patients with recurrent nasopharyngeal carcinoma; extracting image data for analysis, segmenting lesions, extracting image features, screening radiomics key features; inputting radiomics key features into the random forest model obtained through training to predict the risk of nasopharyngeal necrosis. The present invention provides accurate guidance for the prediction of the risk of nasopharyngeal necrosis and the formulation of treatment plans for patients with recurrent nasopharyngeal carcinoma, so that a part of patients with high risk of nasopharyngeal necrosis can avoid adverse outcomes after re-radiotherapy, and promote individualized and accurate diagnosis and treatment of recurrent nasopharyngeal carcinoma.

The present invention relates to a preparation method of a manganese tetraoxide / GSDME complex and its application, which can effectively solve the problem of tumor cell re-proliferation and normal tissue damage after radiotherapy. The technical solution is to firstly prepare a manganese tetraoxide / GSDME complex in the presence of Mn 3 O 4 The surface of the nanoparticles is modified with GSDME protein, and a manganese tetraoxide / GSDME complex is prepared by cross-linking tannic acid with the GSDME protein. The preparation method of the invention is stable and reliable, and is easy to operate. 3 O 4 The / GSDME complex has the advantages of good biocompatibility and low toxicity and side effects. It can achieve efficient intracellular delivery of GSDME protein, activate the Caspase-3 / GSDME signaling pathway, convert radiotherapy-induced tumor cell apoptosis into pyroptosis to inhibit tumor regeneration; and utilize the difference in intracellular GSH content between tumor cells and normal cells to inhibit the expression of Mn in tumor cells. 3 O 4 It can enhance radiosensitization by downregulating GSH, while Mn 3 O 4 It is an innovation in tumor radiotherapy drugs to exert its nanozyme activity to eliminate ROS produced by radiotherapy and achieve radiation protection for normal tissues.

An apparatus comprises a processor and memory that stores instructions to cause the processor to receive user data that includes images of a body and organ. The processor is further caused to generate, via computed tomography imaging, a first image map and a second image map based on the user data. The processor is further caused to analyze, via spatial normalization of each image, the first image map and the second image map to determine strength of correlation of each tissue point of each image. The processor is further caused to execute a trained machine-learning model to generate a dose distribution image map using the first image map and the second image map as inputs. The processor is further caused to predict a change in a patient reported outcome for a user associated with the user data based on the dose distribution image map.

The utility model discloses a post-radiotherapy upper limb nursing exercise device, and relates to the technical field of nursing auxiliary instruments. Comprising a clamping block and a rectangular box, clamping mechanisms are arranged on the lower side wall of the clamping block close to the left and right positions, a gear motor is fixedly installed on the inner wall of the lower side of the rectangular box close to the right position, a transmission rod is fixedly installed on the lower surface of an output shaft of the gear motor, and the lower surface of the transmission rod is fixedly connected with the upper surface of the clamping block; and a cushion block is arranged at the position, close to the right side, of the upper surface of the rectangular box, a placing plate is installed at the position, close to the left side, of the upper surface of the rectangular box through a first hinge piece, and a connecting rod is installed on the lower surface of the placing plate through a second hinge piece. The device is small in size, convenient to install on a sickbed, capable of meeting the requirement for bedridden exercise of a patient after radiotherapy, high in practicability and beneficial to use, and has multiple exercise modes, a following exercise mode and an autonomous exercise mode.

1. The name of the product of this design: electric nasal cleaner. 2. Purpose of the product with this appearance design: used for cleaning patients with nasal diseases such as acute and chronic rhinitis, allergic rhinitis, nasal polyps, sinusitis, etc., and also for nasal cleaning after nasal surgery and radiotherapy and daily nasal care. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention discloses a neck radiotherapy sequelae management and intervention system and method, and relates to the technical field of radiotherapy management, and the system comprises a symptom detection module, a data processing module used for analyzing data obtained by the symptom detection module, an intervention suggestion module used for generating personalized intervention suggestions, and a user interface module. Specifically, physiological parameters of a patient, images of neck skin and muscles and subjective symptom feedback are obtained in real time; integrating data from different sources to form a complete patient symptom database; processing the data by using a machine learning algorithm and a statistical analysis method, and identifying a symptom mode and trend; according to the analysis result, a patient symptom report is generated, and the patient symptom report comprises the severity, the change trend and the potential risk of the current symptom; and personalized intervention suggestions are provided for the patient. According to the invention, early detection, evaluation and personalized intervention of the neck radiotherapy sequelae can be realized, and the method has high clinical application value.

