222 results about "Pulmonary nodule" patented technology

The invention discloses a pulmonary nodule boundary detection method based on semantic segmentation. The method comprises the following steps: generating an edge prior image set; obtaining a multi-scale boundary alignment feature map set and a deformation offset parameter set; a post-gating deformation offset parameter set is obtained, the multi-scale boundary alignment feature image set is updated, cross-scale feature fusion is executed, and a fusion feature image set is generated; establishing a node set and an edge set by utilizing node features in the fusion feature graph set and a spatial adjacency relationship, and initializing a node feature vector set; forming a fusion feature map set enhanced by multi-hop reasoning; and outputting a pulmonary nodule region mask prediction image set and a pulmonary nodule boundary probability prediction image set. According to the method, fracture between pseudo boundaries and slices is effectively inhibited, so that a three-dimensional structure prediction result is more coherent, and high-readability and high-consistency boundary segmentation output is provided for a clinical CAD (Computer Aided Design) system.

The invention provides a pulmonary nodule display method and device, electronic equipment and a storage medium, and the method comprises the steps: segmenting a three-dimensional reconstruction image of the chest of a target patient to obtain a preoperative segmentation image, the three-dimensional reconstruction image being obtained based on preoperative CT data, and the preoperative segmentation image comprising the position information of a pulmonary nodule; segmenting the video image of the intraoperative lung tissue of the target patient collected by the thoracoscope in real time to obtain an intraoperative segmented image; feature matching is conducted on the preoperative segmented image and the intra-operative segmented image, the lens pose of the thoracoscope is determined, the preoperative segmented image is mapped into a two-dimensional reference image based on the lens pose, and the two-dimensional reference image comprises the position information of the pulmonary nodule; and carrying out image registration on the two-dimensional reference image and the intra-operative segmented image, and carrying out pulmonary nodule marking on the intra-operative segmented image to obtain a video image for displaying pulmonary nodules in real time. The position of the pulmonary nodule in the operation is accurately displayed in real time in a non-invasive mode, and the operation efficiency is improved.

The invention discloses a three-dimensional pulmonary nodule detection method and system based on morphological adaptation convolution, and the method comprises the steps: obtaining a chest CT image sequence, and carrying out the preprocessing of all chest CT images in the chest CT image sequence; according to the three-dimensional pulmonary nodule detection method and system based on form adaptive convolution, scale factors are introduced into a 3D dynamic convolution layer and cooperate with the offset, so that the position of a convolution kernel sampling point can be adaptively adjusted according to the form and size of the pulmonary nodule, and the problem that the traditional fixed convolution is difficult to adapt to the size difference and form heterogeneity of the pulmonary nodule is solved; stable detection of pulmonary nodules with different sizes, especially tiny pulmonary nodules, is realized; space coordinate information and multi-scale features are combined through a channel space attention module, extraction of key information such as pulmonary nodule edge texture and density gradient is enhanced, interference of blood vessel, trachea and CT artifacts is inhibited, and the problem that small nodules and background noise are difficult to distinguish is solved.

The embodiment of the invention provides a pulmonary nodule automatic labeling method based on multi-modal semantic affinity iterative optimization, and belongs to the technical field of data processing, and the method specifically comprises the steps: obtaining a lung CT image and a text report; performing structured semantic analysis on the text report; performing visual feature extraction on the lung CT image; based on the key semantic information, performing semantic guidance processing on the depth visual feature representation to generate semantic enhanced visual guidance features; based on the semantic enhanced visual guidance features, calling a segmentation basic model, and generating a plurality of candidate segmentation masks by introducing an active variation mechanism; for each candidate segmentation mask, calculating a semantic matching degree score of the candidate segmentation mask; and comparing the semantic matching degree scores of all the candidate segmentation masks, and selecting the candidate segmentation mask with the highest score as a final pulmonary nodule labeling result to be output. Through the scheme of the invention, the labeling precision, adaptability and interpretability are improved.

