29 results about "Cancer recurrence" patented technology

The invention belongs to the technical field of medical health management, and particularly relates to an AI personalized health education system for elderly patients with head and neck cancer, which comprises a content generation and management module, a personalized push engine, an interaction and artificial triage module, an elderly friendly interaction module and a data monitoring and safety module, by adopting an AI + manual hierarchical response mechanism and combining elderly friendly interactive design and operation training support, the participation obstacles that elderly patients are inconvenient to move and poor in informatization ability are broken through, and the intervention compliance and sustainability are improved; according to the intervention method, strict double-group random control test design is adopted, multi-dimensional and multi-time-point effect evaluation is combined with a scientific statistical analysis method, the improvement effect of AI-assisted health education on cancer recurrence, fear, psychological state and life quality of a patient can be accurately verified, and a solid evidence-based basis is provided for clinical popularization.

The invention discloses a sugar chain marker combination for predicting the recurrence risk of a gastric cancer patient after treatment and application of the sugar chain marker combination. The invention discovers that the levels of six specific N-sugar chains (NGA2F, NG1A2F, NA3, NA3Fb, NA4 and NA4Fb) in blood are obviously related to relapse during definite diagnosis of gastric cancer for the first time. On the basis, a gastric cancer recurrence risk prediction model GC-GPSS is constructed through Cox regression. According to the model, individual risk scores are calculated by using a quantification formula containing a fixed regression coefficient (beta), and patients are divided into a high-risk group and a low-risk group according to a preset threshold. A verification result shows that the model can effectively distinguish patient groups with different recurrence risks. The marker combination, the prediction model and the system provided by the invention provide a brand new tool for realizing earlier and more objective recurrence risk assessment in the early stage of gastric cancer diagnosis and treatment, and have important clinical application value.

Compositions comprising migratory type 1 conventional dendritic cells in combination with a positively charged antigen capturing nanoparticle comprising a hydrophobic surface and use of the nanoparticle and dendritic cells optionally in combination with an immune checkpoint inhibitor in methods of treating cancer or preventing or reducing the risk of reoccurrence of a cancer in a subject.

The disclosure includes the identification and use of gene expression profiles, or patterns, with clinical relevance to extended treatment and cancer-free survival in a patient. In particular, the disclosure includes the identities of genes that are expressed in correlation with benefit in a switch in endocrine therapy used to treat a patient. The levels of gene expression are disclosed as a molecular index for predicting clinical outcome, and so prognosis, for the patient. The disclosure further includes methods for predicting cancer recurrence, and / or predicting occurrence of metastatic cancer, after initial treatment with an anti-estrogen agent. The disclosure further includes methods for determining or selecting the treatment of a subject based upon the likelihood of life expectancy, cancer recurrence, and / or cancer metastasis.

The disclosure includes the identification and use of gene expression profiles, or patterns, with clinical relevance to cancer. In particular, the disclosure includes the identities of genes that are expressed in correlation with tumor grade. The levels of gene expression are disclosed as a molecular index for determining tumor grade in a patient and predicting clinical outcome, and so prognosis, for the patient. The molecular grading of cancer may optionally be used in combination with a second molecular index for diagnosing cancer and its prognosis. The disclosure further includes methods for predicting cancer recurrence, and / or predicting occurrence of metastatic cancer. For diagnosis or prognosis, the disclosure further includes methods for determining or selecting the treatment of cancer based upon the likelihood of life expectancy, cancer recurrence, and / or cancer metastasis.

The present disclosure provides gene expression profiles that are associated with cancer, including certain gene expression profiles that differentiate between cancer that is at a high risk of recurrence. The gene expression profiles can be measured at the nucleic acid or protein level. The gene expression profiles can also be used to identify a subject for cancer treatment. Also provided are kits for use in predicting cancer recurrence and / or prognosing cancer and an array comprising probes for detecting the unique gene expression profiles associated with cancer.