The invention discloses a nano-vesicle composition for inhibiting tumor metastasis after radiotherapy, which is characterized in that the nano-vesicle is an engineered bacterial outer membrane vesicle (OS-D), and the composition also comprises a pH responsive phospholipid modification component and a small interfering RNA (siRNA) capable of silencing CCDC25 gene expression; the siRNA is used for inhibiting formation of neutrophil Extracellular Traps (NETs) induced by radiotherapy and tumor cell migration and metastasis mediated by the NETs by blocking the interaction between the NETs and tumor cells, so that the effect of inhibiting tumor metastasis after radiotherapy is realized. The nano-vesicle composition provided by the invention can specifically block tumor metastasis mediated by neutrophil extracellular traps in a tumor microenvironment so as to enhance the effect of tumor postoperative radiotherapy and chemotherapy and effectively inhibit tumor metastasis and relapse.

This paper proposes a tumor-associated fibroblast-targeted nanosystem, consisting of PLGA nanoparticles co-encapsulating anti-fibrotic and anti-aging drugs as the core, and a 4T1 cell membrane expressing a specific FAP chimeric antigen receptor as the biomimetic shell. The present invention constructs FAP-CAR-4T1 cells by embedding FAP scFv onto 4T1 cells. FAP-CAR-CM@PLGA NPs co-encapsulate anti-fibrotic and anti-aging drugs, enabling FAP-CAR-CM@PLGA-AB NPs to target CAFs and SC CAFs, improving radiotherapy resistance and achieving a dual clearance effect while simultaneously relieving the tumor's immunosuppressive microenvironment, effectively reducing breast cancer recurrence after radiotherapy.

The utility model relates to the technical field of medical treatment, and provides a close-range radiotherapy afterloading equipment storage box which comprises a supporting frame, the top end of the inner wall of the supporting frame is fixedly connected with front and back opposite mounting frames, the bottom end of the inner wall of each mounting frame is provided with left and right opposite limiting grooves, and the top end of the inner wall of each mounting frame is fixedly connected with a double-end motor. The two driving ends of the double-head motor are fixedly connected with screw rods, and the outer side ends of the screw rods are rotationally connected to the inner wall of the mounting frame. According to the storage box, through cooperation of the supporting frame, the mounting frame, the limiting groove, the double-head motor, the screw rod, the threaded block, the first hinge frame, the connecting rod, the second hinge frame, the guide plate and the universal wheels, the effect of reinforcing the bottom of the storage box is achieved, the guide plate can be driven by the double-head motor to move upwards in the using process, and then the universal wheels move synchronously; and further, connection and reinforcement are carried out through the support frame and the contact surface.

This application provides a method and system for predicting tumor radiotherapy response based on multi-instance learning. The method includes: constructing a radiotherapy image dataset; extracting and combining multi-dimensional features from the radiotherapy image dataset to obtain an embedded representation of the multi-dimensional features; the multi-dimensional features include two-dimensional convolutional features and three-dimensional convolutional features; constructing a Transformer-based multi-instance learning model; iteratively training the multi-instance learning model using the embedded representation of the multi-dimensional features as model input to obtain a trained multi-instance learning model; using the trained multi-instance learning model to predict the patient's mid-term radiotherapy response, and analyzing and evaluating the model prediction process using interpretable analysis, and evaluating the model prediction results for the patient's tumor region. The method provided in this application can predict the response after tumor radiotherapy, thereby intelligently assisting doctors in making precise radiotherapy decisions for patients and reducing the risk and harm to patient prognosis.