The invention provides a context feature aggregation pulmonary nodule segmentation method combining edge perception and multi-scale semantic guidance, and the method comprises the steps: obtaining a to-be-segmented pulmonary nodule CT image, inputting the to-be-segmented pulmonary nodule CT image into a trained EGP-Net network model, and obtaining a segmentation result; wherein in the EGP-Net network model, multilayer features of an encoder are sent to a global pyramid guide module to reconstruct and selectively introduce deep global semantics back to a shallow layer, and low-layer features are merged into an edge guide network to extract and reinforce fine-grained boundary information; the attention feature fusion module performs weighted attention fusion on global semantics and local edge features, and the multi-scale context decoder fuses multi-scale information through lightweight reconstruction such as sub-pixel convolution to generate a fine segmentation map. Experimental results show that the EGP-Net network model is superior to an existing advanced method in segmentation precision and boundary consistency, and has good clinical auxiliary diagnosis and precision medical application potential.

The invention relates to the field of medical image processing and artificial intelligence, in particular to a pulmonary nodule treatment effect AI evaluation system which comprises an image acquisition module, an image registration module, a feature representation module, a multi-scale analysis module, a trajectory analysis module, a response prediction module and a decision support module. According to the system, accurate alignment of CT images before and after treatment is realized through a 4D registration technology, manifold representation of a pulmonary nodule state is constructed based on a differential geometry theory, and nodule features are mapped into points on a high-dimensional manifold; extracting features of different time and space scales by adopting multi-scale space-time analysis, and constructing a manifold trajectory representing a treatment response process; geodesic prediction is realized by using a Riemann geometric framework, and long-term curative effect is predicted from early treatment response; the system not only evaluates the current treatment effect, but also can provide personalized treatment suggestions and optimal follow-up visit plans.

The invention discloses a pulmonary nodule malignancy probability prediction method based on radiomics and clinical features, and the method comprises the following steps: collecting and processing original lung image data, and extracting pulmonary nodule image blocks; collecting and processing clinical data of a target patient, and constructing a structured clinical feature vector; inputting the pulmonary nodule image block and the structured feature vector into a dual-channel adaptive fusion network, and extracting an image feature vector and a structured embedded feature vector; a modal sensing module judges that a modal is missing, a modal compensation module is started when the modal is missing, a pseudo-dual-channel output feature pair is generated, and otherwise, a cross attention fusion feature vector is generated; a dynamic fusion module carries out dynamic fusion on the cross attention fusion feature vector or the pseudo dual-channel feature pair, and outputs a dynamic fusion feature vector; and inputting the dynamic fusion feature vector into a classifier, and outputting a malignant probability value of the pulmonary nodule. According to the invention, a dual-channel adaptive fusion network is adopted to realize the intelligent prediction of the malignant probability of pulmonary nodules.

The invention discloses a lung abnormal nodule identification processing method and system, and the method comprises the steps: obtaining lung CT image data, and carrying out the preprocessing of the lung CT image data, and obtaining a to-be-detected lung image; performing pixel point analysis on the lung image to be detected by using a discrete fractional differential operator to obtain a fractional order enhanced image; carrying out multi-scale and multi-direction acquisition on pixel points of the fractional order enhanced image to calculate local energy and local entropy, and constructing a high-dimensional fusion feature matrix; carrying out dimensionality reduction on the high-dimensional fusion feature matrix, and extracting to obtain a pulmonary nodule region; the nodule region in the lung CT image can be effectively extracted; through pixel point analysis, calculation of local energy and entropy, and construction and dimension reduction of a high-dimensional feature matrix, the lung abnormal nodule region is finally identified, and the detection precision and efficiency of the pulmonary nodule are improved.

