162 results about "Thyroid nodular" patented technology

The invention discloses a benign and malignant nodule grading evaluation system based on large model fusion ultrasonic imaging and thyroid gene markers, which can organically fuse non-invasive examination and serological detection, can simulate and diagnose multi-grade risk probability information provided by a gold standard, realizes similar risk grading estimation in a non-invasive mode, and has a wide application prospect. The thyroid nodule risk assessment method can provide visual explanation conforming to clinical logic based on comprehensive information of iconography and molecular biology, can significantly improve the accuracy of thyroid nodule risk assessment, can also effectively improve clinical decision-making efficiency and patient credibility, and has important clinical application prospects. The system comprises a data acquisition module, an ultrasonic image feature extraction module, a gene marker feature extraction module, a multi-modal fusion and hierarchical reasoning module and a generation module.

The invention relates to the field of medicine, and discloses a thyroid nodule surgical operation planning and navigation system based on artificial intelligence, which comprises a multi-modal medical image acquisition module, an image preprocessing and enhancing module, an intelligent anatomical structure analysis module, an operation planning decision module and an intraoperative real-time navigation module, the multi-modal medical image acquisition module is used for integrating data of various image equipment such as ultrasonic equipment, CT equipment and MRI equipment, and space-time consistency of multi-modal images is ensured. According to the thyroid nodule surgical operation planning and navigation system based on artificial intelligence, the real-time calculation problem is solved, the system delay is reduced, which is crucial to real-time navigation in an operation, the real-time performance and the operation efficiency are improved, the operation time is shortened, and complications are reduced; by integrating the advantages of different images, the system can provide more comprehensive dissection views, help doctors to more accurately position nodules and important structures and avoid injury to nerves and blood vessels, so that the accuracy is improved, and the surgical risk is further reduced.

The invention relates to the field of medical image analysis, in particular to a deep learning prediction system and method based on multi-modal thyroid cancer lymph node metastasis, and the system comprises a data collection module, a preprocessing module, a nodule segmentation module, a feature extraction module, a feature fusion module, a metastasis prediction module, an interpretability analysis module and a result display module. An ultrasonic image, an elastic imaging image, an ultra-micro blood flow image and clinical index data of a patient are integrated, an improved U-Net algorithm is used for precise segmentation of a thyroid nodule region, a multi-branch deep network is used for extracting multi-modal features, a dynamic weight fusion algorithm is used for integrating the features, and the accuracy of the thyroid nodule region is improved. According to the method, the thyroid cancer lymph node metastasis state (non-metastasis, central region metastasis or lateral neck metastasis) is predicted, meanwhile, a two-dimensional interpretability framework of Grad-CAM activation diagram and SHAP value contribution degree analysis is introduced, an intuitive prediction basis is provided for doctors, and the thyroid cancer lymph node metastasis prediction accuracy is remarkably improved.

The invention relates to the technical field of image classification, in particular to a thyroid nodule ultrasonic image classification method and system based on feature extraction, and the method comprises the following steps: based on thyroid nodule ultrasonic image data, calling a pixel normalization function to adjust a gray scale range to a set interval, screening an isolated noise region, and carrying out the smooth filtering, and acquiring the ultrasonic image after smoothing filtering. According to the method, by adjusting the gray scale range and screening isolated noise areas, gray scale distribution is balanced, noise interference is reduced, image readability is improved, edge change is recognized by analyzing the gradient direction through a small-scale convolution kernel, texture features are extracted through a medium-scale filter, redundancy is reduced in combination with pooling operation, and the feature hierarchical expression ability is enhanced; the edge sharpness parameter is calculated, the feature weight is optimized, the edge sharpness and the classification adaptability are improved, the local feature segmentation precision is improved, the classification boundary is adjusted according to the similarity score, the classification loss measurement is optimized, the error offset is reduced, and the classification stability and accuracy are improved.

The invention discloses a thyroid nodule automatic diagnosis method based on multi-modal deep learning. After ultrasonic image data, CT image data and clinical data of a patient are collected and preprocessed, spatial alignment of multi-modal data is achieved; extracting deep features from the multi-modal image data by using a convolutional neural network, performing image segmentation on a thyroid nodule region by using an improved ResNet structure and a U-Net structure, and extracting a region of interest; feature extraction and fusion are realized; constructing a deep neural network of a mixed mode, and training and optimizing a deep learning model; the model reasoning comprises the steps of preprocessing new input data, extracting and fusing features and generating a final diagnosis result. According to the invention, the automatic diagnosis of the thyroid nodules is realized by fusing the multi-modal data from different image sources and combining with the advanced deep learning model, so that the accuracy and efficiency of diagnosis are improved.

