168 results about "Tumor detection" patented technology

The invention provides a tumor detection data analysis method based on a peripheral blood immune marker, and belongs to the technical field of immunodetection.The tumor detection data analysis method comprises the steps that firstly, a peripheral blood sample of a patient is collected, and plasma and cell components are separated; analyzing various immune cell phenotypes by using flow cytometry; determining the concentration of key cell factors; analyzing the molecular expression level of the immune checkpoint; establishing an immune cell subset proportion empirical function; analyzing a cell factor expression mode by using an immune lineage transcriptome analysis model; establishing a tumor immune microenvironment score by combining the immune cell subset proportion and a model output result; the accuracy of the scoring system is verified through clinical follow-up visit and iconography examination; a standardized report template is formulated to provide a tumor early detection basis. Wherein the immune lineage transcriptome analytical model adopts a multi-modal attention fusion network architecture, three different immune data streams can be processed and integrated at the same time, and accurate evaluation of tumor-related immune states is realized.

The invention discloses a bioelectrical impedance tumor detection method based on pattern recognition, and the method comprises the following steps: S1, carrying out the electrical impedance measurement of a to-be-detected tumor region through a bioelectrical impedance analysis instrument, and obtaining the current, voltage and impedance values at different depths and frequencies; s2, designing a feature extraction module, and extracting key features in the electrical impedance data of each channel under different frequencies; s3, inputting the extracted key features into a multi-level feature fusion module, and fusing the electrical impedance features under different channels and frequencies to form new fused features; and S4, inputting the fusion features into a self-adaptive classification module, and adjusting model parameters in combination with a self-adaptive optimization method. According to the invention, automatic processing and classification are carried out on data through a pattern recognition algorithm, and the accuracy, sensitivity and reliability of tumor detection can be effectively improved in combination with the feature extraction module, the multi-level feature fusion module and the adaptive classification module.

The invention discloses an enhanced interpretable multi-mode esophageal cancer target detection method and system, and the method comprises the steps: collecting a medical image of an energy spectrum CT detection esophageal tumor, and carrying out the marking processing of the obtained CT image; according to the marked CT image, carrying out multi-modal classification on the medical image and the clinical data information; constructing a fusion model based on multi-modal deep learning to perform fusion processing on the multi-modal features obtained by classification; according to fusion features obtained by fusion, generating an esophageal tumor detection report based on weak supervision contrast learning; constructing an intelligent detection cloud platform by taking the optimized AI diagnosis model as a kernel; the intelligent detection cloud platform is used for transmission, storage management, image data preprocessing, automatic detection and query statistics of energy spectrum CT images. According to the embodiment of the invention, the accuracy and robustness of image segmentation can be improved, efficient and accurate automatic detection of esophageal tumors is realized, and the method can be widely applied to the technical field of computers.

The invention provides a tumor real-time tracking and benign and malignant detection method based on a mammary gland ultrasonic video, and the method comprises the steps: collecting the mammary gland ultrasonic video in real time, and inputting the mammary gland ultrasonic video into an UltraNet basic detector to generate a detection result of each frame of ultrasonic image; calculating a similarity probability matrix Si, j to reflect the similarity between the ith target and the jth target; performing cascade matching on the target based on the similarity probability matrix Si, j to form a target track pipeline Ti; and carrying out confidence coefficient updating and bounding box position updating on the detection result in each target track pipeline Ti, and finally outputting a breast lesion detection result of each frame of ultrasonic image. The tumor real-time tracking and benign and malignant detection method based on the breast ultrasonic video has the advantages of high calculation efficiency, high precision and strong robustness, can assist a doctor in accurately positioning a focus and performing diagnosis, and is beneficial to early discovery of breast diseases.