The invention discloses a pulmonary nodule benign and malignant identification and prediction system based on multi-modal feature fusion, and belongs to the technical field of data processing, and the system comprises a data collection module which is used for collecting pulmonary nodule multi-modal data; the feature fusion module is used for carrying out preprocessing and feature fusion on the pulmonary nodule multi-modal data, and generating pulmonary nodule multi-modal fusion data by integrating features of different modals; and the identification and prediction module is used for constructing a pulmonary nodule benign and malignant identification and prediction model, analyzing and identifying the pulmonary nodule multi-modal fusion data according to the pulmonary nodule benign and malignant identification and prediction model, and determining a pulmonary nodule benign and malignant identification result. According to the method, the problems that effective pulmonary nodule benign and malignant identification and prediction cannot be carried out based on multi-modal feature fusion in the prior art, and the accuracy and efficiency of pulmonary nodule benign and malignant identification are reduced are solved. Effective pulmonary nodule benign and malignant identification and prediction can be carried out based on multi-modal feature fusion, and the accuracy and efficiency of pulmonary nodule benign and malignant identification can be improved.

A traditional Chinese medicine composition for treating pulmonary nodules, wherein the composition is prepared from the following traditional Chinese medicines in parts by weight: a principle drug selected from one or more of 10-45 parts of raw Astragali radix, 10-25 parts of Sargassum, and 10-25 parts of Arisaematis rhizoma preparatum; a minister drug selected from one or more of 10-25 parts of Fritillariae thunbergii bulbus, 10-25 parts of Forsythiae fructus, 10-25 parts of stir-fried Atractylodis macrocephalae rhizoma, 1T0 part of Hirudo, 10-25 parts of Dioscoreae nipponicae rhizoma, 10-35 parts of Adenophorae radix, 10-35 parts of honey-processed Mori cortex, 10-25 parts of Perillae fructus, 10-25 parts of Peucedani radix, 10-35 parts of Phragmitis rhizome, 10-45 parts of raw Coicis semen, 5-25 parts of vinegar-processed Cyperi rhizoma, 10-25 parts of Aurantii fructus, 10-25 parts of Paeoniae radix alba, 10-35 parts of Scutellariae barbatae herba, 10-35 parts of Herba duchesneae indicae, 10-35 parts of Salviae chinensis herba, 5-20 parts of Menispermi rhizoma, 5-20 parts of Momordicae semen, 10-25 parts of Curcumae rhizoma, and 5-20 parts of Gleditsiae spina; an assistant drug selected from one or more of 5-25 parts of vinegar-processed Bupleuri radix, 5-25 parts of Citri reticulatae pericarpium, 10-25 parts of Perillae fructus, 2-12 parts of processed Ephedrae herba, 10-25 parts of Cynanchi stauntonii rhizoma et radix, 5-20 parts of Typhonii rhizoma, 2-12 parts of Zingiberis rhizoma, 10-35 parts of Morindae officinalis radix, 10-35 parts of Scutellariae barbatae herba, 10-45 parts of Radix aconiti lateralis praeparata nigra, and 10-25 parts of Typhonii rhizoma praeparata; and an envoy drug selected from one or more of 5-20 parts of Fagopyri dibotryis rhizoma, 10-25 parts of Platycodonis radix, 10-25 parts of Paeoniae radix alba, and 5-25 parts of Glycyrrhizae radix et rhizoma praeparata cum melle.

The invention discloses a pulmonary nodule MRI segmentation method based on attention-guided cross-modal fusion, belongs to the field of medical image processing, and aims to solve the problems of high missing detection rate of small nodules, insufficient multi-modal fusion and low boundary segmentation precision in existing pulmonary nodule MRI segmentation. The method comprises the following steps: preprocessing a lung multi-modal MRI image and generating a pulmonary nodule multi-modal attention feature map; constructing a segmentation network with attention jump connection and a cross-modal fusion network based on a conditional diffusion model, and forming a dual-branch joint optimization framework of segmentation and fusion decoupling; and end-to-end training is completed through a joint loss function including segmentation loss, fusion loss and consistency loss. According to the method, the pulmonary nodule segmentation precision and the model robustness are remarkably improved, and the method is suitable for accurate diagnosis scenes of clinical pulmonary nodules.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention belongs to the technical field of medicine, and discloses a pulmonary nodule intraoperative positioning system based on a flexible array sensor, which comprises the flexible array sensor arranged at a to-be-detected part of a subject and used for collecting a compression signal and obtaining stress distribution; the nodule area detection unit is used for determining a nodule area through clustering analysis according to the stress distribution; the morphological optimization unit is used for performing morphological optimization on the determined nodule region; the feature extraction unit is used for extracting related geometric features based on the optimized nodule region; and the visualization unit is used for visually displaying the stress distribution, the optimized nodule region and related geometric features. The method can assist doctors in accurately positioning the pulmonary nodules, and meanwhile, quantitative features of the nodules are provided.

