68 results about "Neoadjuvant therapy" patented technology

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention relates to the technical field of medical decision and artificial intelligence, and discloses an artificial intelligence-driven new adjuvant therapy scheme optimization method and system for antral sinus cancer, and the method comprises the steps: S1, obtaining clinical data of a antral sinus cancer patient, and carrying out the preprocessing; s2, screening core features by adopting an iterative ReliefF algorithm, and constructing composite features; s3, constructing a prediction model based on a Bayesian optimization K-nearest neighbor algorithm, taking the composite features as input of the prediction model to predict treatment response, and outputting individual tumor recession grade probability distribution of the patient; s4, taking the tumor recession grade probability distribution as a core component of a state space, inputting the tumor recession grade probability distribution into a double-layer reinforcement learning framework based on a penalty mechanism to optimize a treatment decision, and outputting a personalized treatment strategy parameter set; and S5, performing real-time scanning and verification on the drug dosage parameters in the strategy parameter set, and outputting a final approved personalized treatment scheme. The precision and intelligent level of gastric antrum cancer treatment is improved.

The invention provides a pathological recession grading system and method after neoadjuvant therapy of gastric cancer, and the system comprises a data input module which obtains a preoperative medical image and a postoperative pathological image of a target patient, a feature extraction module which extracts image omics and pathological omics features, a multi-modal fusion module which integrates the features to obtain a fusion vector, and a classification module which carries out classification on the fusion vector. The grading model module processes to obtain a result containing discrete grades, continuous scores and joint prediction, the interpretable module generates interpretable contents such as a regional heat map, and the clinical interface module integrates an evaluation report. The system improves the clinical credibility, and achieves the breakthrough of grading precision, efficiency and generalization ability.

Disclosed are multi-peptide compositions and / or methods of use of the multi-peptide compositions for patients with resected (or definitively treated) high-risk uveal melanoma. Pharmaceutically acceptable embodiments administered to patients may raise an immune system response against uveal melanoma cells, leading to a decrease in uveal melanoma cell number. Further embodiments may take the form of an adjuvant therapy or a neoadjuvant therapy, as monotherapy or in combination with other therapies.

The invention belongs to the technical field of medical data processing, and discloses a rectal cancer neoadjuvant therapy curative effect prediction method and system based on multi-modal knowledge decomposition. The invention provides a multi-modal data fusion algorithm based on multi-modal knowledge decomposition, which decomposes multi-modal data into a plurality of independent modal knowledge, and does not lose important inter-modal complementary information while reducing redundant information. According to the method, the unsupervised clustering algorithm and the supervised CNN algorithm are used at the same time, and compared with a traditional image selection method only using unsupervised random selection or based on information entropy, the interference of noise data on image screening can be reduced, so that representative enteroscope images and CT images can be selected.

The invention relates to the technical field of rectal cancer, and discloses an AI-based local advanced rectal cancer neoadjuvant therapy curative effect prediction system, which comprises a data collection and processing module, a prediction model construction and training module, a prediction model verification and optimization module and a clinical application module, the data collecting and processing module is used for collecting clinical data, image data, radiotherapy target area data, radiotherapy dose data, radiobiology data and genomics data of a patient and then transmitting the data to the prediction model building and training module, and the prediction model building and training module extracts and selects key features in the data. And constructing a tumor segmentation multi-modal fusion and calibration network prediction model based on the PET / CT image. According to the invention, the data of the patient is collected through the data collection and processing module, so that the accuracy of radiotherapy effect and side reaction prediction is improved through the constructed prediction model, and the problem of feature capture limitation caused by a single center and a single mode of an existing AI model is solved.