The invention provides a tumor recognition method based on multi-modal feature fusion, relates to the technical field of tumor recognition, and is used for solving the problems of missing multi-modal feature calibration, insufficient semantic association, poor generalization and insufficient clinical interpretability in the prior art. The method comprises the following steps: firstly, synchronously acquiring visual structure and electrical impedance bimodal data through an event-driven sensor array, and completing preprocessing through pulse coding, STDP rule optimization and quality verification; then, respectively extracting structured feature vectors of a tumor visual structure class and characteristic feature vectors of a numerical attribute class by a heterogeneous twin coding engine, and unifying dimensions and distribution through processes such as multi-scale dynamic perception and cross-modal calibration; then based on an attention mechanism, InfoNCE contrast loss and minority class weight gain, dynamic weighted fusion and semantic association enhancement are performed on the bimodal features, and a comprehensive feature vector is generated; and finally, through clinical logic adaptation and multi-center deviation correction, outputting a tumor benign and malignant identification result through a full-connection classifier, and synchronously generating a clinical interpretable report containing key features and weights. According to the method, the complementary advantages of bimodal information are effectively integrated, the problems of heterogeneous multi-center equipment, unbalanced samples and the like are solved, the tumor recognition accuracy and the early-stage tiny tumor detection rate are improved, the computing power consumption is reduced, edge medical equipment deployment is adapted, and the requirements for low misjudgment and traceability of clinical diagnosis are met.

The present invention discloses a method for detecting anti-tumor by bioelectrical impedance based on pattern recognition, which includes the following steps: S1: Use a bioelectrical impedance analysis instrument to measure the impedance of the tumor area to be detected, and obtain the current, voltage and impedance values at different depths and frequencies; S2: Design a feature extraction module to extract the key features in the impedance data at different frequencies of each channel; S3: Input the extracted key features into a multi-level feature fusion module to fuse the impedance features at different channels and frequencies to form new fused features; S4: Input the fused features into an adaptive classification module and adjust the model parameters by combining an adaptive optimization method. The present invention can automatically process and classify data through a pattern recognition algorithm, and at the same time, by combining a feature extraction module, a multi-level feature fusion module and an adaptive classification module, it can effectively improve the accuracy, sensitivity and reliability of tumor detection.

The invention provides a high-precision lightweight breast ultrasonic tumor detection method based on Improved-YOLOv8, and the method comprises the steps: obtaining a breast ultrasonic image, carrying out the preprocessing, and obtaining a preprocessed image; performing dynamic data enhancement to obtain an enhanced image; a fast network Faster Net and a self-adaptive wavelet down-sampling layer AWFD are introduced, a backbone network AWFD-Faster Net of an Improved-YOLOv8 network extracts features of an enhanced image, and then three image features are output to a fusion network Fusion; the fusion network Fusion carries out feature fusion on the input image features and then outputs three fusion features to the detection head network Head; the detection head network Head outputs an ultrasonic breast tumor detection result; the method has the advantages of short operation time, high calculation efficiency, high detection precision and the like, and meanwhile, the method is small in network parameter quantity and low in calculation complexity.

The embodiment of the invention provides a cancer computer-aided diagnosis method and system, an esophageal cancer computer-aided diagnosis method and a gastric cancer computer-aided diagnosis method.The tumor detection method comprises the steps that a CT image processing task is received, the CT image processing task carries a plurality of CT images corresponding to a target detection area, and the target detection area corresponds to the CT images; the CT image processing task is used for detecting whether a tumor exists in the target detection area; the multiple CT images are input into a CT image processing model, a detection result corresponding to the target detection area is obtained, the CT image processing model generates the detection result corresponding to the target detection area based on the multi-scale feature information corresponding to the multiple CT images, and the detection result corresponding to the target detection area is obtained. The detection result comprises detection annotation information, detection category information and a detection guidance text. The accuracy of subsequently generated detection results is improved by obtaining the multiple pieces of scale feature information corresponding to the CT image. The detection result comprises the position information of the abnormal object, the information of the to-be-detected object and the guidance text, the detection result is enriched, multi-dimensional detection information is provided for the user, and the use experience of the user is improved.

The invention provides a prediction or diagnosis system for myeloid tumors and application, and belongs to the technical field of tumor detection. The research finds that the intensity levels of Raman peaks 786 and 1579 cm <-1 > representing nucleic acid, Raman peaks 643, 759, 103, 126, 1603 and 1616 cm <-1 > representing protein, Raman peaks 1437, 1443 and 1446 cm <-1 > representing lipid, Raman peak 957 cm <-1 > representing beta-carotene and Raman peak 1345 cm <-1 > representing collagen in serum are detected as biomarkers, and the serum can be used for predicting or diagnosing myeloid tumors and subtypes thereof. The myeloid tumor and the subtype thereof can be quickly and noninvasively predicted or diagnosed by utilizing the myeloid tumor prediction or diagnosis system, more choices are provided for quickly predicting or diagnosing the myeloid tumor and the subtype thereof, and the system has profound scientific research and clinical significance.