The invention provides an uncertainty perception pulmonary nodule segmentation method based on a multi-branch collaborative network, and the method introduces an expert conflict perception mechanism, and enables a model to learn consensus information and divergence features between scorers at the same time through constructing a probability graph soft label and a conflict graph based on multi-scorer labeling. Therefore, the uncertainty existing in clinical practice can be reflected more comprehensively; a multi-branch collaborative segmentation network structure is provided, an encoder-decoder trunk is combined with three functional branches of consensus, conflict and boundary, and adaptive weighted integration is realized through a conflict regulation and control fusion module. The method not only improves the overall precision of the segmentation result, but also can generate probability distribution prediction which better meets the actual diagnosis demand under the condition that the opinions of multiple scorers are inconsistent, and provides a more reliable basis for clinical auxiliary decision making.

The invention relates to the field of medical image processing, in particular to a pulmonary nodule natural orifice optimal path intelligent planning system which comprises a data preprocessing module, a bronchus three-dimensional segmentation module, a pulmonary nodule segmentation module, a differential geometric path search module, a path evaluation module and a doctor interaction module. The differential geometric path search module comprises a Riemannian curvature flow modeling unit, a multi-scale Gaussian curvature constraint unit and a geodesic curvature self-adaptive correction unit, can model bronchus into a Riemannian manifold, applies curvature flow optimization, and combines a multi-scale decomposition strategy and a geodesic theory to obtain a geodesic curvature self-adaptive correction model; precise description and optimal path planning of bronchial geometrical characteristics are realized, the path evaluation module provides multi-dimensional evaluation of blood vessel avoidance, diameter adaptation and curvature safety, and the doctor interaction module supports three-dimensional visualization and real-time path editing. The problem that a traditional Euclidean space algorithm is insufficient in description of complex forms of bronchus is solved by introducing a differential geometry theory.

The invention relates to the technical field of audio signal processing. The invention provides a method, system and device for predicting pulmonary nodules through sound and a storage medium. The method comprises the following steps: in a quiet test environment, directionally collecting a sound signal of a patient through an audio sensor; wherein the sound signals comprise breath sounds, cough sounds, vowels and sentences; performing noise reduction processing on the collected sound signals to obtain a noise reduction data set; extracting multi-dimensional voiceprint features from the noise reduction data set, and constructing a sound feature matrix; and performing multi-modal fusion analysis on the sound feature matrix and clinical data of the patient, inputting the sound feature matrix and the clinical data into a sound prediction model optimized by a focus loss function, and outputting a pulmonary nodule risk prediction result and a visual diagnosis report. The problems that the misdiagnosis and missed diagnosis rate is high, the radiation risk is large and the equipment dependence is strong when the pulmonary nodules are examined by the existing iconography, and the characteristic analysis is single, the noise immunity is poor and the screening accuracy is insufficient in the sound detection technology are solved.

The invention provides a pulmonary nodule patient treatment prediction method based on artificial intelligence, and the method comprises the steps: obtaining a historical tongue picture feature and a historical pulmonary nodule CT image, extracting a tongue coating color value RGB component, a spatial frequency of a tongue surface texture feature, a gradient value of pulmonary nodule edge sharpness, and a CT value standard deviation of pulmonary nodule density distribution, and obtaining an initial feature set; extracting the change trend of the tongue coating thickness and the pulmonary nodule edge sharpness in the treatment cycle from the weighted feature set, analyzing the dynamic evolution rule of the correlation between the change rate of the tongue coating thickness and the gradient value of the pulmonary nodule edge sharpness, and obtaining a time sequence feature vector in combination with the time rhythm characteristics of the traditional Chinese medicine tongue diagnosis; and carrying out standardization and normalization processing on the fusion feature set, predicting by combining a clinical curative effect evaluation standard and a pulmonary nodule iconography improvement index to obtain the correlation strength of the change rate of the thickness of the tongue coating and the improvement trend of the edge sharpness of the pulmonary nodule in a treatment cycle, and obtaining a predicted curative effect result.