The invention relates to a plasma lipid isomer biomarker for predicting survival benefit of neoadjuvant therapy of breast cancer and application of the plasma lipid isomer biomarker. The plasma lipid isomer biomarker is PC 17: 1 (n-8) 18: 1 (n-7), PC 18: 018: 1 (n-7), PC 14: 018: 1 (n-10), PE 16: 018: 1 (n-7), PE 14: 018: 1 (n-7) and PC 16: 1 (n-7) 18: 1 (n-9). The invention also constructs a breast cancer NAT survival benefit prediction model. According to the method, the survival benefit probability of the patient can be accurately evaluated, and a scientific basis for personalized treatment decision is provided for clinic, so that a more accurate treatment scheme is realized, the treatment effect of the patient is optimized, and excessive treatment or insufficient treatment is avoided.

The present invention relates to methods for photodynamic therapy (PDT) of bladder cancer and their use as adjuvant or neoadjuvant therapy in the treatment of bladder cancer. The present invention provides a composition comprising hexyl 5-ALA ester (HAL) or a pharmaceutically acceptable salt thereof for use in a method of photodynamic therapy of bladder cancer wherein the composition is dripped into the bladder of a patient in need of such treatment and the interior of the bladder is exposed to blue light having an energy flow rate of 1.5 to 12.5 mW / cm2.

The present disclosure provides methods for treating, reducing the severity of, inhibiting the growth of a tumor, or inducing necrosis of a tumor, wherein the method includes selecting a patient with cancer (e.g., liver cancer, lung cancer, or head and neck cancer) in need thereof and administering to the patient a neoadjuvant therapy comprising a therapeutically effective amount of a programmed death 1 (PD-1) inhibitor (e.g., cemiplimab or a bioequivalent thereof) in combination with radiation therapy (e.g., SBRT) or an anti-LAG-3 antibody (e.g., fianlimab) followed by surgical resection, and optional administration of a programmed death 1 (PD-1) inhibitor (e.g., cemiplimab or a bioequivalent thereof) as post-surgery adjuvant therapy. In certain embodiments, the cancer is liver cancer, such as hepatocellular carcinoma (HCC).

The present disclosure provides methods for treating, reducing the severity of, inhibiting the growth of a tumor, or inducing necrosis of a tumor, wherein the method includes selecting a patient with cancer (e.g., liver cancer, lung cancer, or head and neck cancer) in need thereof and administering to the patient a therapeutically effective amount of a programmed death 1 (PD-1) inhibitor (e.g., cemiplimab or abioequivalent thereof) as neoadjuvant therapy followed by surgical resection and optional administration of a programmed death 1 (PD-1) inhibitor (e.g., cemiplimab or a bioequivalent thereof) as post-surgery adjuvant therapy. In certain embodiments, the liver cancer is hepatocellular carcinoma (HCC), the lung cancer is non-small cell lung cancer (NSCLC), or the head and neck cancer is head and neck squamous cell carcinoma (HNSCC).

The invention discloses a method and a device for constructing a prognosis prediction model of a patient after esophageal cancer neoadjuvant therapy. The method is applied to the technical field of prediction models. An initial training data set of an esophageal cancer patient is acquired from a cancer database; performing binary classification on the esophageal cancer patients in the initial training data set according to the ten-year survival states, and obtaining a training data set according to variable characteristics of the esophageal cancer patients in the initial training data set after binary classification and the corresponding survival states within the ten-year period; and finally, training the machine learning algorithm model by using the training data set, and constructing a prognosis prediction model for the patient after the neoadjuvant therapy of the esophageal cancer. In the process, a cancer database is used for selecting data, the influence of data heterogeneity on the performance of the model is reduced, the generalization ability of the model is improved, a prediction time window is extended to ten years, and a longer time dimension is provided for clinical decision making.

Provided herein is a neoadjuvant therapy and / or adjuvant therapy for the treatment of uveal melanoma, inter alia, ocular tumors in uveal melanoma to reduce the incidence of metastasis and / or enucleation and / or preserving eye function.

The present invention relates to antibodies that inhibit human CD39 enzyme activity and methods of treating cancer using compounds, including in combination with anti-PD (L) 1 antibodies. The anti-CD39 antibody and the anti-PD (L) 1 antibody can be administered as neoadjuvant therapies for the treatment of lung cancer. The invention also relates to dosing regimens for treatment with an anti-CD39 antibody and an anti-PD (L) 1 antibody in combination.