The invention discloses a thyroid cancer pathological image classification method and system based on multi-modal feature fusion and Bayesian optimization, and relates to the technical field of medical image processing, and the method comprises the steps: collecting a thyroid ultrasound image data set, and carrying out the preprocessing operation; extracting an improved local binary pattern feature, a Haralick texture feature and a VGG16 depth feature, and constructing a mixed feature space; and splicing the features in the mixed feature space into a 4119-dimensional mixed feature vector, and carrying out feature importance screening by utilizing ExtraTres. Improved local binary pattern features, Haralick texture features and VGG16 depth features are fused, a mixed feature space is constructed, feature importance screening and PCA dimension reduction are performed by using ExtraTrees, multi-level features of the image are effectively extracted, the accuracy of benign and malignant thyroid nodule classification is remarkably improved, meanwhile, network hyper-parameters are dynamically adjusted through a Bayesian optimization algorithm, and the classification accuracy of benign and malignant thyroid nodules is improved. And model convergence is accelerated in combination with a cosine annealing strategy, so that the generalization ability of the model is enhanced, and the model can be excellently expressed on different data sets.

The utility model discloses a puncture rotary cutting needle for thyroid nodule treatment, which is characterized in that an outer tube and an inner tube are sleeved on the puncture rotary cutting needle, the inner tube is rotatably connected in the outer tube, and an outer side rotary cutting opening and an inner side rotary cutting opening are respectively arranged on the outer tube and the inner tube and are matched to form a rotary cutting window communicated with an inner cavity of the inner tube; the far end of the outer tube is constructed to be provided with the puncture tip, so that a puncture function is further realized on the basis of realizing rotary cutting and suction, thyroid tissues can be punctured and rotary-cut at the same time in a puncture entering process, the condition that the thyroid tissues need to enter and exit for multiple times is avoided, and the problem that an existing rotary cutting planer tool is difficult to adapt to a thyroid surgery scene is solved. According to the embodiment, the far end of the outer side rotary cutting opening extends to a part of the puncture tip, so that the opening position of the rotary cutting window at the far end is relatively lower than the opening position of the rotary cutting window at the near end, and when the thyroid tissue is sucked under negative pressure, the adsorption force to the thyroid tissue at the far end can be larger; and therefore, a better inhalation effect can be obtained.

The invention discloses a thyroid nodule segmentation method based on a large model and cooperative consistent training, relates to the technical field of ultrasonic image analysis, and organically embeds the general reasoning ability of the large model into a semi-supervised model to complement the advantages of the semi-supervised model and the large model. The insufficient knowledge migration capability of the semi-supervised model is relieved by utilizing the general prediction capability of the large model, and meanwhile, the high-confidence misprediction of the large model is relieved by utilizing the feature collaborative consistency of the semi-supervised model, so that the actual prediction performance of the model is improved; through large model fine tuning operation under a semi-supervised framework, the large model can be subjected to fine tuning synchronously based on an unsupervised image and a supervised image, and meanwhile, the fine tuning convergence speed and accuracy of the model are accelerated based on collaborative loss and a high-quality pseudo tag; and through collaborative consistency training operation, the advantages of a semi-supervised model and an SAM model are organically complemented, and a high-quality pseudo-label generation scheme and collaborative loss based on self-reliability and collaborative reliability are provided.

The invention discloses a method for identifying benign and malignant thyroid nodules. The invention provides application of a reagent for detecting the expression level of genes in a sample of a subject to preparation of a kit for diagnosing or predicting thyroid cancer of the subject or identifying benign and malignant thyroid nodules of the subject, wherein the genes comprise one or more genes selected from the group consisting of TPO, SLC5A8, SLC26A4, GLIS3, DIO1, DIO2, TG, SLC5A5 and NKX2-1. The invention also provides a corresponding device and a kit.