The invention provides a MaskRcnn-based tumor detection method and system, and the method comprises the steps: inputting medical image data into a MaskRcnn network integrated with an edge perception module, generating an edge response graph through a Sobel operator, and enabling the edge response graph to serve as an additional channel injection feature graph, and enhancing the boundary representation; fusing multi-scale features through a feature pyramid network FPN, extracting candidate tumor area features through RoI Align, introducing non-local attention modeling global dependence in the area, extracting directional entropy, texture energy, uniformity and contrast in combination with a gray level co-occurrence matrix GLCM, and splicing to generate structure sensing features; performing classification, bounding box regression and mask segmentation based on the structure perception features, and constructing a joint loss function including classification, regression, mask cross entropy, edge alignment and structure consistency; the positioning capability of the model on the fuzzy tumor contour is effectively improved, the boundary positioning error is remarkably optimized, and the defect that over-segmentation or missing detection is likely to occur in a traditional model is overcome.

This invention relates to the field of data analysis technology, specifically to a cloud-based tumor detection data analysis and sharing system. The invention includes a cloud service platform and multiple local terminals. The local terminals communicate with the cloud service platform via a network and are deployed in various medical institutions. They digitize tumor detection samples to generate raw medical data and extract metadata. The cloud service platform includes a data receiving module for receiving raw data and metadata; a template management module for building and maintaining a feature template library, including a two-stream ensemble classifier; a data calibration module for calling standardized parameter sets from metadata and generating standardized data through a reversible neural network; a data analysis module for analyzing the standardized data; and a data sharing module for responding to requests to share data. The system solves the problem of heterogeneity in multi-center data, improves the model's generalization ability and robustness, and achieves efficient data sharing and accurate analysis.

The invention relates to a radionuclide labeled trimer probe 68Ga-TRAP-(FAPI) 3 targeting FAP and a preparation method thereof, Trap is used as a trivalent chelating platform, and three FAPI targeting units are covalently connected by clicking a chemical linking arm to form a trimer structure; the invention further relates to application of the 68Ga-TRAP-(FAPI) 3 probe in preparation of imaging agents or therapeutic drugs for diagnosing FAP positive diseases, experiments prove that the trimer probe is good in affinity and high in stability, and in cell experiments and animal experiments, the uptake rate of FAP positive cells is kept at a high level within 240 minutes; preclinical and preliminary clinical applications show that the tumor detection rate of the < 68 > Ga-TRAP-FAPI 3 in various cancer models is superior to that of < 18 > F-FDG, and compared with < 68 > Ga-FAPI-04, the < 68 > Ga-TRAP-FAPI 3 is clearer to display focuses (particularly tiny metastases), higher in uptake intensity and particularly outstanding in delayed imaging, and the < 68 > Ga-TRAP-FAPI 3 can be used for preparing a medicine for treating cancer. And a novel drug candidate with better performance is provided for precise diagnosis and staging of tumors and subsequent radionuclide targeted therapy.

A prostate-specific membrane antigen (PSMA)-targeting conjugate having a phosphonate backbone, which is used for tumor detection after chelation of a diagnostic nuclide and is used for radiation therapy after being combined with a treatment-type nuclide. A conjugate nuclide chelation part is replaced with a fluorescent probe or a cytotoxic drug so that the PSMA-targeting conjugate can then be used for diagnosis and treatment of diseases.

The invention relates to a DNA methylation detection method and a reagent. Specifically, the invention relates to a method for diagnosing whether a tumor exists in a subject and whether a tumor postoperative minimal residual focus exists or not by detecting a methylation marker in free DNA in a sample from the subject to judge the methylation level of the DNA, and judging the postoperative prognosis of the subject suffering from the tumor. Methods of predicting postoperative recurrence or assessing the efficacy of a treatment on a subject having a tumor. The invention also relates to a marker, a kit and a detection reagent for the method.