Indivi dualized neoantigen vaccines, as well as methods for their use in subjects to treat urothelial orbladder cancer are provided. In some embodiments, individualized neoantigen vaccines are used with an immune checkpoint inhibitor and optionally enfortumab vedotin to treat a urothelial or bladder cancer after surgical resection. Prior to surgical resection, in some embodiments, the subject is administered a neoadjuvant therapy comprising an immune checkpoint inhibitor, individualized neoantigen vaccines, and, optionally, enfortumab vedotin.

The invention discloses a liver cancer neoadjuvant therapy curative effect prediction method and system based on a multi-sequence MRI (Magnetic Resonance Imaging) image. The method comprises the following steps: acquiring multi-sequence MRI images of a target liver before and after treatment; performing cutting and size normalization processing on the multi-sequence MRI image to obtain a preprocessed MRI image; and inputting the preprocessed MRI image into a trained image classification model to obtain a new adjuvant therapy curative effect prediction result. According to the method, the problems of small sample size, unbalanced categories and the like in medical images are solved, and the classification capability of the model on small sample liver cancer MRI image data is effectively improved.

The invention belongs to the technical field of biology, and particularly relates to a detection method of a tumor serological marker. The application of the reagent for detecting the concentration of the sGRP78 protein in the serum of the breast cancer patient in preparation of a product for evaluating the curative effect of the breast cancer patient after neoadjuvant therapy is characterized in that the number of neoadjuvant therapy is four. In order to overcome the defects of dynamic monitoring deficiency, invasiveness, high cost and insufficient serum marker sensitivity in the existing neoadjuvant therapy metaphase evaluation of the breast cancer, the invention provides a detection kit, namely, the serum marker sGRP78 is monitored in the neoadjuvant therapy metaphase evaluation of the breast cancer, and the sensitivity of the serum marker sGRP78 in the neoadjuvant therapy metaphase evaluation of the breast cancer is improved. Therefore, a noninvasive, low-cost, high-sensitivity and high-specificity neoadjuvant therapy metaphase evaluation index is achieved.

This application relates to the field of biotechnology, specifically disclosing the application of immune cell subsets in the preparation of detection products for assessing the efficacy of neoadjuvant therapy and the risk of recurrence and metastasis in breast cancer. The detection method described in this application is non-invasive and convenient, requiring only peripheral blood, and is easy to repeat sampling and dynamic monitoring. This application also provides a predictive model for the efficacy of neoadjuvant therapy in breast cancer, and a predictive model for the risk of recurrence and metastasis in breast cancer treated with neoadjuvant therapy. The constructed predictive model has a high AUC value (0.82-0.87), and good sensitivity and specificity. Furthermore, through regular postoperative monitoring, abnormal dynamic changes in immune cell subsets can be detected, potentially indicating signs of recurrence earlier than imaging and traditional tumor markers. This helps identify advantageous populations before treatment and high-risk populations after treatment, enabling optimization of treatment plans and individualized adjustment of follow-up density.

This disclosure provides methods for treating tumors, reducing tumor severity, inhibiting tumor growth, or inducing tumor necrosis, wherein the methods include: selecting a patient with cancer (e.g., liver cancer, lung cancer, or head and neck cancer) in need of treatment and administering neoadjuvant therapy to the patient in a combination of a therapeutically effective amount of a programmed death 1 (PD-1) inhibitor (e.g., cimiprimab or its bioequivalent) and radiation therapy (e.g., SBRT) or an anti-LAG-3 antibody (e.g., fenpromab), followed by surgical resection and optionally administration of a programmed death 1 (PD-1) inhibitor (e.g., cimiprimab or its bioequivalent) as postoperative adjuvant therapy. In some embodiments, the cancer is liver cancer, such as hepatocellular carcinoma (HCC).

