134 results about "Early cancer" patented technology

MK (midkine) was found to rise in the blood or urine of patients with various types of cancers at early stage. Based on this finding, a method for detecting early cancer, comprising the step of measuring MK in blood or urine was completed.

Devices and methods are provided for ablating areas of the gastrointestinal tract affected with certain benign, pre-cancerous, or early cancerous lesions that originate within the epithelium and are limited to the mucosal layer of the gastrointestinal tract wall. Examples of such lesions include benign conditions such as cervical inlet patch (ectopic gastric mucosa in the upper esophagus), as well as pre-cancerous and cancerous conditions such as intestinal metaplasia/intra-epithelial neoplasia/early cancer of the stomach, squamous intra-epithelial neoplasia and early cancer of the esophagus, oral and pharyngeal leukoplakia, flat colonic polyps, anal intra-epithelial neoplasia (AIN), and early cancers of the anal canal. Ablation, as provided the invention, commences at the epithelial layer of the gastrointestinal wall and penetrates deeper into the gastrointestinal wall in a controlled manner to achieve a successful patient outcome, the latter of which is defined generally as eradication of the targeted lesion, and/or a change in the targeted lesion to prevent or forestall patient morbidity. Embodiments of the device include an ablational electrode array that spans 360 degrees and an array that spans an arc of less than 360 degrees.

Improved methods for treatment of cancer which involve the targeting of slow-growing, relatively mutationally-spared cancer stem line are provided. These methods are an improvement over previous cancer therapeutic methods because they provide for very early cancer treatment and reduce the likelihood of clinical relapse after treatment.

Improved methods for treatment of cancer which involve the targeting of slow-growing, relatively mutationally-spared cancer stem line are provided. These methods are an improvement over previous cancer therapeutic methods because they provide for very early cancer treatment and reduce the likelihood of clinical relapse after treatment.

Improved methods for treatment of cancer which involve the targeting of slow-growing, relatively mutationally-spared cancer stem line are provided. These methods are an improvement over previous cancer therapeutic methods because they provide for very early cancer treatment and reduce the likelihood of clinical relapse after treatment.

This invention is related to a method for the detection of early or pre-cancer using DNA isolated from blood and body fluids. This invention provides an improved methylation-based PCR assay, a panel of methylated-based cancer-related gene markers for the detection of general cancer and a panel of demethylation-based tissue- or cell-specific gene markers for discriminating different type of cancer detected from blood samples. This method couples a sequential cancer-related gene marker detection and tissue or cell-specific gene marker assay and is particularly useful as a simultaneous screening test for following type of cancer: lung, breast, ovarian, colon, stomach, prostatic, pancreatic and liver cancer.

The invention belongs to the technical field of medical image intelligent processing, in particular to a gastrointestinal endoscopic image classification and early cancer detection system based on a multi-task neural network. The system of the invention comprises: (1) a feature extraction backbone network; (2) Classification of gastrointestinal endoscopic images; (3) regional detection branch of early gastrointestinal cancer. The invention adopts a multi-task depth neural network structure, and classifies and detects two tasks to share a plurality of convolution layers. Endoscopic images are inputted into the neural network model, and the detection and classification results can be obtained simultaneously after a forward propagation, which can effectively reduce the computational load andimprove the classification and detection accuracy. The experimental results show that the invention can accurately classify the endoscopic images into normal and early cancers and detect the irregularlesion areas in the early cancers images, reduce the influence of human factors and improve the efficiency of clinical diagnosis.

The invention discloses a cancer lesion range prediction supporting system based on deep learning and method. The system comprises a client and a server, wherein the client is used for monitoring an image acquired by the current endoscope device and transmitting the image to the server, and an analysis result fed back by the server is received and displayed; and the server is used for instantly judging the part corresponding to the image and the part features according to the image acquired by the client, and the analysis result is fed back to the client. When the endoscope device performs image acquisition and the client is triggered, the collected endoscope image is acquired and transmitted to the server; the server receives the endoscope image as a parameter, a convolutional neural network model is called for analysis on whether the endoscope image is qualified and analysis on part judgment and part feature recognition; and according to the acquired analysis result, the client callsan image representing each part and a sign representing the part feature for overlapped display. An early cancer can be diagnosed, a cancerization range is circled, the operation is simple and easy to use, and social and economic value is significant.

The invention provides a digestive tract early cancer auxiliary diagnosis system based on depth learning and an examination device. The system comprises a feature extraction network, an image classification model, an endoscope classifier and an early cancer recognition model, wherein the feature extraction network is used for performing initial feature extraction on endoscope images according to aneural network model; the image classification model is used for extracting initial features and acquiring image classification features; the endoscope classifier is used for extracting initial features to obtain endoscope classification features and classifying gastroscope and coloscope images; the early cancer recognition model is used for splicing initial features, endoscope classification features and image classification features to obtain the probability of early cancer focuses of white light images, electronic straining images or chemical straining images corresponding parts or obtainwashing prompt or position recognition prompt of corresponding parts. The AI auxiliary diagnosis quality and digestive endoscopy diagnosis efficiency are improved.