The invention belongs to the technical field of tumor radiotherapy assistance, and particularly relates to a tumor radiotherapy assistance device which comprises a radiotherapy bed, a translation plate and positioning holes. According to the invention, the radiotherapy bed is assembled at the side end of the external radiotherapy equipment through the positioning hole and the bolt body structure at the side end. The translation plate slides through a motor and a ball screw which are arranged in the radiotherapy bed, and conveys the patient to a radiotherapy area. After the patient finishes radiotherapy, if the patient is weak and inconvenient to walk, the patient can sit on the first cushion of the cart under the support of the medical staff. Afterwards, transferring is carried out through traction of medical staff. In the process, an operator only needs to pull the trigger plate with two hands, the spring bolt can be separated from the clamping groove under the supporting of the supporting shaft, and then unlocking is completed. When the cart is used for transporting and transferring a patient, due to the fact that the first cushion leaves, the second cushion needs to be pulled out from the inside of the radiotherapy bed, and the gap of the radiotherapy bed is filled up by the second cushion under the support of the spring bolt, so that a complete bed surface is formed.

The invention provides a tumor-associated fibroblast targeting nano adjuvant as well as a preparation method and application thereof, and belongs to the field of nano drugs. The nano adjuvant (named as PF9PEI and alpha PC NPs) provided by the invention is coupled with PEI25K modified by 3-perfluorobutyl-1, 2-epoxypropane by utilizing a PD-L2 targeting peptide nrirgenyryh, so that an efficient gene vector (PF9PEI) is formed, and is used for loading an alpha PD-L1-P2A-CD86 plasmid. According to the nano adjuvant, CAFs can be reprogrammed into engineered antigen presentation CAFs by expressing a costimulatory molecule CD86, so that T cell immunity of breast cancer specificity is activated, and T cell failure is retarded at the same time. Besides, the engineered CAFs generated by the nano adjuvant generate alphaPD-L1 locally, so that the immune escape mechanism of breast cancer cells is effectively blocked, and the immune surveillance function of T cells is recovered. After radiotherapy, by using the nano adjuvant for treatment, the generation of central memory T cells is promoted, the progress of the same solid tumor is inhibited, the tumor metastasis is eliminated, and the tumor recurrence is inhibited.

The utility model relates to oral cavity exercise equipment technical field especially nasopharyngeal carcinoma post -radiotherapy oral cavity support exercise device, its technical scheme includes support ring, still includes upper denture and lower denture, is located in the one side of support ring and sets up oppositely up and down, is located in the upper denture and lower denture middle part air bag, the utility model discloses the cooperation of support ring, air bag, measuring mechanism, broadcast mechanism etc. structure, when using device, through the inflation nozzle through gas pipe to air bag inflation, can adjust the upper denture and lower denture opening amplitude, adapts to different oral cavity state, promotes the comfort degree, when the upper denture and lower denture movement, drive first slide rod, second slide rod and pointer slide on the oval measuring ring, the intuitive display opening amplitude, sliding adjustment slider set target value, when first slide rod triggers contact sensor, voice prompter will feedback further situation, let the user clear grasp progress, strengthen the persistence power, avoid the midway abandonment.

The utility model discloses a skin protection device after breast radiotherapy, which comprises a first suspender and a second suspender, protective covers are connected outside the front surfaces of the first suspender and the second suspender through zippers, and an adjusting mechanism is arranged on one side of the first suspender; the adjusting mechanism comprises an adjusting shell fixedly installed on one side of the first suspender, an adjusting opening is formed in the outer wall of one side of the adjusting shell, the inner wall of the bottom of the adjusting shell is rotationally connected with a rotating shaft through a bearing, and the distance between the two protection covers can be adjusted through rotation of the rotating shaft. The protective covers of the first suspender garment and the second suspender garment can accurately protect the breast part through the rotation operation of the rotating shaft in the adjusting shell, the effectiveness of the device can be protected through fine adjustment, extra pressure or friction on a wound can be avoided, and the good protection effect on a breast cancer patient is achieved.