The invention relates to the technical field of computer medical detection, in particular to a thyroid nodule benign and malignant classification method based on clinical information, radiomics and gene detection. According to the method, pathological diagnosis of thyroid nodules is used as a dependent variable, detection data including radiomics characteristics and molecular omics are used as continuous variables, and the detection data of at least three genes CLDN10, HMGA2 and LANM3 are selected as the data of the molecular omics; meanwhile, in combination with part of clinical pathological characteristics including gender, age and BRAF V600E mutation condition factors, a thyroid nodule preoperative diagnosis prediction model based on radiomics and molecular omics is constructed through an SVM modeling method. Gene detection, radiomics and clinical basic information are combined, and a thyroid nodule diagnosis model based on the combination is established on the basis of Chinese population. The method is used for assisting clinicians in distinguishing benign and malignant thyroid nodules and guiding clinical decisions

The invention relates to the technical field of ultrasonic imaging analysis, in particular to a thyroid nodule ultrasonic examination method and system.The thyroid nodule ultrasonic examination method comprises the following steps that thyroid tissue ultrasonic echo signals are obtained, instantaneous characteristics of nodule area signals are analyzed, energy distribution and instantaneous frequency difference values are calculated, stable resonance modes are screened, and a resonance mode characteristic matrix is established. According to the method, the decomposition precision of echo data and the accurate recognition capability of a target signal are improved by optimizing instantaneous feature analysis of an ultrasonic signal, background noise interference is remarkably reduced by combining energy distribution and frequency difference screening, image enhancement is optimized through a gradient change rate and a transformation kernel matrix, the definition of image details is improved, and the recognition accuracy of the target signal is improved. Short-time frequency feature extraction and frequency deviation measurement enhance the recognition capability of lesion tissues, and meanwhile, through analysis of frequency shift stability parameters and the deviation change rate, the accuracy of automatic judgment of nodule properties is improved, so that the thyroid nodule ultrasonic examination is more scientific and accurate.

The invention discloses a thyroid nodule ultrasound image segmentation method fusing factorization VMama and characteristic frequency band separation, and relates to the field of computer vision assisted medical treatment. Preprocessing the thyroid nodule ultrasonic image data set; inputting the processed image into an encoder of a segmentation model FMVM-DFFT for processing, extracting image features in a multi-level manner by using an FMVSS module based on VSS, and further finely extracting and fusing feature information under different frequencies by using a feature frequency band separation module DFFT; the method comprises the following steps of: transforming input features into a frequency domain from a space domain by adopting improved fast Fourier transform, decomposing the input features into high and low frequency components, respectively optimizing the high and low frequency components through point-by-point convolution and a channel attention module, recombining the high and low frequency components in a complex domain, and restoring the high and low frequency components to the space domain through inverse Fourier transform; then, through dynamic convolution, layer normalization processing and reserved input features, merging and transmitting to a decoder; the details of the segmentation result are further optimized; the problem that edge segmentation is inaccurate and not fine is solved.

The invention discloses a thyroid nodule benign and malignant prediction method based on automatic segmentation and semi-supervised learning. The thyroid nodule benign and malignant prediction method comprises the following steps of data set establishment, image preprocessing, thyroid nodule segmentation model training, mask and image fusion processing, thyroid nodule benign and malignant classification model training and semi-supervised learning model construction. According to the method, the thyroid nodule effective area is segmented firstly, subsequent benign and malignant classification tasks are assisted, and information space is expanded and learned through semi-supervised learning, so that the problems of high medical image marking cost, insufficient data and limitation on deep learning model performance in the prior art are solved; and meanwhile, the second-generation basic model of the sliding window layered visual converter after mixing fusion is carried out on the basis of pre-training and by using the nodule region generated by the thyroid segmentation model realizes good performance, and high-precision thyroid nodule benign and malignant classification prediction can be realized.

The invention relates to a primer composition, kit and method for detecting benign and malignant thyroid nodule related gene mutation, and the primer composition comprises a primer pair for detecting benign and malignant thyroid nodule related gene DNA mutation and RNA fusion variation. The primer pair can be independently amplified, and can be used for performing efficient, high-sensitivity and high-specificity multiple amplification on DNA and cDNA templates to be detected under the same PCR reaction system and reaction program, and then the gene mutation condition of a related region can be obtained by constructing a library and sequencing, so as to assist clinical diagnosis of benign and malignant thyroid nodules. Moreover, specific nucleotide sequences are respectively connected to the 5'end of the upstream primer and the 3 'end of the corresponding downstream primer in a proper proportion in the primer composition, so that the homogeneity and specificity of multiple PCR amplification can be further improved, and the detected nucleic acid input amount and the lowest detection limit of mutation frequency (as low as 0.5%) are effectively reduced.

