51 results about "Cancer recurrence" patented technology

The invention discloses a cancer recurrence probability deep learning prediction system based on multi-modal time sequence characteristics, and relates to the technical field of medical data processing. The method comprises the following steps: constructing a multi-modal time sequence data set by collecting clinical static data, dynamic treatment data and time sequence monitoring data of a patient; missing value filling and abnormal value elimination based on sliding Z-score are carried out on the data to form an effective data set; screening key clinical static features by using a random forest model; constructing a radiotherapy cumulative toxicity model to quantify residual toxicity, and generating dynamic characteristics by combining the dose change rate three-state characteristics; converting the features into a four-dimensional time sequence tensor through a dynamic sliding window; optimizing LSTM hyper-parameters by adopting random search, and training a high-precision prediction model to output a recurrence probability; finally, differentiated clinical intervention is triggered based on risk grading, a'prediction-decision 'closed-loop system is formed, and the initiative and accuracy of cancer recurrence diagnosis and treatment are remarkably improved.

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 present invention relates to nitrogen-containing derivatives of narasin having the formula (I), as well as synthesis and uses thereof, in particular for the treatment and / or prevention of cancer. Also disclosed are medicaments including the nitrogen-containing derivatives of narasin, which include medicaments for preventing cancer metastasis and / or for preventing cancer recurrence and / or for decreasing resistance to a chemotherapy in a subject.

Presented herein are systems and methods for determining scores related to metastatic recurrence of cancers in subjects using multimodal machine learning (ML) architectures. A computing system may obtain, for a subject at risk of recurrence of cancer, a dataset comprising at least one of: (i) a biomedical image of a tissue sample from an organ associated with the cancer or (ii) a text report identifying a plurality of characteristics of a tumor associated with the cancer. The computing system may apply an ML architecture to at least one of the biomedical image or the text report of the dataset. The computing system may determine, based on applying the ML architecture, a score indicating a likelihood of recurrence associated with cancer in the subject. The computing system may generate a classification of the subject for administration of a therapy for the cancer, in accordance with the score.

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.

The present application relates to scaffolds for treating solid tumor cells and evasion variants. Implantable stents are described that treat solid tumors and evasion variants and provide effective vaccination against cancer recurrence. The scaffold includes a genetically reprogrammed lymphocyte and a lymphocyte activating portion.

This invention relates generally to biomarkers that are useful for determining whether a cancer is likely to recur in a subject, and / or determining whether a subject is likely to respond to cancer therapy. The invention therefore relates to methods, kits and compositions for determining whether a cancer is likely to recur in a subject, or whether a subject is likely to respond to cancer therapy, and to methods of treatment based on a determination that a subject with cancer is likely to respond to cancer therapy.

The disclosure, in one aspect, relates to methods for treating cancer in a subject, the methods including at least the steps of administering at least one drug from each of at least two classes selected from an inhibitor of poly-ADP ribose polymerase (PARP inhibitor), an inhibitor of DNA-dependent protein kinase catalytic subunit (DNA-PKcs inhibitor), an inhibitor of wild-type isocitrate dehydrogenase (IDH inhibitor), an inhibitor of histone acetyltransferase (HAT inhibitor), an inhibitor of histone deacetylase (HDAC inhibitor), an inhibitor of DNA polymerase Θ (POLΘ inhibitor), an inhibitor of platelet-derived growth factor receptor (PDGFR inhibitor), and a DNA alkylating agent to the subject. In any of these aspects, the disclosed method allows for administering lower doses of the drugs in combination compared to administration as single drugs while preventing the recurrence of the cancer, preventing drug resistance of the cancer, and reducing side effects associated with cancer treatment.

The invention provides a biomarker for judging cancer metastasis or cancer recurrence and application thereof, and relates to the technical field of molecular biology. The biomarker based on STEAP4 protein and / or IGFBP6 protein provided by the invention has the advantages of convenience, rapidness and the like in detection, can provide dynamic monitoring of diseases for patients, and achieves the technical effect of timely discovering cancer metastasis signs or cancer recurrence signs.

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 invention belongs to the technical field of traditional Chinese medicines, and particularly relates to a traditional Chinese medicine composition for preventing and treating tumor cancer and resisting relapse, which comprises the following raw materials in parts by weight: 1-50 parts of sanqi, 1-50 parts of Chinese mahonia, 10-100 parts of gynostemma pentaphylla, 1-50 parts of wild poria cocos, 1-50 parts of local scorpion and 1-60 parts of wild gastrodia elata. The whole formula is synergistic, and the cachexia metabolic disorder is improved by adjusting the intestinal flora-immune axis; the traditional Chinese medicine composition integrates three treatment methods of promoting blood circulation to remove blood stasis, clearing away heat and toxic materials and supporting healthy energy, eliminates evil without hurting healthy energy through multi-channel cross regulation and control, and provides a traditional Chinese medicine innovative scheme for tumor prevention and treatment.

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.

The application discloses a cancer recurrence probability deep learning prediction system based on multi-modal time sequence characteristics, and relates to the technical field of medical data processing; the application collects clinical static data, dynamic treatment data and time sequence monitoring data of patients, constructs a multi-modal time sequence data set, carries out missing value filling and abnormal value elimination based on a sliding Z-score on the data, forms an effective data set, uses a random forest model to screen key clinical static features, constructs a radiotherapy cumulative toxicity model to quantize residual toxicity, combines a dose change rate tri-state feature to generate dynamic features, converts the features into a four-dimensional time sequence tensor through a dynamic sliding window, adopts random search to optimize LSTM hyperparameters, trains a high-precision prediction model to output a recurrence probability, and finally triggers differentiated clinical intervention based on risk grading, forms a "prediction-decision" closed-loop system, and significantly improves the initiative and accuracy of cancer recurrence diagnosis and treatment.

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.

To provide a method for selectively targeting cancerous cells while sparing normal healthy tissues.SOLUTION: Provided are a method of killing carcinoma cells comprising administering an effective amount of a NEO-201 antibody to a patient in need thereof; a method of treating a carcinoma comprising administering an effective amount of a NEO-201 antibody to a patient in need thereof; and a method of preventing the recurrence of a carcinoma comprising administering an effective amount of a NEO-201 antibody to a patient in need thereof.SELECTED DRAWING: Figure 1

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 disclosure, in one aspect, relates to methods for treating cancer in a subject, the methods including at least the steps of administering at least one drug from each of at least two classes selected from an inhibitor of poly-ADP ribose polymerase (PARP inhibitor), an inhibitor of DNA-dependent protein kinase catalytic subunit (DNA-PKcs inhibitor), an inhibitor of wild-type isocitrate dehydrogenase (IDH inhibitor), an inhibitor of histone acetyltransferase (HAT inhibitor), an inhibitor of histone deacetylase (HDAC inhibitor), an inhibitor of DNA polymerase Θ (POLΘ inhibitor), an inhibitor of platelet-derived growth factor receptor (PDGFR inhibitor), and a DNA alkylating agent to the subject. In any of these aspects, the disclosed method allows for administering lower doses of the drugs in combination compared to administration as single drugs while preventing the recurrence of the cancer, preventing drug resistance of the cancer, and reducing side effects associated with cancer treatment.

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.