The invention relates to the technical field of medical image processing, in particular to a pulmonary nodule follow-up visit intelligent management system which comprises a pulmonary nodule intelligent matching and change analysis engine, a pulmonary nodule intelligent follow-up visit scheme generation engine, a cloud storage and collaborative service module, an early warning module and a user management and operation module. The surface of the pulmonary nodule is modeled into a differential manifold, accurate matching of the pulmonary nodule and quantitative analysis of small changes are achieved through a multi-scale differential invariant feature extraction framework and a geodesic distance-based change analysis technology, the system generates a personalized follow-up visit scheme based on a risk assessment model, and through a multi-stage early warning mechanism and multi-channel notification distribution, the risk assessment accuracy of the pulmonary nodule is improved. The system also adopts a GPU acceleration computing architecture, so that the processing efficiency is remarkably improved, multi-terminal cooperative operation is supported, the accuracy, intelligence and individuation of pulmonary nodule follow-up visit management are realized, and the follow-up visit efficiency and the early detection rate of lung cancer are improved.

The invention relates to a lung elastography and AI-assisted pulmonary nodule benign and malignant identification system and a medium, and solves the problem of limited identification accuracy of a traditional iconography examination method. The system comprises at least one processor which is configured to realize the lung elastography and AI-assisted pulmonary nodule benign and malignant identification method when executing a computer program, and the specific steps are as follows: inputting obtained comprehensive consistency indexes, individual characteristics of patients and key information in multi-source data into a pre-trained correlation model; outputting the prediction probability that the case pulmonary nodule is malignant; based on the prediction probability and the initial confidence coefficient, adopting a preset confidence coefficient adjustment algorithm to calculate the adjusted confidence coefficient; and displaying the pulmonary nodule benign and malignant judgment result, the confidence coefficient, the multi-physical field simulation result and the comprehensive consistency index on a display terminal in a multi-modal mode. The method has the advantages that the accuracy of identifying benign and malignant pulmonary nodules is improved, and the limitation of a traditional method is made up.

This invention provides a lung stem cell peptide for treating pulmonary nodules, its preparation method, and its application, belonging to the field of biopharmaceutical technology. The invention involves inducing iPSCs to differentiate into lung stem cells; culturing the lung stem cells, centrifuging, collecting the supernatant to obtain crude lung stem cell solution; mixing the crude lung stem cell solution with a protease for enzymatic hydrolysis, centrifuging, concentrating, and spray-drying to obtain the lung stem cell peptide for treating pulmonary nodules. This invention optimizes the composition of each culture medium to induce iPSCs to differentiate into lung stem cells, then extracts and separates lung stem cell peptides from these cells. The extracted lung stem cell peptides are intravenously reinfused into the lungs, effectively inhibiting the formation of pulmonary tumor nodules, significantly reducing the number of pulmonary tumor nodules, and without toxic side effects or adverse reactions.

The invention provides computer equipment for executing a sub-solid pulmonary nodule growth prediction method based on CT imageomics, and the computer equipment comprises a memory, a processor and a computer program, and when the processor executes the program, density value extraction is carried out on a sub-solid pulmonary nodule region through multi-stage CT images, the internal structure of a nodule is segmented by adopting a convolutional neural network, and the internal structure of the nodule is obtained; obtaining nodule internal density distribution data from the segmentation result; calculating a cavity expansion rate and an edge density increase rate in unit time by combining the density amplitude data according to a focus characteristic influence degree evaluation result; according to the lesion cavity expansion rate and the edge density growth rate, the malignant progress risk level is judged, and malignant progress time window estimation is obtained; according to the malignant progress time window estimation, the matching degree between the density distribution data and the development speed index is calibrated through clinical feedback data, and sub-solid pulmonary nodule growth prediction output is obtained.