The invention relates to a gastric early cancer histological image classification system based on a deep neural network, and the system comprises a WSI pathological section obtaining module which is used for obtaining a gastric early cancer pathological section image; an image preprocessing module which is used for preprocessing the early gastric cancer pathological section image; a data set division module which is used for dividing the data set after image preprocessing into a training set and a verification set, a training set which is used for model training, and the verification set is used for verifying a model effect; a feature extraction module which is used for extracting picture features of the training set or the verification set based on a GoogleNet double-branch network structure and outputting feature vectors of corresponding pictures; and a classifier training module which is used for training based on the neural network model to obtain a gastric early cancer classification model.

The invention relates to the technical field of medical image processing, in particular to a gastric early cancer auxiliary diagnosis method based on a deep learning multi-model fusion technology, which comprises the following steps: S1, constructing multiple models; s2, collecting gastroscope images, forming continuous serialized image frames, identifying the light source mode of the current image frame by utilizing the image classification model 1, entering the step S3 to mark the position of a focus by the target detection model 2 when the current image frame is identified as a white lightmode, and marking the high-risk focus by utilizing the image classification model 3; and when the image frame is identified as the dyeing amplification mode, entering the step S4 in which a segment model group can extract the boundary range, the microvessel form and the micro-tissue structure feature map in the image frame in real time, and outputting whether canceration occurs or not, the credibility and the differentiation type by the decision-making model 7. A plurality of deep learning models are constructed according to different tasks, a parallel cascade model fusion technology is adopted, and a full-process intelligent auxiliary diagnosis function is provided in the stomach early cancer screening process of endoscopists.

The invention relates to a medicament for curing cancer and a preparation method thereof. The medicament comprises a No.1 polypharmaceutical, a No.2 polypharmaceutical and a No.3 polypharmaceutical, wherein the No.1 polypharmaceutical comprises 31 flavors of traditional Chinese medicines including Glauber salt, kudzuvine root, red sange root, mongolian snakegourd, rhubarb, eucommia bark, and the like; the No.2 polypharmaceutical comprises 26 flavors of traditional Chinese medicines including Chinese clematis, dried rehamnnia root, sesame kernel, schisandra fruit, tangerine peel, coix seed, and the like; and the No.3 polypharmaceutical comprises 6 flavors of traditional Chinese medicines including liquorice, cogongrass rhizome, dandehon herb, mint, dendrobium stem and cyperus rotundus linn. The bulk drugs consisting of the No.1 polypharmaceutical, the No.2 polypharmaceutical and the No.3 polypharmaceutical are crushed firstly, the No.1 polypharmaceutical and the No.2 polypharmaceutical can be respectively prepared into capsules, tablets, drop pills, granulas, oral liquid and injection by corresponding technologies; and the No.3 polypharmaceutical can be prepared into fine powder medicinal tea or granulas. The medicament has the unique effects of inhibiting tumor and removing pain, suppressing metastasis, removing necrosis and promoting granulation, balancing nutrition, expelling toxin, increasing immunity, and the like. The surgery can be replaced for curing the cancer generally, and the radiotherapy and the chemotherapy are not used; the effective rate of preventing the caner is 100 percent, the radical rate to the early cancer is 100 percent, and the curative ratios of the caner of medium and late stages are respectively over 98 percent and over 70 percent; and the invention also has the advantages of omni-directional treatment, high curative ratio and no toxic side effect.

The invention relates to the technical field of image analysis, and particularly discloses an early-stage cancer auxiliary diagnosis system based on artificial intelligence, and the system comprises:an image obtaining module which is used for obtaining a marked sample image of a digestive tract endoscope, carrying out the preprocessing and random sorting of the sample image, and generating a training image set; a model construction module which is used for constructing a convolutional neural network model, carrying out iterative training on the convolutional neural network model based on thetraining image set, then carrying out testing, and outputting the successfully trained convolutional neural network model after the testing is completed; and a diagnosis module which is used for acquiring a to-be-diagnosed image of the digestive tract endoscope, judging the to-be-diagnosed image based on the successfully trained convolutional neural network model, and outputting a judgment result.By adopting the technical scheme of the invention, the training effectiveness can be improved.

