45 results about "Kidney tumor" patented technology

The invention discloses a CT contrast image kidney tumor segmentation method based on a three-dimensional convolution neural network. The method firstly roughly segments the kidney region in the CT contrast image, the kidney and tumor are labeled separately, and the data set is generated. Then the training set is put into the convolution neural network based on pyramid cisternization and step-by-step feature enhancement module, and the training model is obtained. The new kidney data is predicted by the training model, and the segmentation mask of kidney tumor is obtained. The invention also provides a CT contrast image kidney tumor segmentation system based on a three-dimensional convolution neural network. The invention mainly solves the problem of difficult image segmentation of kidney tumor, and the invention can directly obtain a segmentation mask of kidney tumor.

Genes that are differentially expressed in subtypes of renal cell carcinomas are disclosed as are their polypeptide products. This information is utilized to produce nucleic acid and antibody probes and sets of such probes that are specific for these genes and their products. Methods employing these probes, including hybridization and immunological methods, are used to determine the subtype of a renal cell tumor sample from a subject based on the differential expression of such genes that is characteristic of the cancer subtype.

The invention relates to the field of cellular immunotherapy of tumor and in particular to a medicine composition and application thereof. The medicine composition comprises a medicine specifically targeting kidney tumor GPC3, wherein the medicine specifically targeting kidney tumor GPC3 comprises any one or a combination of at least two of CAR-T (Chimeric Antigen Receptor T-Cell Immunotherapy) cells, an anti-GPC3 antibody, TCR-T (T-lymphocyte Cell Receptor) cells targeting GPC3, and siRNA of DC-CIK (Dendritic Cell-Cytokine-Induced Killer) cells targeting GPC3 or targeting silenced GPC3. Testsshow that GPC3 can be new targeting antigen of nephroblastoma, and by adopting the medicine composition, nephroblastoma can be effectively killed and inhibited.

The present invention relates to Chimeric Antigen Receptors (CAR) that are recombinant chimeric proteins able to redirect immune cell specificity and reactivity toward selected membrane antigens, and more particularly in which extracellular ligand binding is a scFV derived from a ROR1 monoclonal antibody, conferring specific immunity against ROR1 positive cells. The engineered immune cells endowed with such CARs are particularly suited for treating lymphomas and leukemia, and for solid tumors such as breast, colon, lung, and kidney tumors

The invention provides a point interaction deep learning segmentation algorithm specially for solving the kidney tumor segmentation problem in a medical image. The algorithm is composed of a point interaction preprocessing module, a bidirectional ConvRNN unit and a core deep segmentation network. The algorithm starts from a tumor center position provided by an expert; in 16 directions with uniformintervals, 16 image blocks with the size of 32 * 32 are intensively collected from inside to outside according to the step length of 4 pixels to form an image block sequence, a deep segmentation network with sequence learning is used for learning the inside and outside change trend of a target, the edge of the target is determined, and segmentation of the kidney tumor is achieved. The method canovercome the influences of low contrast, variable target positions and fuzzy target edges of medical images, and is suitable for organ segmentation and tumor segmentation tasks. Compared with the prior art, the method has the following characteristics: 1) the interaction mode is simple and convenient; (2) a Sequence Patch Learning concept is provided, and a sequence image block is used for capturing a long-range semantic relationship, so that a relatively large receptive field can be obtained even in a relatively shallow network; and 3) a brand-new ConvRNN unit is provided, the inside and outside change trend of the target is learned, the interpretability is relatively high, the actual working mode of doctors is met, and the final model is high in precision and strong in applicability.

The invention discloses an improved U-net kidney tumor segmentation method. The method comprises the following steps: combining a kidney and a kidney tumor together through a pixel superposition method; extracting the image for a plurality of times by using convolution at the encoder part, and generating corresponding segmented kidney and kidney tumor images by using transposed convolution at thedecoder part; introducing a residual learning unit into the encoder; and carrying out optimization training by using a Luoshi loss function in network training. According to the method, a deep residual network and a The Lovasz-Softmax loss function are combined to achieve the semantic segmentation method based on the ResUnet, the characteristics of the image are better extracted by utilizing the Lovasz hinge residual network, the improved method is more excellent than an original method, and the segmentation precision of the kidney tumor is effectively improved.