The invention relates to the technical field of image registration, in particular to a dose CT scanning lung nodule dynamic monitoring system. The method comprises the following steps: obtaining an edge line cluster containing all lung images according to position distances corresponding to all contour points among edge lines in all lung images; according to the position distribution of all contour points in different lung images, obtaining a plurality of groups of same-position contour points; according to the position relation between each group of co-location contour points and other groups of co-location contour points with the same order angle number on the lung image and the fluctuation characteristics of the order angle corresponding to each group of co-location contour points, obtaining the reference of each group of co-location contour points, and screening out anchor points; constructing a relative position model of the nodule area in each lung image according to the position distribution of the anchor point and the nodule area in each lung image; registration of lung images is achieved, and dynamic monitoring is conducted on lung nodules. According to the method, the lung image of the patient is subjected to elastic registration, so that the accuracy of dynamic monitoring of the lung nodule is improved.

The invention discloses a three-dimensional pulmonary nodule target detection method and system based on a neural network. The method comprises the steps of obtaining a pulmonary nodule three-dimensional CT image and preprocessing the image to obtain a training set and a test set; a target detection model eFATE-Net is constructed, and training and evaluation are carried out; and preprocessing a to-be-detected three-dimensional CT image, and inputting the preprocessed to-be-detected three-dimensional CT image into the trained eFATE-Net to realize target detection. A false positive reduction module is introduced into the eFATE-Net, so that false positive misinformation of pulmonary nodules is reduced on the premise of keeping high detection sensitivity. On the basis of the fine-grained spatial features and the coarse-grained semantic features of the pulmonary nodules, global dependency modeling is carried out through a self-attention mechanism to reconstruct and enhance the deep semantic features of the pulmonary nodules so as to further reduce false positive and false alarm of the pulmonary nodules. A gating attention mechanism is introduced to improve key feature selectivity, the response of the model to key features of the pulmonary nodule is enhanced, and the detection performance of the pulmonary nodule is improved.

The present application relates to a kind of lung nodule early warning method and system based on multi-model fusion, belong to health management field.Therein, the method includes collecting lung nodule CT image and implementing standardization processing, generates lung nodule analysis image;The feature recognition of lung nodule analysis image is executed to generate the lung nodule label image with multi-dimensional pathological feature label, and the lung nodule time series data set is structured;Lung nodule label image and lung nodule time series data set are used to construct lung nodule fusion prediction model and output lung nodule relabeling pathological image, lung nodule fusion prediction model integrates time series probability prediction model, learning supervision model and relabeling model;Based on lung nodule relabeling pathological image, construct three-dimensional convolutional neural network early warning model, quantify deterioration risk coefficient, when deterioration risk coefficient breaks through preset alert threshold, trigger clinical early warning mechanism, the present application realizes the relabeling of patient lung nodule potential state by fusion model, completes lung nodule early warning.

The present invention relates to a thoracoscopy simulation apparatus and method for performing a simulation that includes generating a 3D atelectasis model on the basis of a CT lung image and displaying the location of a pulmonary nodule and a safe resection margin. The thoracoscopy simulation method according to the present embodiment is a thoracoscopy simulation method in which at least a portion of each step is performed by a processor, and may comprise the steps of: generating a 3D lung model in which a pulmonary nodule is displayed, the 3D lung model being generated on the basis of a chest CT image of a patient in an inspiratory state; changing the 3D lung model to generate a 3D atelectasis model in an expiratory state; generating a 3D thorax model using the 3D atelectasis model and the location of the ribs included in the chest CT image; and positioning the 3D thorax model in a virtual space and generating a simulation image on the basis of the 3D thorax model and the tracked locations of a thoracoscope and a surgical tool.