Methods for predicting cancer aggressiveness and / or risk of cancer recurrence in a subject are provided. The methods can include the steps of detecting histone gene expression at one or more replication-coupled histone genes in a biological sample from the subject and comparing the levels of histone gene expression at the one or more replication-coupled histone genes in the biological sample to a reference, wherein an increase in the levels of histone gene expression at the one or more replication-coupled histone genes in the biological sample compared to the reference is indicative of cancer aggressiveness and / or risk of cancer recurrence. Also disclosed are methods of ameliorating cancer aggressiveness and / or risk of cancer recurrence in a subject by reducing elevated histone gene expression at one or more replication-coupled histone genes in a subject.

Disclosed are methods of treating a subject in need thereof, comprising: applying an alternating electric field to a target site of a subject in need thereof; and administering a Notch1 inhibitor or a Hes5 inhibitor to the subject in need thereof. Disclosed are methods of preventing recurrence of a cancer in a subject having a cancer, comprising: applying an alternating electric field to a target site of a subject in need thereof; and administering a Notch1 inhibitor or a Hes5 inhibitor to the subject in need thereof. Disclosed are methods of reducing growth of a cancer cell, comprising: applying an alternating electric field to a cell population comprising one or more cancer cells; and contacting a Notch1 inhibitor or a Hes5 inhibitor with the cell population. Disclosed are methods of sensitizing a cancer cell to an alternating electric field, comprising: applying an alternating electric field to a cell population comprising one or more cancer cells; and contacting a Notch1 inhibitor or a Hes5 inhibitor with the cell population.

Methods for screening therapeutic agents for activity against dormant cancer cells, therapeutic agents identified by the methods, and treatment methods related to the same, and methods for screening therapeutic agents for activity against microscopic metastatic tumor cancer cells, therapeutic agents identified by the methods, and treatment methods related to the same.

This disclosure relates to the detection of minimum residual disease (MRD) in surgical drain fluid. The data collected from the drain fluid is also useful in predicting the recurrence of cancer. Comparison is also made between the analysis results of plasma and drain fluid.

This disclosure relates to the detection of minimum residual disease (MRD) in surgical drain fluid. The data collected from the drain fluid is also useful in predicting the recurrence of cancer. Comparison is also made between the analysis results of plasma and drain fluid.

The invention is applicable to the technical field of medical artificial intelligence, and provides a method for constructing a cancer recurrence prediction model based on a multi-modal anti-noise graph network, which comprises the following steps of: firstly, constructing a multi-modal graph structure containing image, pathology, gene and clinical features by utilizing semantic feature extraction and combining similarity calculation; secondly, each modal feature is extracted through a graph convolutional network, and fusion is carried out by adopting a self-adaptive feature selection mechanism; and finally, introducing a noise edge filtering module integrated with a multi-head self-attention mechanism, comparing learning loss with dynamic adjacency matrix updating in combination with the graph, optimizing the graph structure and improving the model robustness. According to the method, complex correlation among patients can be comprehensively modeled, efficient complementation and noise suppression of multi-modal information are realized, so that more accurate and stable results are obtained in cancer recurrence prediction tasks, and the method has remarkable clinical application value.

The present disclosure relates generally to methods and systems for determining cancer recurrence risk. More specifically, the disclosure relates to methods and systems for determining breast cancer recurrence risk using gene expression analysis of multiple biomarkers.

The present invention relates to a composition for diagnosing resistance to a combination of ECF (Epirubicin, Cisplatin and 5-Fluorouracil; ECF), which is used for the treatment of gastric cancer, and a diagnostic kit comprising the same. In order to solve a problem that cancer recurrence and drug resistance occur due to an increase in tumor initiating cells (TICs) after treatment with a drug belonging to the ECF family, NINJ2 may be inhibited by early detection of resistance appearance, thereby suppressing the recurrence of gastric cancer and treating resistance to the drugs.