The present invention relates to a method for treating tumors or inhibiting tumor proliferation, comprising administering, as a neoadjuvant therapy, a therapeutically effective amount of a programmed death-ligand 1 (PD-L1) inhibitor (for example, an anti-PD-L1 antibody) to a cancer patient in need thereof, followed by surgical resection. In particular, the present invention relates to a method for administering a PD-L1 inhibitor as a neoadjuvant therapy prior to surgery to patients with surgically resectable upper gastrointestinal cancer, for example, localized gastric cancer, esophageal cancer or liver cancer. It has been identified that the administration of a PD-L1 inhibitor, particularly an anti-PD-L1 antibody, exhibits an excellent therapeutic effect as a neoadjuvant therapy for patients with surgically resectable localized gastric cancer, esophageal cancer, and liver cancer.

This disclosure pertains to the field of biomedicine and relates to the use of an anti-HER2 antibody-drug conjugate (ADC) for the treatment of breast cancer. It also relates to a method for treating a subject with breast cancer, the method comprising administering an anti-HER2 antibody-drug conjugate to the subject. Furthermore, it relates to the use of the anti-HER2 antibody-drug conjugate in the preparation of a medicament for neoadjuvant therapy of breast cancer. The method provides benefit to breast cancer subjects and has a good safety profile.

The invention provides an early prediction method for breast cancer neoadjuvant therapy based on a pathological full-slide image, and belongs to the technical field of breast cancer auxiliary diagnosis and treatment. The method comprises the following steps: firstly, acquiring a breast cancer digital pathological image sample, and performing color normalization to obtain a preprocessed image; then, background removal and block cutting are carried out under the amplification factors of 10X and 40X, and features are extracted by using a pre-training standard model to obtain two groups of vectors; constructing a deep learning model, inputting two groups of vectors, performing multi-scale feature fusion analysis, and outputting a probability value; and finally, on the basis of semi-supervised multi-instance learning, dividing samples into a training set and a verification set in proportion, training the model, adjusting hyper-parameters according to the verification set, and retraining full samples to obtain a final model for prediction. The technical problems that tumor cell microscopic information cannot be mined through existing MRI image analysis, the manual labeling cost of a digital pathology full supervision method is high, and data size difference and tumor surrounding information mining are difficult to consider are solved.

The present invention relates to neoadjuvant therapies for bladder cancer in bladder cancer patients scheduled for bladder resection and methods of implementing such neoadjuvant therapies. Specifically, the present invention relates to a composition comprising hexyl 5-ALA ester (HAL) or a pharmaceutically acceptable salt thereof for use in a neoadjuvant therapy of bladder cancer in a bladder cancer patient scheduled for bladder resection, the therapy comprising dripping said composition into the bladder of said patient and exposing the interior of said bladder to light.

The present invention relates to anti-GDF15 antibodies for use in the neoadjuvant treatment of cancer.

Provided herein is a neoadjuvant therapy and / or adjuvant therapy for the treatment of uveal melanoma, inter alia, ocular tumors in uveal melanoma to reduce the incidence of metastasis and / or enucleation and / or preserving eye function.

The invention is applicable to the technical field of medicines for treating melanoma, and provides application of oncolytic viruses combined with PD-1 monoclonal antibodies in preparation of medicines for treating melanoma. The invention further provides a melanoma treatment medicine. The medicine comprises the oncolytic virus and the PD-1 monoclonal antibody. According to the invention, the oncolytic virus and the PD-1 monoclonal antibody are combined for use, due to the oncolytic characteristic of the herpes simplex virus, the modified HSV-1 virus vector performs specific oncolytic treatment at an injection site to kill tumor cells, and the virus vector is expressed at the tumor site to generate high-concentration GM-CSF to enhance the anti-tumor immune function of a body and exert a side-killing effect; the inhibition effect is generated on the metastatic tumor at the far end; the PD-1 monoclonal antibody can be combined with PD-1 on the surface of a T cell to block the combination of the PD-1 monoclonal antibody and ligands PD-L1 and PD-L2, so that the immunosuppression of tumor cells on a PD-1 signal channel is eliminated, the proliferation of the T cell can be promoted, the function of the T cell can be activated, the tumor growth can be inhibited, and the combination of the PD-1 monoclonal antibody and the PD-1 monoclonal antibody can improve the immune microenvironment of the melanoma and improve the treatment efficiency; the invention shows excellent curative effect and good prospect in the field of neoadjuvant therapy of high-risk melanoma.