The invention relates to the technical field of image processing, in particular to an ultrasonic thyroid nodule feature pyramid edge weighted diffusion segmentation method, which comprises the following steps: acquiring an ultrasonic thyroid nodule image; a PEW-SegDiff model is constructed; gaussian noise is added through time in the forward diffusion process of the real mask image, and a label with noise is obtained; in the back diffusion process, the input image and the real mask image after forward diffusion are added to obtain a noise label feature image; training a PEW-SegDiff model by using the noise tag feature graph and the input graph to obtain a predicted initial image; and calculating the loss between the predicted image and the back diffusion image of the real mask. The thyroid nodule edge blurred image extraction method solves the problems that an original SegDiff model is limited in boundary learning capability and the effect is poor when thyroid nodule edge blurred images are extracted.

The invention discloses a composition for detecting benign and malignant thyroid nodules and a detection method thereof, and relates to the technical field of medical detection, the composition comprises the following components: a DNA / RNA extraction reagent and a fluorescent quantitative PCR reagent; by combining the DNA / RNA extraction reagent and the fluorescent quantitative PCR reagent, nucleic acid can be efficiently and accurately extracted from the thyroid nodule fine needle puncture sample, specific gene mutation and gene fusion are detected through the fluorescent quantitative PCR technology, the accuracy and sensitivity of benign and malignant judgment of the thyroid nodule are remarkably improved, and the method is suitable for clinical application. And a doctor can formulate a treatment scheme earlier and more accurately, and the prognosis of a patient is improved.

The thyroid nodule classification system based on the ultrasonic radio frequency signal comprises a data acquisition module, a model construction module and a classification module. The data acquisition module is used for acquiring ultrasonic radio-frequency signal data of thyroid nodules, preprocessing ultrasonic radio-frequency signals to form an instance set, and constructing a data set with labels. The model construction module is used for constructing a neural network model for performing feature classification on the thyroid nodules and training the neural network model based on the data set; the neural network model comprises a feature conversion layer, a dynamic weight generation layer, a self-attention layer and a classification probability fusion layer. And the classification module is used for carrying out feature classification on the thyroid nodules based on the trained neural network model. The method has the advantages that the original ultrasonic radio-frequency signals are directly analyzed, so that information loss caused by image reconstruction is effectively avoided, key information in the ultrasonic radio-frequency signals can be accurately captured and analyzed, and high-precision classification of thyroid nodules is realized.

The invention discloses a lightweight multi-feature fusion thyroid nodule classification method. Relates to the technical field of medical image processing, in particular to the technical field of a lightweight multi-feature fusion thyroid nodule classification method. According to the method, the Hifuse model is improved, so that the parameter quantity and the calculation complexity can be reduced while the global features and the local features of the image are fully mined and the benign and malignant thyroid nodules are comprehensively discriminated in a multi-view and multi-azimuth manner. The method comprises the following steps: acquiring and preprocessing a thyroid nodule ultrasonic diagnosis report image data set; a Hifuse model is constructed and improved; a radiomics feature learning module is introduced into the Hifuse model, the local attention module is replaced by the improved local attention module, and the global attention module is replaced by an Officient ViM global extraction module; and completing thyroid nodule classification by adopting the trained Hifuse improved model.

The invention relates to a thyroid nodule classification method based on a global lightweight attention module, and belongs to the field of deep learning and medical image processing. Comprising the steps of constructing a thyroid nodule data set; preprocessing and expanding a data set; constructing an attention mechanism-based improved classification model; training an attention mechanism-based improved classification model; the model is used for thyroid nodule benign and malignant classification; according to the method, the region of interest of the thyroid nodule ultrasonic image with the unified size is input, a global lightweight attention module and a high-low channel jump connection module are combined, and particularly, local features and global dependency of a feature map are adaptively combined through the global lightweight attention module, so that the local features and the global dependency of the feature map are combined, and the local features and the global dependency of the feature map are combined. And according to the channel direction, high and low frequency features are divided into a jump connection module, and feature extraction is further carried out on the thyroid nodule. More comprehensive thyroid nodule features can be obtained by using the network model, so that a more accurate and more efficient classification result can be obtained.