The present invention relates to a novel drug conjugate comprising a CTLA4 targeting molecule, at least one cytotoxic agent and a linker linking the CTLA4 targeting molecule and the cytotoxic agent, and to compositions and kits comprising such a drug conjugate, and uses thereof, in particular for treating cancer and / or preventing cancer relapse in a subject in need thereof.

A combination, composition, and method of use of (i) a conjugate comprising a targeted immune modulator and (ii) one or more immune checkpoint inhibitors for reprogramming M2-like macrophages into M1-like macrophages to reverse the pro-inflammatory response observed in cancer into an anti-inflammatory response. In at least one embodiment of a method of treating a subject having cancer, the subject is experiencing or at risk of experiencing cancer or cancer recurrence, and the step of administering a first treatment further comprises administering or applying to the subject a therapeutically effective amount of at least one compound or a pharmaceutically acceptable salt or hydrate thereof.

The invention relates to the field of cancer recurrence prediction, in particular to a breast cancer patient recurrence risk prediction method, a computing device and a storage medium, the method comprising: acquiring first feature data, the first feature data comprising at least one of gland hardness and para-carcinoma hardness of a breast, the first feature data characterizing a recurrence risk of a breast cancer patient; and determining the recurrence risk of the breast cancer patient according to the first feature data.

The present application provides a gastric cancer recurrence prediction model. First, a set of new mRNAs (AGTR1, DNER, EPHA7 and SUSD5) for postoperative recurrence of gastric cancer patients is determined through systematic and comprehensive transcriptome analysis. Further, it is verified in a large number of public data sets, and then comprehensively verified in tissue samples of six independent clinical cohorts. Finally, the 4-mRNA small group and clinical features are integrated by risk stratification assessment (RSA) model, which has better prediction performance, and also proves that the 4-mRNA has good therapeutic effect on gastric cancer. Continuous monitoring of patients can be achieved, thereby realizing early intervention and personalized treatment adjustment. Not only is it expected to improve the treatment effect of patients by finding recurrence at a more treatable stage, but also to reduce the physical and economic burden related to traditional diagnostic methods.

A method for predicting risk of recurrence of cancer in an individual with cancer, the method comprising a step of assaying a cancer sample from the individual for positive expression of at least two genes or proteins encoded by those genes selected from the group consisting of FOXM1, UHRF1, PTTG1, E2F1, MYBL2, HMGB2, ATAD2, E2F8, ZNF367 and TCF19, wherein positive expression of the at least two genes correlates with increased risk of recurrence of cancer compared with an individual who does not exhibit positive expression of the at least two genes or proteins encoded by those genes.

The present invention describes an integrated apparatus that enables identification of invasive tumor cells directly from a specimen. The methods using the apparatus can be used to prognose or predict the survivability of the cancer in a subject and the risk of recurrence of the cancer in the subject after treatment. The methods disclosed herein can be used to determine which chemotherapeutic or other therapies most strongly inhibit the tumor cells invasiveness as a form of personalized therapy.

The invention relates to the field of intelligent medical treatment, in particular to a recurrence prediction method, device and program product after cancer ablation based on multi-time sequence images. The method includes: acquiring an image of a cancer patient before tumor ablation; transmitting the tumor pre-ablation image to a multi-task classification prediction model to obtain a recurrence or non-recurrence classification result; the multi-task classification prediction model training process comprises the following steps: acquiring a multi-time sequence image set of a cancer patient; dividing the multi-time sequence image set based on an ablation time node and a latest time node to obtain a pre-ablation image set and a latest image set respectively; and the multi-time-sequence image set is transmitted to the neural network for feature extraction to obtain a first feature, a second feature and a third feature, the first feature, the second feature and the third feature are transmitted to the classification task module for training to obtain a multi-task classification prediction model, and the application has good clinical value.

Methods and systems to determine cancer recurrence in a patient and for integrating a plurality of data types to identify disease specific signals in cell-free DNA (cfDNA). Also provided are genomic data integration methods including a variety of artificial intelligence (AI), machine learning, and deep learning methods to identify disease associated epigenetic landscapes. These methods can elucidate molecular changes in chromatin structure in health and disease.

