310 results about "Prognosis prediction" patented technology

A method and system for integrating radiological and pathological information for cancer diagnosis, therapy selection, and monitoring is disclosed. A radiological image of a patient, such as a magnetic resonance (MR), computed tomography (CT), positron emission tomography (PET), or ultrasound image, is received. A location corresponding to each of one or more biopsy samples is determined in the at least one radiological image. An integrated display is used to display a histological image corresponding to the each biopsy samples, the radiological image, and the location corresponding to each biopsy samples in the radiological image. Pathological information and radiological information are integrated by combining features extracted from the histological images and the features extracted from the corresponding locations in the radiological image for cancer grading, prognosis prediction, and therapy selection.

The invention relates to an immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time, and belongs to the technical field of biological medicines. The model can be used for evaluating the infiltration degree of immune cells in a tumor in clinical practice by detecting the expression levels of 22 specific immune related genes of ahepatocellular carcinoma patient, so that the model can be used for predicting hepatocellular carcinoma tumor immune infiltration in clinical practice and improve the prediction capability of the liver cancer immunotherapy response. The model can be used for judging the postoperative overall survival risk of a patient and guiding the formulation of a postoperative treatment strategy, and the corresponding microarray chip kit can realize the standardization and convenience of detection. Meanwhile, the immune gene prognosis model provided by the invention can increase the prediction accuracy andthe clinical net income of a hepatocellular carcinoma TNM staging system on the total survival time of three years and five years after operation. As a molecular marker for objectively and accuratelyevaluating the tumor immune state and poor prognosis risk of hepatocellular carcinoma, the model can realize accurate implementation of hepatocellular carcinoma immunotherapy and accurate prognosis prediction.

The invention discloses an in situ hybridization probe, a reagent and an application of long non-coding RNA LOC401317. The long non-coding RNA LOC401317 can be used for preparing a prognosis preparation for a patient with nasopharyngeal carcinoma, and particularly a kit for predicting prognosis of the patient with the nasopharyngeal carcinoma employing an in situ hybridization detection method is prepared. A research proves that expression of the LOC401317 in a nasopharyngeal carcinoma tissue is lowered, and the patient with the nasopharyngeal carcinoma in low-expression LOC401317 is worse than the patient with the nasopharyngeal carcinoma in high-expression LOC401317 in prognosis, therefore, expression of the LOC401317 is applied to prognosis prediction of the patient with nasopharyngeal carcinoma; a powerful molecular biology basis can be provided for prognosis of the patient with the nasopharyngeal carcinoma; and the long non-coding RNA LOC401317 has profound clinical significance and important popularization and application prospects.

The invention relates to miRNAs (micro ribonucleic acids) used for detecting prostatic cancer and a kit, and belongs to the technical field of molecular biology, wherein the miRNAs comprise carcinogenic miRNAs and cancer-inhibiting miRNAs; expression quantity of the carcinogenic miRNAs in real-time PCR (polymerase chain reaction) method detection, and expression quantity of the cancer-inhibiting miRNAs in the real-time PCR method detection are lowered. According to different degrees of the prostatic cancer, different carcinogenic miRNAs and cancer-inhibiting miRNAs are adopted to respectively carry out diagnosis in a targeted manner. A technical scheme provided by the invention is adopted, so that diagnosis and prognosis prediction results of the prostatic cancer can be quickly, simply, efficiently and stably obtained.

The invention relates to prognostic markers and prognostic signatures, and compositions and methods for determining the prognosis of cancer in a patient, particularly for melanoma. Specifically, the invention relates to the use of genetic and protein markers for the prediction of the risk of progression of a cancer, such as melanoma, based on markers and signatures of markers. In various aspects, the invention provides methods, compositions, kits, and devices based on prognostic cancer markers, specifically melanoma prognostic markers, to aid in the prognosis and treatment of cancer.