The invention relates to a thyroid nodule classification method and system, and the method comprises the steps: firstly obtaining a plurality of thyroid nodule images, building a class label of each thyroid nodule image, carrying out the preprocessing of all thyroid nodule images, obtaining the preprocessed thyroid nodule images, building a nodule classification model, and carrying out the classification of the thyroid nodule images. The thyroid nodule image recognition method comprises the steps of preprocessing thyroid nodule images, inputting the preprocessed thyroid nodule images into a nodule classification model to obtain label reference parameters corresponding to each category label, and finally training the nodule classification model based on the label reference parameters to obtain a trained nodule classification model. And the problem of misjudgment or missed judgment is reduced.

The invention discloses a thyroid nodule segmentation system capable of dynamically fusing multi-echo features, relates to the technical field of artificial intelligence and ultrasonic image analysis, and realizes accurate identification and segmentation of thyroid nodule multi-echo types through cooperative work of three feature branches of low echo, high echo and equal echo. A special feature extraction module is designed for each branch for a specific echo type, the extraction process of different echo nodule features is optimized, the capturing capability of key features is enhanced, and the adaptability of the model to a complex ultrasonic image is remarkably improved; according to the method, adaptive weight distribution of different echo type features is realized through attention weighted fusion of the multi-modal feature maps, the mechanism generates attention weights by using a double-layer full-connection network, and weighted fusion is performed on the feature maps after Softmax normalization, so that the problem of fixed weight or lack of adaptability in a traditional feature fusion method is solved.

The invention belongs to the technical field of food processing, and particularly relates to low-molecular-weight fucoidin and application thereof. The fucoidin with low molecular weight is obtained after the fucoidin is treated, the number-average molecular weight of the fucoidin is 3-4 kDa, the content of sulfate radicals in the fucoidin is 10%-18%, the fucoidin has specific monosaccharide composition, and experimental results show that the fucoidin has the advantages that the fucoidin is low in content of sulfate radicals, and the fucoidin is low in content of sulfate radicals. The low-molecular-weight fucoidin provided by the invention has a wide application prospect in preparation of drugs for adjuvant treatment of thyroid nodules, and fills the technical blank about the relationship between fucoidin and thyroid nodules at present.

The application relates to the field of medical image assisted ablation technology, and provides a thyroid nodule ablation control system based on ultrasound contrast parameters. The system comprises an ultrasound data acquisition module, a contrast parameter analysis module, a blood supply identification module, an ablation strategy generation module, an ablation execution module, a feedback evaluation module, a closed-loop control module and an energy optimization module. By acquiring an ultrasound contrast image of target tissue, perfusion time, perfusion intensity and perfusion sequence parameters are extracted, blood supply arteries or blood supply areas are identified, and a phased ablation strategy is generated, the blood supply area is preferentially ablated, and then main ablation is carried out on the target tissue; at the same time, residual active areas are detected based on contrast feedback, and supplementary ablation is carried out through a closed-loop control mechanism; and ablation parameters are dynamically optimized in combination with a unit volume energy model. The system can improve ablation integrity, reduce the heat sink effect and the risk of intraoperative bleeding, reduce the recurrence rate, and improve energy utilization efficiency.

The invention relates to the field of medical informatics, and provides a biomarker-based thyroid nodule benign and malignant identification method and system. The method comprises the following steps: collecting a serum sample; performing quantitative detection on the multiple markers in the serum sample to obtain concentration data of the multiple markers; inputting the multi-marker concentration data into a pre-constructed probability calculation model to obtain a prediction probability value; and according to a preset diagnosis threshold value and the prediction probability value, judging whether the thyroid nodule is benign or malignant to obtain a thyroid nodule benign or malignant identification result. By integrating multi-marker information, the biological difference between benign and malignant nodules is effectively captured, and the overall efficiency of differential diagnosis is improved.

The application relates to the technical field of data processing, in particular to a thyroid nodule fine-grained automatic identification and classification system based on weak supervision learning, which comprises a server, the server is pre-set with a thyroid fine-grained identification model, the server is used for acquiring an identification image, and a fine-grained identification result output by the thyroid fine-grained identification model according to the identification image; the thyroid fine-grained identification model comprises: a feature pyramid attention module, which is used for processing the identification image according to a feature pyramid attention algorithm; a spatial attention module, which is used for acquiring a binary mask, generating an attention map according to the binary mask, and positioning and discriminating a region; and a multi-network fusion module, which is used for acquiring output results of multiple backbone networks, fusing the output results, and generating a fine-grained identification result. The scheme can solve the technical problem that, when the thyroid nodule is classified in a fine-grained manner in the prior art, the classification accuracy is low due to the multi-scale and multi-network structure of an ultrasonic image.