The present invention relates to Chimeric Antigen Receptors (CAR) that are recombinant chimeric proteins able to redirect immune cell specificity and reactivity toward selected membrane antigens, and more particularly in which extracellular ligand binding is a scFV derived from a ROR1 monoclonal antibody, conferring specific immunity against ROR1 positive cells. The engineered immune cells endowed with such CARs are particularly suited for treating lymphomas and leukemia, and for solid tumors such as breast, colon, lung, and kidney tumors

This invention is in the field of chemical restructuring of pharmaceutical agents known to cause tissue toxicity as a side effect, by producing their orotate derivatives. More particularly, it concerns orotate derivatives of the anthracyclines, doxorubicin and daunorubicin, that are found to reduce levels of the pharmaceutical agent in noncancerous tissues. These orotate derivatives are equally efficacious in inhibiting the SCCAKI-1 kidney tumor in animals and the reduction in the heart tissue of doxorubicin compared with doxorubicin HCl suggests a reduction in toxicity induced by free radical generation by the anthracyclines.

This invention is in the field of chemical restructuring of pharmaceutical agents known to cause tissue toxicity as a side effect, by producing their orotate derivatives. More particularly, it concerns orotate derivatives of the anthracyclines, doxorubicin and daunorubicin, that are found to reduce levels of the pharmaceutical agent in noncancerous tissues. There orotate derivatives are equally efficacious in inhibiting the SCCAKI-1 kidney tumor in animals and the reduction in the heart tissue of doxorubicin compared with doxorubicin HCl suggests a reduction in toxicity induced by free radical generation by the anthrracyclines.

This invention provides an anti-cancer immunogenic agent(s) (e.g. vaccines) that elicit an immune response specifically directed against renal cell cancers expressing a G250 antigenic marker. Preferred immunogenic agents comprise a chimeric molecule comprising a kidney cancer specific antigen (G250) attached to a granulocyte-macrophage colony stimulating factor (GM-CSF). The agents are useful in a wide variety of treatment modalities including, but not limited to protein vaccination, DNA vaccination, and adoptive immunotherapy.

The kidney benign and malignant tumor classification method based on multi-view information cooperation comprises the following steps: step 1) medical image preprocessing: performing data enhancementprocessing on images of three views of kidney CT; 2) constructing a multi-view convolution network sub-model for the image of each view; and 3) constructing a multi-view information collaborative convolutional neural network model, unifying the outputs of the sub-models of the three views to the same neuron classification layer, and finally inputting a Sigmoid function to obtain a classification result. A penalty function is added for false positive cases, and greater penalty is given to reduce the occurrence of false positive conditions; and 4) kidney tumor benign and malignant classification: inputting a kidney CT image to be detected into the multi-view information collaborative convolutional neural network model constructed in the step 3), and performing network output to obtain a benign and malignant result of the tumor. According to the method, the multi-view image information of different kidney tumors is combined and fully utilized, the accuracy of kidney tumor benign and malignant classification can be improved, and meanwhile, the problem of poor model generalization ability caused by insufficient neural network training data due to lack of case image data is avoided.

The invention discloses an image segmentation method for a kidney tumor, and the method comprises the following steps: S1, obtaining an abdomen scanning image, and dividing the abdomen scanning imageinto a data set and a training set; S2, performing down-sampling preprocessing on the acquired abdomen scanning image to obtain a scaled image; S3, determining an area of interest of the preprocessedimage in the step S2 by using global position information of the abdominal space, performing image segmentation, and performing training and prediction by a U-shaped kidney tumor segmentation network;S4, outwards expanding the abdomen scanning image in the step S1 for a certain range, segmenting images of the left kidney and the right kidney, interpolating all segmented images, and unifying the interpolated images into the same data distribution to obtain left and right kidney VOI images; S5, performing tumor segmentation prediction on the left and right kidney VOI images by a U-shaped kidneytumor segmentation network. Interference of other organs and tissues is effectively avoided, the accuracy of kidney tumor identification and image segmentation is improved, and efficiency is higher.

The invention discloses a kidney tumor segmentation method based on multi-scale feature learning, and the method comprises the following steps: S1, obtaining an abdomen scanning image, and randomly dividing the obtained abdomen scanning image into a training set; S2, preprocessing the abdomen scanning images in the training set to obtain preprocessed images; S3, constructing a multi-scale featurenetwork, and fully capturing global structure information of the image through the network in combination with a pyramid pooling module and a feature pyramid fusion module so as to perform accurate kidney segmentation; S4, predicting and segmenting the image preprocessed in the step S2 through a multi-scale feature network; The invention can be used for effectively detecting kidney tumors with different volumes, false negative results are avoided, and high-accuracy detection results are obtained.