Provided herein, among other things, is a method for identifying a cancer patient that is at high risk for cancer recurrence. In some embodiments, the method may comprise analyzing a section of a tumor obtained from the cancer patient to quantify interactions between cancer stem cells and PDL1+ macrophages. In these embodiments, a higher level of interactions between cancer stem cells and PDL1+ macrophages in the tumor indicates that the patient is at high risk for cancer recurrence. In some embodiments, the method may comprise identifying the patient has having a high risk of cancer recurrence based on the results of this analysis and administering an adjuvant therapy to the patient.

This specification provides a method for providing information on metachronous recurrence of gastric cancer, the method comprising: measuring the mRNA or protein level of at least one marker gene among CD8, CD4 and PD-L1 in a biological sample isolated from an individual; and determining the likelihood of metachronous gastric cancer recurrence in an individual based on the measured mRNA or protein level of the marker gene.

Biomarkers and methods using the biomarkers for the prediction of the recurrence risk of cancer in a patient are provided.

Methods and systems are described that combine both genomic and epigenetic information to monitor neoantigen targets and molecular residual disease (MRD) in patients undergoing treatment with a personalized cancer vaccine (PCV). Also provided are neural networks trained to determine cancer recurrence in a patient treated with a PCV and methods for integrating a plurality of data types to identify disease specific signals in tissue and cell free DNA. Additionally, the disclosure provides genomic data integration methods including a variety of artificial intelligence (AI), machine learning, and deep learning methods to identify disease associated signals. These methods can elucidate molecular changes in the DNA sequence and epigenetic modifications in health and disease.

The invention discloses a bladder cancer recurrence fear evaluation and cognitive training method and system, and the method comprises the steps: collecting brain wave signals of a bladder cancer patient, and dividing the current fear level of the bladder cancer patient based on a cancer recurrence fear scale; preprocessing the brain wave signal, and extracting a coupling type feature corresponding to the preprocessed brain wave signal; determining network learning features of the brain wave signals based on a convolutional neural network model, and fusing the network learning features with the coupled features to obtain fused features; performing bladder cancer recurrence fear evaluation through a bidirectional long-short term memory network model in combination with the fusion features and the current fear level to obtain a target classification result; and generating a cognitive training strategy based on the target classification result in combination with a historical cognitive training method library, and executing the cognitive training strategy to perform cognitive training on the bladder cancer patient. According to the method, the problem that the evaluation accuracy is relatively poor due to the fact that the cancer recurrence fear evaluation of the bladder cancer is carried out by only using a cancer recurrence fear scale is solved.

The invention provides application of SQ109 in preparation of drugs for preventing and / or treating cancers, and relates to the technical field of drugs. The invention finds that the small molecule compound SQ109 and the target protein DRAM1 have certain binding capacity for the first time, the small molecule compound SQ109 and the target protein DRAM1 can form a stable composite structure, and the small molecule compound SQ109 and the target protein DRAM1 can have a relatively good inhibition effect on cancer cells with high expression of DRAM1 at a relatively low order of magnitude. A new functional component is provided for cancer treatment and cancer recurrence prevention.

The invention relates generally to the field of oncology. Provided herein is a method for predicting the likelihood of recurrence or prognosis of a cancer in a subject, comprising comparing the level of expression of one or more biomarkers selected from the group consisting of Fibroblast Growth Factor 2 (FGF2), Complement Component 3 (C3), and GTPase, IMAP Family Member 7 (GIMAP7) in a sample obtained from the subject to a reference. In particular, the cancer is nasopharyngeal cancer. Also provided is a composition or solid support comprising a plurality of DNA / RNA complexes, wherein each DNA / mRNA complex in the plurality comprises a biomarker and a first and second DNA probe hybridized to the biomarker, wherein the plurality of DNA / mRNA complexes comprise one or more biomarkers selected from the group consisting of FGF2, C3, and GIMAP7.