The invention discloses a kit for diagnosing primary hepatoma, preparation method and application thereof. The kit can quickly and accurately carry out the clusterin detection so as to carry out quickauxiliary diagnosis on the primary hepatoma of each period; and the kit especially has an important clinical application value for distinguishing hepatocirrhosis and early liver cancer. The kit comprises an enzyme-linked immunological diagnosis system established on the basis of a double-antibody sandwich principle. The kit has high sensitivity, strong specificity, simple operation, stable reagent, good repeatability, easy popularization and application and extensive market prospect.

The invention relates to a high-efficiency rapid bladder cancer urinalysis kit and a urinary cell active preservative fluid, which belong to the field of biomedicine. The high-efficiency rapid bladder cancer urinalysis kit contains the urinary cell active preservative fluid and a specific fluorescence labeled primer for detecting the activity of telomerase. The kit can perform detection of the activity of the telomerase on cells taken from urine further to efficiently and accurately detect whether a patient suffers from diseases related to the activity of the telomerase such as bladder cancer; the urinary preservative fluid in the kit is a key factor; the activity of the telomerase of an early cancer patient has lower expression level, the activity of the telomerase is difficult to detect in a small amount of tested cells, and the urinary preservative fluid can ensure that collected urinary cells preserve good activity so as to provide adequate time for acquiring adequate active cells; and the high-efficiency rapid bladder cancer urinalysis kit and the urinary cell active preservative fluid provide a feasible way for accurately detecting early bladder cancer and non-invasively monitoring the bladder cancer.

A system (40) for diagnosis and staging of early stages of cancer in the tissue of a patient is provided. The system is configured to combine information from a Polarized Light Scattering Spectroscopy measurement (70) having a first probe depth, and a Differential Path Length Spectroscopy measurement (60) having a second probe depth, wherein the second probe depth is set larger than' the first probe depth. By comparing the results of the Polarized Light Scattering Spectroscopy and Differential Path Length Spectroscopy measurements early stages of cancer, such as dysplasia may be detected. Also hyperplasia, carcinoma in situ, and carcinoma may be detected. A computer-readable medium, method and use are also provided.

The present invention concerns a method of prognosing the risk of early ovarian cancer relapse in a subject having ovarian cancer comprising: a) detecting the level of at least one marker selected from the group consisting of BTF4, GCS and HLA-DRbeta1; and b) comparing the level of the above at least one marker with that of a corresponding control sample, wherein the detection of a lower level of the at least one marker compared to that in the control sample is indicative that the subject is at risk of early cancer relapse. Also provided is a method of stratifying a subject suffering from ovarian cancer based on the expression levels of the disclosed markers and kits for practicing the methods of the present invention.

The invention relates to the technical field of image processing, and particularly discloses an early cancer lesion range prediction auxiliary system based on deep learning. The system comprises an image acquisition module used for acquiring a sample image of a digestive tract endoscope with a target frame, preprocessing the sample image, recording coordinate information of an end point of the target frame and generating a training image set; a model construction module used for constructing a convolutional neural network model, carrying out iterative training on the convolutional neural network model based on the training image set, then carrying out testing, and obtaining a successfully trained convolutional neural network model after the testing is completed; and a range division moduleused for receiving a to-be-diagnosed image of the digestive tract endoscope, judging the to-be-diagnosed image based on the successfully trained convolutional neural network model, and outputting coordinate information of a prediction endpoint of the to-be-diagnosed image. The range division module draws a target box on the to-be-diagnosed image based on the coordinate information of the prediction endpoint. By adopting the technical scheme, the early cancer lesion range can be marked.

Disclosed are methods of identifying tumor-derived exosomes as an early cancer diagnostic, as well as for staging, assessing progression and assessing therapy of cancer.

Disclosed is a magnetic nanostructure for detecting and isolating circulating tumor cells including a conductive polymer to which an antibody and magnetic nanoparticles are bound, which enables circulating tumor cells from early cancer patients and various circulating tumor cell types to be effectively detected using a small amount of blood, circulating tumor cells to be monitored with the naked eye through colorimetric detection, and a very small amount of circulating tumor cells present in blood to be efficiently captured with a strong magnetic field generated by a large amount of the loaded magnetic nanoparticles; in detecting, isolating, and collecting the circulating tumor cells in a very small amount, a long nanowire structure and various antibody types are used, whereby contact with cancer cells may be increased and strong bonding may be formed; sensitivity is increased and various interactions with cancer cells are facilitated, thereby exhibiting increased detection and isolation effects.

The present invention relates to a fully quantitative spectroscopy imaging instrument for wide area detection of early cancer (dysplasia). This instrument provides quantitative maps of tissue biochemistry and morphology, making it a powerful surveillance tool for objective early cancer detection. The design, construction, calibration, and diagnostics applications of this system is described with the use of physical tissue models. Measurements were conducted on a resected colon adenoma, and the system can be used for vivo imaging in the oral cavity.