The invention provides a peripheral blood mRNA marker for predicting prognosis of patients with lung adenocarcinoma and a screening method and application thereof and belongs to the technical fields of medical molecular biology and clinical medicine. The screening method includes the steps of 1), comparatively analyzing gene expression profiles of wild-type human lung cancer cell lines and Nrf2 gene knockout human lung cancer cell lines to obtain Nrf2 target genes; 2), analyzing the relationship between Nrf2 target gene expression and the prognosis survival rate of lung adenocarcinoma by the aid of a discovery group database and screening out the genes with the Nrf2 target gene expression Wald statistics greater than 2 in the discovery group database to obtain the periphery blood MRNA marker for predicting prognosis of the patients with the lung adenocarcinoma. The marker obtained with the screening method is applied to predicting prognosis of the patients with clinical lung adenocarcinoma, good flexibility, stability and specificity are achieved, and spreading use can be realized.

The present invention provides a system for accurately predicting the prognosis of gastric cancer patients. The system is based on the Nomogram prognostic prediction model constructed by the present invention. Compared with the traditional TNM staging system, the system has high accuracy, individualized prediction, and conforms to Prognostic features of gastric cancer with Chinese characteristics.

The invention discloses an application method of long no-coding RNA (Lnc RNA (Ribonucleic Acid)) RMST (Rhabdomyosarcoma 2 associated Transcript). The application method is used for preparing a prognosis preparation for glioma patients and particularly used for preparing a kit which is used for predicting the prognosis of the glioma patients by a real-time fluorescent quantitative analysis method. Confirmed by researches, the expression of Lnc RNA RMST in glioma tissue is increased, and the prognosis of the glioma patients with high Lnc RNA RMST expression is poorer than that of the glioma patients with low Lnc RNA RMST expression, so that by applying the expression of Lnc RNA RMST to the prognosis prediction of the glioma patients, a powerful basis for molecular biology can be provided for the prognosis of the glioma patients; the application method has far-reaching clinical significance and generalization performance.

The invention relates to a prognosis prediction model for patients with hilar cholangiocarcinoma. Specifically, the present invention provides a carrier for postoperative prognosis of patients with hilar cholangiocarcinoma, which is used to calculate risk factor scores and patient 3-year survival rate Y3 and/or 5-year survival rate Y5; Wherein, the risk factors include at least the patient's age X, and the fraction of the patient's age, the 3-year survival rate and the 5-year survival rate satisfy the relationship described in the article. The present invention systematically investigates the clinical and pathological characteristics that affect the prognosis of patients with hilar cholangiocarcinoma, and the effect of patients after surgical treatment, evaluates the key factors that affect the survival and prognosis of patients with hilar cholangiocarcinoma, and establishes a method for patients with hilar cholangiocarcinoma. The list of survival prognosis is used to evaluate the 3-year and 5-year survival rate of patients after surgery, and to screen out high-risk groups for intervention to improve the survival prognosis of patients.

A model for prognosis prediction of breast cancer patients is characterized in that joint judgment is performed by ten lncRNAs expression quantities. The established model can predict prognosis of thepatient more accurately and individually, better guide clinical decision, provide reference for selection of a treatment scheme of the patient and reduce unnecessary treatment, and has important significance for accurate diagnosis and treatment of breast cancer. In view of the limited prognosis evaluation capability of the current breast cancer TNM staging system and molecular typing on patients,the model makes an effective progress for the research in the direction. According to the method, the prognosis condition of the patient can be accurately evaluated, and the model has very high practicability and guidance.

The invention discloses a lung cancer prognosis prediction model and a construction method and device. The method comprises the steps of collecting original gene expression data and corresponding clinical survival data of a lung cancer sample, and carrying out standardization to obtain a gene expression matrix; obtaining the types of immune cells in the tumor and calculating the relative ratio ofthe various types of immune cells; screening out parameters for constructing a prognosis prediction model from the obtained immune cell types and obtaining corresponding regression coefficients, wherein the parameters are multiple immune cell types; and based on the screened parameters, calculating an immune score according to the relative ratio and the corresponding regression coefficient to obtain a lung cancer prognosis prediction model. Prognostic risk stratification of lung cancer patients is achieved from the molecular and immune cell level, high-risk and low-risk patients are remarkablyseparated, then the clinical result of lung cancer can be predicted, individualized treatment is guided, and high clinical application value is achieved.