This invention is in the field of chemical restructuring of pharmaceutical agents known to cause tissue toxicity as a side effect, by producing their orotate derivatives. More particularly, it concerns orotate derivatives of the anthracyclines, doxorubicin and daunorubicin, that are found to reduce levels of the pharmaceutical agent in noncancerous tissues. There orotate derivatives are equally efficacious in inhibiting the SCCAKI-1 kidney tumor in animals and the reduction in the heart tissue of doxorubicin compared with doxorubicin HCl suggests a reduction in toxicity induced by free radical generation by the anthrracyclines.

The invention discloses a remote-controllable transparent human abdomen model for explaining disease conditions, which comprises a shell, a model of main abdominal organs, a battery, an air pump and a remote controller. The outer shell is transparent, and there are batteries, an air pump and the main organs in the abdominal cavity inside. The battery powers the air pump and the robotic arm inside the kidney. The kidneys consist of left and right kidneys, each of which has a retractable mechanical arm. The mechanical arm is stretched and moved freely under the command of the remote control, and there is an air bag at the other end, which represents the tumor. The air pump injects gas into the air bag through the plastic tube connected to the air bag to make the air bag bigger and smaller to represent the volume change of the tumor. The airbag moves with the robotic arm and can reach any position in the kidney to display tumors in various parts of the kidney. When explaining the disease, the doctor can intuitively describe the normal shape and adjacent structure of the abdominal organs, mainly the kidney, simulate the location and size of the tumor in the kidney, and explain the operation process in detail, so that the patient and his family can fully understand the disease. And it can replace the traditional single organ model for popular science teaching.

The invention discloses a kidney tumor segmentation method based on mixed dimension convolution, and the method comprises the following steps: S1, obtaining an abdomen scanning image, and dividing theobtained abdomen scanning image into a data set and a training set; S2, preprocessing the abdomen scanning image in the data set to obtain a preprocessed image; S3, constructing a mixed-dimension convolution network, and optimizing the feature learning of the mixed-dimension convolution network for the kidney tumor through the network in cooperation with a mixed-dimension convolution module; S4,inputting the preprocessed image into a mixed-dimension convolutional network for prediction, and finally obtaining a segmentation result; 2D, 2.5 D and 3D convolution features of the kidney tumor arelearned at the same time through the hybrid convolution network, and the generalization ability of model features is enhanced through feature fusion of the 2D, 2.5 D and 3D convolution features.

The present invention relates to a composition for modulating tumor cell dissemination, in particular metastatic cancer cells. In particular, the invention relates to an agent for modulating metastatic tumor cell dissemination for use in the treatment and / or prevention of a metastatic cancer wherein the agent is a capture agent and / or a chemoattractant for tumor cells. The invention also relates to a product, comprising an agent for modulating metastatic kidneys tumor cell dissemination, and to a method of treatment or prevention of cancer.

The invention relates to a U-Net kidney tumor image segmentation method and device based on aggregation interlayer information, and a storage medium. The method comprises the following steps: 1) acquiring an image; 2) image preprocessing; 3) constructing a label, and constructing a corresponding label for the preprocessed image obtained in the step 2); 4) constructing a U-Net model for aggregating interlayer information; 5) training a U-Net model for aggregating interlayer information; 6) segmenting the kidney tumor image, and inputting a kidney tumor image to be segmented into the trained U-Net model of the aggregation interlayer information to realize image segmentation. According to the method, the information in the slice layers and the information between the slice layers are fully utilized to learn context information, and memory consumption and hardware requirements of a computer are reduced. The output extraction features after convolution are realized on multiple scales, so the information redundancy can be reduced, and the segmentation accuracy can be improved.