The embodiment of the application provides a kind of multi-modal gastric cancer neoadjuvant therapy curative effect prediction method, device and equipment based on image and liquid biopsy genomic data and storage medium, it is related to gastric cancer neoadjuvant therapy technical field, the method comprises: obtaining the image data of tumor focus of gastric cancer patient before receiving gastric cancer neoadjuvant therapy, liquid biopsy genomic data and clinical baseline data;First key feature is extracted from image data and input into first prediction model to obtain first curative effect;Second key feature is extracted from genomic data and input into second prediction model to obtain second curative effect;According to first curative effect and second curative effect, the predicted curative effect of gastric cancer patient after receiving gastric cancer neoadjuvant therapy is determined.The above-mentioned three kinds of information of the gastric cancer patient to be predicted are input into the curative effect prediction model trained based on the information of sample gastric cancer patient by the above scheme, i.e.The curative effect of the gastric cancer patient to be predicted after receiving gastric cancer neoadjuvant therapy can be predicted.

The invention relates to the technical field of data processing, and provides an HER2 positive breast cancer neoadjuvant therapy response prediction system based on a multi-omics deep fusion network, and the system comprises a data collection and preprocessing module which is used for obtaining and preprocessing the multi-omics data of a patient, and generating a standardized multi-mode feature set; the multi-omics deep fusion network module comprises a single-mode deep sub-network group which is used for extracting deep features corresponding to each mode from the standardized multi-mode feature set; the cross-modal interaction fusion module is used for distributing fusion weights by calculating feature similarity among modals and performing redundancy filtering on the features to generate cross-modal interaction features; the global information fusion module is used for carrying out global modeling on the cross-modal interaction features and outputting global attention features; and the prediction output module is used for generating a prediction result aiming at the neoadjuvant therapy response based on the global attention features. According to the method, the accurate prediction of the pCR state, the treatment sensitivity and the risk stratification can be realized.

Provided is a method of treating a tumor in an individual by neoadjuvant therapy, wherein the individual has not previously undergone treatment to effectively reduce tumor burden, the method comprising administering an oncolytic chimeric poliovirus construct, or an oncolytic chimeric poliovirus construct and an immune checkpoint inhibitor, followed by reduction of the tumor. The method may further comprise administration of immune checkpoint inhibitor or oncolytic chimeric poliovirus construct following reduction of tumor. Kits for performing the methods are also provided.

The present invention relates to antibodies that inhibit the enzymatic activity of human CD39 and methods of using the compounds to treat cancer, including in combination with an anti-PD(L)1 antibody. The anti-CD39 antibody and the anti-PD(L)1 antibody can be administered as neoadjuvant therapy to treat lung cancer. The invention also relates to dosing regimens for combination treatment with anti-CD39 antibody and an anti-PD(L)1 antibody.

The present application relates to the field of cancer cell identification, and particularly relates to a method and system for identifying different types of cancer cells of a renal pelvis cancer patient based on urine, the method comprising: collecting a urine sample, obtaining a detection sample by using the urine sample; sequencing the detection sample to obtain a cell expression profile of the detection sample; obtaining cell expression profile data of a mixed urothelial cell subpopulation by using the cell expression profile, the mixed urothelial cell subpopulation comprising normal urothelial cells and cancerous urothelial cells; extracting different proto-oncogene expression data from the cell expression profile data of the mixed urothelial cell subpopulation; analyzing the different proto-oncogene expression data to obtain a cancer cell ratio and identify different types of cancer cells. The present method quickly and accurately obtains different types of cancerous urothelial cells through non-invasive examination and proto-oncogene expression profile data, and provides a reference and basis for preoperative neoadjuvant therapy of renal pelvis cancer.