The invention provides a microsatellite site stability detection method and an electronic device thereof. The microsatellite locus stability detection method comprises the following steps: S1) utilizing high-throughput targeted sequencing data of a sample to be detected to obtain feature data in a preset microsatellite locus set, the feature data at least comprising length variation of a short tandem repeat sequence and structural variation occurring at a microsatellite locus; according to the microsatellite stability detection method and device, the problem that in the prior art, a method for detecting microsatellite instability has limitation can be solved, and the microsatellite stability detection method and device are suitable for the field of tumor detection.

The invention provides a digestive tract tumor detection method and system, relates to the field of medical image detection, and solves the technical problem of low accuracy of tumor boundary detection and automatic positioning in a digestive tract tumor image acquisition process in the prior art. The method comprises the following steps: taking a pan-tumor region image as a training sample of a binary decision tree algorithm, constructing an objective function based on the training sample to obtain a first objective function value, and obtaining a depth threshold value and an optimal classification point of the training sample; optimizing the optimal classification point based on the candidate feature sound to obtain a second objective function value; an unknown digestive tract tumor image is accessed, image points of the digestive tract tumor image are marked through a trained binary decision tree algorithm and a regression forest algorithm, the position distribution probability of the image points is generated, a position distribution thermodynamic diagram is generated based on the position distribution probability, and a tumor core area is obtained through mean shift. And completing the separation of the tumor region and the non-tumor region. The application is used in the digestive tract tumor detection process.

The invention relates to a composition and a kit for detecting tumor-derived tEVs and application of the composition and the kit, and belongs to the technical field of biology. In order to solve the problems of insufficient sensitivity, poor specificity and complex operation of the existing tumor-derived extracellular vesicles (tEVs) detection technology, the invention provides a composition which is composed of double aptamers for specifically recognizing PD-L1 and EpCAM proteins on the surface of the tEVs, a connecting sequence, an annular template, a branch primer, crRNA and a fluorescent probe and is combined with T4DNA ligase, Phi29 polymerase and a CRISPR / Cas12a system. The method is applied to rapid detection of tEVs. Good application prospects are shown in liquid biopsy and dynamic monitoring. Therefore, high-sensitivity and high-specificity tumor tEVs detection is realized, and a reliable tool is provided for early diagnosis of cancers.

The present invention relates to the technical field of medical image processing, and particularly to a method and system for detecting brain tumors in nuclear magnetic resonance (NMR) images. The method includes: obtaining an initial NMR brain tumor image, using a double convolutional cross-stage division network as the backbone network, and using a partial visual state space network as the feature enhancement network to lightweight the YOLOv8 model while improving its performance; in the improved YOLOv8 model, constructing a reparameterized dynamic selectable large kernel convolutional network as the multi-scale feature interaction network in the neck structure, and further improving the performance of the YOLOv8 model using reparameterization to obtain an improved YOLOv8 model; using the initial image dataset to perform iterative optimization training on the improved YOLOv8 model to obtain an NMR image brain tumor detection model; inputting the initial image dataset into the NMR image brain tumor detection model for detection, and outputting the NMR brain tumor detection result. The present invention can improve the accuracy and efficiency of brain tumor detection.

The invention discloses a tumor detection device and method based on a diamond quantum sensor, and belongs to the technical field of biomedical detection. The device comprises an optical fiber laser, an optical fiber beam splitter, an optical fiber attenuator, an optical fiber circulator, an optical filter, two photoelectric detectors, a differential amplifier, an electronic filter, a computer, a signal generator, two microwave antennas and an optical fiber probe. The optical fiber probe comprises an optical fiber packaged in a plastic shell, a diamond quantum sensor which is fixed at the tail end of the optical fiber and comprises an NV color center, and a first microwave antenna integrated beside the optical fiber. The laser excites the diamond to generate fluorescence, meanwhile, the signal generator drives the two microwave antennas to apply a microwave field, and the dielectric property of the sample to be detected modulates the fluorescence intensity of the diamond by influencing the microwave field. Laser noise is eliminated through a differential amplification and filtering technology, and fluorescence signal change is analyzed by a computer, so that dielectric constant distribution of a sample is reconstructed, and non-invasive and high-sensitivity detection of tumors is realized.