The invention discloses a kit and system for prognosis prediction of a metastatic colorectal cancer and relates to the field of biomedicine. The kit comprises a reagent for detecting the expression level of a target gene, wherein the target gene is selected from one or more genes in the group consisting of ACOT11, C12orf45, CHDH, COX17, CTNNB1, CYP2S1, DDTL, DUSP18, FAM221A, FGFR4, KLC4, LARS2, PFDN6, SLC27A3 and TNFRSF11A, the expression levels of the target genes are detected by using the kit, the prognosis condition of the metastatic colorectal cancer can be stably predicted according to the detection result, the prediction result is reliable, the kit has a clinical application prospect, and a new prediction thought or strategy is provided for the prognosis of the metastatic colorectal cancer.

The invention discloses a prognosis prediction system and method for lung cancer patients, belonging to the field of neural networks. The system comprises: an annotation module for annotating digitalpathological images to obtain annotated images; an acquisition module for acquiring prognosis data and survival time corresponding to the annotated images; a collection module for respectively addingeach annotated image, the prognosis data and the survival time into a data set to generate training data sets; a classification module for dividing the training data sets into a training set and a test set; a training module for training the training set to obtain a prognosis prediction model for lung cancer patients; a test module for inputting the test set into the prognosis prediction model forthe lung cancer patients to obtain corresponding prediction accuracy; and a prediction module for inputting the digital pathological image of a patient to be detected into the prognosis prediction model for the lung cancer patients to obtain predicted prognosis data and predicted survival time. The prognosis prediction system and method have the following beneficial effect: a doctor can formulatea treatment scheme according to prediction results, so treatment effect is improved, and survival time is prolonged.

A gene for predicting or diagnosing the prognosis of early-stage breast cancer and to a use thereof is disclosed, and more specifically a genetic marker for predicting or diagnosing the prognosis of breast cancer, including TRBC1 (T cell receptor beta constant 1), BTN3A2 (butyrophilin, subfamily 3 member A2) or HLA-DPA1 (major histocompatibility complex, class II, DP alpha 1) for providing information necessary for predicting or diagnosing the prognosis of a breast cancer patient. The genetic marker allows the prediction or diagnosis of the prognosis of a breast cancer patient, and can therefore advantageously be used for the purpose of providing a direction as to the future course of breast cancer treatment, including a decision on whether anticancer therapy is necessary.

The present invention relates to a gene for predicting or diagnosing the prognosis of early-stage breast cancer and to a use thereof, and more specifically relates to a genetic marker for predicting or diagnosing the prognosis of breast cancer, including TRBC1 (T cell receptor beta constant 1), BTN3A2 (butyrophilin, subfamily 3, member A2) or HLA-DPA1 (major histocompatibility complex, class II, DP alpha 1) for providing information necessary for predicting or diagnosing the prognosis of a breast cancer patient. The genetic marker of the present invention allows the prediction or diagnosis of the prognosis of a breast cancer patient, and can therefore advantageously be used for the purpose of providing a direction as to the future course of breast cancer treatment, including a decision on whether anticancer therapy is necessary.

A stroke diagnosis and prognosis prediction system includes: an image acquisition unit configured so as to receive a plurality of images including at least a part of a human brain; an image alignment unit for aligning the plurality of images on the basis of a standard brain image; a lesion area detection and mapping unit for respectively detecting lesion areas from the plurality of images, and mapping the detected lesion areas so as to generate one mapping image; a matching and correction unit, which scales a mapping image so as to match the same to the standard brain image and performs image correction on the mapping image; a three-dimensional image generation unit storing the mapping image in a three-dimensional data space, thereby generating a three-dimensional lesion image; and a stroke diagnosis unit for diagnosing a stroke on the basis of the three-dimensional lesion image.