The utility model discloses a pulmonary nodule positioning needle releasing device in the technical field of pulmonary nodule positioning needles, which comprises an outer tube, a handle is mounted at one end of the outer tube, an inner tube penetrates through the inner side of the handle, a connecting rod penetrates through the inner tube, and a positioning piece is placed at one end of the connecting rod and located on the inner side of the inner tube. A limiting piece is installed on the inner side, close to one end, of the inner tube, the outer tube is driven by the handle to be inserted into the body of a patient, then the inner tube is inserted into the inner side of the outer tube to slide, the positioning piece at one end of the inner side of the inner tube is pushed by the connecting rod to be placed in lung tissue, and the positioning piece is limited and fixed by the limiting piece. The inner tube is limited and fixed through the fixing piece, the inner tube is prevented from shaking or shifting, and the problems that the stability of supporting of the positioning needle is low, the positioning needle is prone to shifting or shaking due to lung tissue during release, the limiting effect of the inner tube structure on the inner side of the puncture tube is poor, and convenience and practicability of release and position adjustment of the positioning needle are affected are solved.

The invention discloses a tumor specific signal analysis device and equipment for uncertain pulmonary nodules and a storage medium, and relates to the technical field of electrical digital data processing.The device constructs a LungTCR database containing lung cancer tissue enrichment type and blood enrichment type CDR3 sequences through TCR beta chain sequencing and IMGT database comparison, and the LungTCR database is used for analyzing the lung cancer tissue enrichment type and blood enrichment type CDR3 sequences. A basis is provided for tumor specific signal recognition; a Needleman-Wunsch algorithm and an editing distance threshold value are adopted for screening, so that high-precision sequence matching is realized; through quantification of lung cancer tissue scores, blood scores and mutation specificity scores, a TCR feature group with biological significance is formed; and finally, the TCRnodseek plus model trained by clinical and image features is combined, a reliable malignant probability prediction value can be output, the model AUC can reach 0.90 or above, and the model is obviously superior to traditional imaging diagnosis and tumor markers.

The utility model relates to the technical field of medical instruments, in particular to a CT (computed tomography)-guided pulmonary nodule positioning needle which comprises a puncture needle, a rotating component inserted into the top end of a needle handle and comprising a limiting block, an insertion hole formed in one end of a fixing sleeve, the needle handle inserted into the insertion hole, and a rotating shaft sleeved at the other end of the fixing sleeve. The limiting block is arranged on the upper portion of the rotating shaft, a positioning assembly is fixedly installed on one side of the limiting block and comprises a U-shaped fixing plate, the two ends of the U-shaped fixing plate are fixedly installed at the two ends of the limiting block, the supporting plate is welded to one side of the U-shaped fixing plate, and one ends of the vertical clamping plates are fixedly installed at the two ends of the supporting plate. The two ends of the transverse clamping plate and the other end of the vertical clamping plate are fixedly installed, the protractor is fixedly installed on the top of the fixing shaft, and a clamping assembly is fixedly installed on the other side of the limiting block. Through the use of the rotating assembly and the positioning assembly, the angle and the position of the puncture needle are accurately controlled, and the accuracy of pulmonary nodule positioning is improved.

The invention discloses application of ceftriaxone sodium in preparation of a medicine for preventing and / or treating pulmonary nodules, and belongs to the technical field of tumor prevention and treatment, ceftriaxone sodium is converted into inhalable aerosol particles through an aerosol inhalation device, targeted delivery of the medicine to the lung can be achieved, systemic adverse reactions caused by systemic administration are remarkably reduced, and the curative effect of the medicine is improved. Therefore, the medicine has a treatment effect on pulmonary nodules. Experimental results in the early stage prove that ceftriaxone sodium with different concentrations is subjected to aerosol inhalation to intervene in a mouse pulmonary nodule model induced by uratan, and the ceftriaxone sodium has a good prevention and treatment effect. The research can provide new experimental basis and reference for development of lung nodule related prevention and treatment medicines, provides new technical ideas for prevention and treatment of other diseases, and has important academic value and potential of further development and research.