1. Name of the design product: Diagnostic instrument (for gynecological and urological tumors). 2. Use of the design product: This design product is used for the detection of gynecological and urological tumors. 3. Design key points of the design product: Lies in the shape. 4. Picture or photo that best shows the design key points: Perspective view.

The invention discloses a nano antibody specifically combined with CD176 protein and application thereof, the nano antibody sequentially comprises a CDR1 region, a CDR2 region and a CDR3 region, the amino acid sequence of the CDR1 region is shown as SEQ ID No.6, the amino acid sequence of the CDR2 region is shown as SEQ ID No.7, and the amino acid sequence of the CDR3 region is shown as SEQ ID No.8. The invention further discloses a preparation method of the nano antibody specifically combined with the CD176 protein and application of the nano antibody specifically combined with the CD176 protein. The invention discovers that the nano antibody with the three complementary determining regions can be combined with CD176 protein (EC50 is 8.617 nM) with high affinity, has the characteristics of small molecular weight, small immunogenicity, better solubility and stability and the like, can effectively detect the expression of CD176 or inhibit the activity of CD176, and has potential value in the aspects of tumor detection and treatment.

The present invention discloses a medical imaging brain tumor detection method and system based on an improved Faster R-CNN. The method comprises: obtaining a magnetic resonance image dataset from a database; preprocessing the images of the dataset using an optical flow method and a generative adversarial network to perform data enhancement; constructing an improved Faster R-CNN deep network model; using the obtained dataset to train the constructed improved Faster R-CNN deep network model; inputting the magnetic resonance image to be predicted into the trained network model, and outputting the brain tumor target detection result. The present invention adopts a data enhancement method based on an optical flow method and a generative adversarial network, and designs an improved Faster R-CNN target detection network model, adds an image feature pyramid structure and a convolutional gated recurrent unit (ConvGRU) module, modifies the data reading method during network training, enhances the continuity of tumor detection results, and improves the retrieval accuracy and recall rate of brain tumors.

The invention relates to the technical field of tumor detection equipment, in particular to a tumor sample detection device convenient to replace and adjust. Comprising a constant temperature and humidity chamber, a workbench, a sealing cover plate, a push-out cover plate, a tumor sample fixing assembly, a tumor sample detection assembly, a tumor sample position adjusting assembly, a tumor sample push-out assembly, a cover plate linkage adjusting assembly and an overall driving assembly. By using the tumor sample fixing assembly, tumor samples of different sizes and shapes can be conveniently fixed, and diversified sample detection requirements can be met; by using the tumor sample detection assembly, the scheme is suitable for the requirements of different detection items on the sample detection depth, and the application range of the tumor sample detection device is widened; through cooperative use of the constant-temperature and constant-humidity chamber, the tumor sample fixing assembly and the workbench, conversion from storage to detection of tumor samples can be rapidly completed in a short time, and the sample treatment time is greatly shortened.

The present invention belongs to the technical field of crotonate dyes, and in particular to a near-infrared second-region crotonate dye and its preparation method and application. The near-infrared second-region crotonate dye having a structure shown in Formula 1 provided by the present invention introduces an electron-donating group into the crotonate structure to expand the π conjugation degree of the system, and has a donor-acceptor-donor structure to form a rigid resonant plane structure that stabilizes the zwitterion, effectively regulating the absorption and emission wavelengths of the dye, and red-shifting it to a near-infrared region with a longer wavelength. Thus, the crotonate dye having a structure shown in Formula 1 provided by the present invention has good absorption properties in the near-infrared second region and can be used for photoacoustic imaging of tumor detection; at the same time, it has good fluorescence quantum yield and can be used for fluorescence imaging of tumor detection; and the near-infrared second-region crotonate dye provided by the present invention also has good photothermal conversion efficiency and can be used for photothermal therapy for tumor treatment.