The invention discloses a method for detecting variation and methylation of tumor specific genes in ctDNA (complementary deoxyribonucleic acid). According to the method, variation (including point mutation, insertion and deletion mutation, HBV integration and other mutation forms) and/or methylation of tumor specific genes in ctDNA can be detected in one sample at the same time, the sample size requirement is low, and an MC library prepared through the method can support 10-20 times of subsequent detection, the result of each detection can represent mutation conditions of all original ctDNA specimens and methylation modification conditions of enzyme cutting site coverage areas, and sensitivity and specificity cannot be reduced. The method has important clinical significance for early tumorscreening, disease tracking, curative effect evaluation, prognosis prediction and the like, and has important application value.

The invention discloses a construction method of an imaging omics model for pancreatic cancer prediction. The construction method provided by the invention comprises the following steps: acquiring data; feature extraction; constructing a feature dimension reduction and image omics model; constructing and verifying an image omics model; survival analysis. The method has the following beneficial effects that the method has good practicability; the imaging omics model for pancreatic cancer early stage and progression stage classification and prognosis prediction constructed by the method can accurately distinguish pancreatic cancer patients in the early stage and progression stage, predict prognosis of the patients, analyze all focus tissues instead of being limited to local parts (such as puncture and the like), and provide more comprehensive information of focuses. The method is non-invasive, non-invasive, good in repeatability and comprehensive in detection, does not increase extra medical expenses of patients, and is easy to operate and convenient for clinical popularization and application.

The embodiment of the present application discloses a method and system for predicting tumor prognosis. The tumor prognosis prediction method includes: acquiring the characteristic information of a tumor patient, the characteristic information reflecting at least the gene mutation information of the tumor patient at a tumor site; and determining, according to a tumor prognosis prediction model andbased on the characteristic information of the tumor patient, a prognosis prediction result of the tumor patient. The method establishes a tumor prognosis prediction model based on the tumor patientdata, and can improve the accuracy of tumor prognosis prediction.

The invention discloses a method for establishing a severe spinal cord injury prognosis prediction model, and the method is characterized by comprising the following steps: extracting clinical data of cases of patients diagnosed as spinal cord injury: 1) incorporating the following clinical characteristics; 2) preprocessing the clinical features: processing missing data through different filling methods according to the types of the clinical features; 3) incorporating an algorithm combination of a feature selection method and a machine learning classification algorithm, wherein the feature selection method is used for screening clinical features with significant prediction values, and the selected clinical features are used for training the machine learning classification algorithm; 4) selecting the algorithm combination with the maximum area AUC under the micro average curve from the prediction performance of the algorithm combination in the step 3) in the training data set, and integrating the algorithm combination by using a stacking method to obtain a prediction model. The invention has accurate and objective performance for predicting the prognosis of the patient with severe spinal cord injury.

The invention discloses a CREPT (Cell-cycle Related and Expression-elevated Protein in Tumor) antibody for identifying tumor cells or tumor tissues. The invention provides an application of a reagent for detecting CREPT protein in the following steps (a), (b) and (c): (a) assisting in identifying the tumor cells; (b) assisting in identifying the tumor tissues; and (c) assisting in identifying tumor patients. The CREPT antibody discovers that the expression level of a CREPT gene in a tumor cell line is much higher than that in normal cells, and the expression level of the CREPT gene in the tumor tissues is much higher than that in paracancerous tissues and normal tissues. Therefore, by applying the antibody provided by the invention, the tumor cells, the tumor tissues and the tumor patients can be subjected to assisted identification. The CREPT antibody has the advantages of simplicity and convenience in operation, low cost, high accuracy, prognosis prediction and the like.