The invention provides a methylation marker combination for identifying and diagnosing benign and malignant thyroid nodules or diagnosing thyroid cancer. The methylation marker combination comprises methylation TSHR, NIS and TPO genes or fragments thereof. In addition, the invention further provides a thyroid cancer diagnosis product and device based on the methylation marker combination and application of the thyroid cancer diagnosis product and device in thyroid nodule benign and malignant identification and thyroid cancer diagnosis. Whether a thyroid tissue sample or a blood sample is taken as a detection object, the methylation marker combination can realize efficient and sensitive diagnosis of thyroid cancer, and especially has outstanding performance in early thyroid cancer diagnosis, so that the methylation marker combination has a good application prospect. And the kit has important clinical application value and practical significance for early screening and diagnosis of thyroid cancer.

The invention provides a thyroid disease multi-omics mass spectrum construction analysis method and system.The thyroid disease multi-omics mass spectrum construction analysis method comprises the steps that a thyroid ultrasound image and multi-omics mass spectrum data are obtained, and a cross-modal incidence relation is generated based on anatomical structure characteristics of the thyroid ultrasound image and biomolecule space distribution characteristics of the multi-omics mass spectrum data; in combination with the thyroid anatomical structure deformation field and a multi-task optimization target, performing nonlinear coupling calculation on a back propagation error in a multi-task learning iteration process to obtain a gradient contribution weight between tasks; and generating a target multi-omics mass spectrum based on the cross-modal correlation features, the anatomical structure deformation field and the gradient contribution weight, and performing thyroid pathological feature analysis on the target multi-omics mass spectrum to obtain a three-dimensional positioning coordinate and a malignancy probability prediction value of the thyroid nodule. The thyroid disease diagnosis comprehensiveness and accuracy are improved.

This invention relates to the field of traditional Chinese medicine and natural medicines, and discloses a traditional Chinese medicine composition for treating thyroiditis, nodular goiter, and thyroid nodules, as well as its preparation method and application. The traditional Chinese medicine composition is made from raw materials in parts by weight of 1-30 parts of Prunella vulgaris, 1-30 parts of Taraxacum mongolicum, 1-30 parts of Hordeum vulgare malt, 1-20 parts of Citrus reticulata peel, 1-24 parts of Crataegus pinnatifida, and 1-24 parts of Raphanus sativus seed. It can not only significantly reduce serum TgAb and TPOAb antibody levels, inhibiting the attack of autoimmune response on the thyroid gland from the source, but also effectively reduce lymphocyte infiltration, repair damaged thyroid follicular structure and restore colloid content, thereby reversing immune damage and tissue destruction of the thyroid gland, reducing thyroid nodules, and solving the problems of existing treatments for thyroiditis, nodular goiter, and thyroid nodules that cannot reverse immune damage to the thyroid gland, destruction of follicular structure, and tissue fibrosis.

The invention discloses a combined medicine composition of a thyroid Qixia patch and a selenium yeast tablet and application of the combined medicine composition, and belongs to the technical field of medicines. The composition is composed of a thyroid and astragalus mongholicus summer patch and a selenium yeast tablet, the thyroid and astragalus mongholicus summer patch is prepared by grinding astragalus mongholicus, selfheal, radix clematidis, turmeric, airpotato yam and semen brassicae as raw materials, and adding vaseline to prepare paste. The selenium yeast tablet is used for oral administration and is matched with the thyroid qi-summer patch for external application on the neck, and the treatment course is 3 months. Clinical verifications show that the total effective rate of the composition reaches 87.80%, the composition can effectively improve symptoms such as anterior cervical mass, dysphoria, irritability and the like, reduce the level of serum TPOAb, reduce thyroid and nodule volumes and improve nodule blood flow, the safety is good, the curative effect is superior to that of pure western medicine treatment, and an effective treatment scheme is provided for related diseases.

The application belongs to the technical field of molecular biology and medicine, and discloses a methylation marker for detecting the benignity and malignancy of thyroid nodules and an application. 780 new methylation sites are identified, 273 tumor occurrence and development related genes are associated, and the Fc gamma R-mediated phagocytosis signal pathway is involved. The methylation marker for detecting the benignity and malignancy of thyroid nodules comprises multiple genes and multiple methylation sites. The methylation level of multiple genes and multiple sites is detected, the problem of low and unstable methylation signal of single gene or single site is overcome, and thus the sensitivity and specificity of detection are improved.