The invention belongs to the technical field of radiography, and particularly relates to a tumor specific imaging probe and a preparation method thereof. The invention provides a novel bimodal imaging probe activated by gamma-glutamyltranspeptidase (GGT). The bimodal imaging probe synergistically integrates the functions of magnetic resonance imaging (MRI) and fluorescence imaging (FI). The probe is meticulously designed, and chiral Gd-DOTA and Cy5 fluorophores are connected to a polymer support through covalent bonds. The molecular structure comprises a GGT responsive part, and selective activation in a tumor microenvironment is ensured, so that the specific targeting ability to tumors is enhanced. Cy5 is a near-infrared fluorescent dye with definite characteristics, and when Cy5 is introduced into the probe, extremely high sensitivity is provided for fluorescence-based tumor molecular level visualization. Due to the high-performance magnetic resonance imaging function and the high-sensitivity fluorescence imaging function, the bimodal probe is endowed with extremely high accuracy in tumor detection and treatment monitoring.

This invention belongs to the field of precision oncology diagnosis and treatment technology, specifically involving a nanomedicine delivery system with tumor microenvironment regulation capabilities, its preparation method, and its application. S1: An organic phase is prepared by dissolving ionizable lipids, helper phospholipids, cholesterol, and functionally modified lipids in a solvent at a molar ratio of 50:38.5:10:1.5. S2: Capsaicin is dissolved in the organic phase obtained in S1, and doxorubicin is dissolved in the solvent to prepare an aqueous phase. The organic and aqueous phases are rapidly mixed in a microfluidic device at a volume ratio of 3:1 to obtain a mixture. S3: The mixture obtained in S2 is placed in a dialysis bag and immersed in Tris buffer for magnetic stirring and dialysis, followed by concentration to obtain (C+D)@LNPPRT. This invention significantly enhances the precision detection of tumors, enables efficient early tumor detection and targeted chemotherapy, and improves treatment efficacy.

The application provides a fluorescence in situ hybridization image breakage and fusion collaborative determination method and device and medium, in order to solve the problems of FISH image breakage and fusion detection fragmentation, insufficient robustness and high misjudgment rate, after separating the cell image channel, the application calculates a binary threshold value based on an adaptive extreme value ratio, combines a connected domain median area filter to extract independent fluorescent points and an equivalent diameter, generates a cell nucleus mask based on fusion morphology and appearance characteristics, performs two-way collaborative determination, globally determines that heterochromatic fluorescent points with a distance less than a fusion threshold value are fused and physically removed, and then connects the remaining points in the same cell nucleus by using a global optimal pairing algorithm, and determines that the breakage threshold value based on the equivalent diameter is greater than the distance dynamically set, and finally outputs a comprehensive classification state. The application is used for precise determination of tumor FISH detection.

The invention discloses a tumor detection marker and application thereof in medicine preparation, and belongs to the technical field of biomedicine. Clinical sample qRT-PCR (quantitative real-time polymerase chain reaction) shows that ENST00000412203.1 is obviously up-regulated in breast cancer tissues, the area (AUC) under the ROC curve reaches 0.9822, and the diagnosis efficiency is excellent. In addition, the invention further finds that the expression of the polypeptide in lymph node metastasis tissues is obviously improved, and metastasis specificity is displayed. Meanwhile, the expression of ENST00000412203.1 is knocked down through siRNA, it is found that proliferation, migration and invasion of breast cancer cells are remarkably inhibited, and therefore the siRNA can be used for preparing targeted therapy drugs for breast cancer and has important clinical application value.

Provided herein are methods for measuring concentration of circulating cell free DNA (cfDNA) from a sample of a subject, for the purposes of cancer or tumor detection and / or characterization.

We disclose a composition, comprising a compound, comprising a melanin precursor that spontaneously synthesizes a melanin when in free form in vivo, and a peptide directly covalently bonded to or indirectly linked to the melanin precursor, wherein the direct covalent bond or indirect link of the peptide to the melanin precursor blocks spontaneous synthesis of the melanin in vivo in the absence of protease activity. We also disclose methods of tumor imaging, tumor detection, diagnosis, and treatment, including methods comprising administering, to a patient suffering from a tumor, the composition; and either surgically resecting the tumor or thermally ablating the tumor, wherein the surgical resection is guided at least in part by contrast imparted by, or the thermally ablating targets cells containing, the melanin, wherein the melanin is spontaneously synthesized in the tumor after administering the composition.