The invention provides a method for predicting difficulty and complications of a multi-mode kidney tumor kidney protection operation, and belongs to the technical field of medicine. Three imageology comprehensive scoring capabilities of an R.E.N.A.L scoring system, an MAP scoring system and a CSA scoring system are obtained through computer artificial intelligence deep learning; and weighted calculation is performed on basic disease information of the patient, whether anticoagulant drugs, antibiotics and hormones are used or not, preoperative heart, lung and kidney function data and imaging comprehensive scoring ability through computer artificial intelligence deep learning; then, an imaging scoring part of an existing scheme is reserved and fused, more and more comprehensive imaging features are considered compared with all different scoring systems of the existing scheme, the defects of labor consumption, time consumption, instability and the like of manual calculation are overcome, and the result is more accurate and stable.

The invention provides external use traditional Chinese medicine for curing kidney cancer. The external use traditional Chinese medicine comprises a basic remedy and an adjuvant. The basic remedy is formed by 41 crude drugs such as semen strychni, monkshood, radix aconiti agrestis and osteon myospalacem baileyi. The adjuvant is formed by safflower carthamus, liquorice, centipede and Shanxi mature vinegar. The external use traditional Chinese medicine for curing kidney cancer has the obvious function for inhibiting kidney tumors, and has the advantages of being safe, effective, fast, easy to operate, free of pains and economical.

A method of diagnosing the likelihood of recurrence of clear cell renal cell carcinoma is provided. The method involves a) detecting the gene expression signatures of mitochondrial electron transport chain subunits, mitochondrial ribosomal proteins, major histocompatibility complex class II (MHC-II) proteins or combinations thereof in a kidney tumor tissue sample; and b) determining that the subject has an elevated risk of recurrence of clear cell renal cell carcinoma if the gene expression signatures include certain sequences. In another embodiment, the method uses copper levels to diagnose the likelihood of recurrence of clear cell renal cell carcinoma.

The present invention provides a method for classification of kidney tumors through the analysis of the expression patterns of specific microRNAs and nucleic acid molecules relating thereto. Classification according to a microRNA expression framework allows optimization of treatment, and determination of specific therapy.

The invention discloses a method for automatically evaluating difficulty of kidney tumor enucleation based on CT (computed tomography) images, which comprises the following steps: based on artificial intelligence and image analysis technologies, segmenting an abdominal wall, a kidney and a kidney tumor at the same time by establishing a deep network model, automatically calculating an MAP score and an R.E.N.A.L score by utilizing relevance among the tumor, a renal pelvis, the kidney and the abdominal wall, and automatically evaluating the difficulty of the kidney tumor enucleation based on the CT images. Therefore, the difficulty of the kidney tumor enucleation is reasonably and effectively evaluated; the method has the advantages of time and labor saving and high evaluation accuracy and reliability.

The invention discloses a biological fermentation preparation method of ergosterol peroxide, and relates to the technical field of bioengineering. Glycerin, yeast powder and peptone are used as main raw materials, and Paecilomyces cicadae mycelium is obtained through liquid shake-flask culture of Paecilomyces cicadae and enlarged culture of a liquid seeding tank. The paecilomyces cicadae myceliumis subjected to ethanol extraction, macroporous resin Amberlite XE-243 separation and C-18 column purification to obtain the ergosterol peroxide. Through experimental study in paecilomyces cicadae liquid fermentation and separation and purification of the ergosterol peroxide from the paecilomyces cicadae liquid fermentation liquid, it proves that the ergosterol peroxide can be used as a medicine for treating kidney tumors, and an ideal medicine is provided for kidney cancer patients.

The present invention provides a method for classification of kidney tumors through the analysis of the expression patterns of specific microRNAs and nucleic acid molecules relating thereto. Classification according to a microRNA expression framework allows optimization of treatment, and determination of specific therapy.

The invention relates to an index evaluation method for renal tumor partial resection risk and postoperative early prognosis. The method comprises the steps: dividing different operation modes into open operations and non-open operations; according to preoperative CT imaging data of a patient, obtaining the sum of a basic size index of a kidney tumor and a position index of the tumor as a preoperative index evaluation index, and establishing an operation difficulty and early prognosis index for preoperative evaluation of the kidney tumor according to different operation modes. Compared with other scoring systems, the system can be more suitable for diversified operation modes at present, and reference suggestions are provided for clinicians. Compared with other scoring systems, the evaluation system for evaluating the operation risk and postoperative early prognosis of renal tumor partial resection based on different operation modes can be more suitable for the diversified operation modes at present; the system is an evaluation system established through prospective research on the basis of the corresponding illness state of a patient, and compared with a previous scoring system (based on retrospective research), the operation risk and postoperative early prognosis can be better evaluated.