52 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 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 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).

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 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 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.

The application provides a protein marker for predicting the sensitivity of adjuvant chemotherapy of an ovarian cancer patient and an application, based on proteomics data of an ovarian cancer tissue sample, combining a preliminary screening model and a classification model and PRM technology, screening a plurality of protein markers with representative meanings for predicting the sensitivity of adjuvant chemotherapy of an ovarian cancer patient after neoadjuvant therapy, training an ovarian cancer patient chemotherapy sensitivity prediction model by using the screened protein markers, predicting the chemotherapy sensitivity of an ovarian cancer patient who has not undergone adjuvant therapy by using the ovarian cancer patient chemotherapy sensitivity prediction model, if the chemotherapy sensitivity is low, it means that the probability of recurrence of the patient after 6 months of adjuvant therapy is high, so medical staff can adjust the treatment plan accordingly, avoid unnecessary chemotherapy treatment for ovarian cancer patients, and play a preventive effect.

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

The application discloses application of a CDK4 / 6 inhibitor combined with oxaliplatin in preparation of a treatment drug for chemotherapy-resistant colorectal cancer. The combined use of the CDK4 / 6 inhibitor combined with oxaliplatin has excellent therapeutic effect on locally advanced rectal cancer which is resistant to a first-line chemotherapy scheme, can overcome the drug resistance of the rectal cancer patient to the chemotherapy drug, so that the tumor proliferation is inhibited, and the apoptosis of the cancer cell is promoted. The application provides a combined drug treatment scheme for neoadjuvant therapy of locally advanced rectal cancer chemotherapy resistance, can achieve the purpose of effectively treating or assisting in treating the rectal cancer, and provides a new way for treating, diagnosing or predicting the rectal cancer patient.

The invention relates to a compound, 2-hydroxypropyl-β-cyclodextrin (HPβCD), for use in methods of treating cancer, as well as to methods of treating a cancer using the compound HPβCD and to uses of the compound in the manufacture of one or more medicaments for the treatment of cancer. In particular, the compound HPβCD may be useful as an adjuvant or neoadjuvant therapy. Also provided, are pharmaceutical compositions comprising the HPβCD, optionally together with additional chemotherapeutic or cancer therapy agents.

The invention provides an antibody-drug conjugate targeting TROP2, and a preparation method and application thereof. The TROP2-targeting antibody-drug conjugate shows very strong tumor specific cytotoxicity, and shows a stronger bystander effect in an in-vivo experiment, so that the TROP2-targeting antibody-drug conjugate can be used as a medical drug, especially a single drug or a combined drug for treating tumors, and used for neoadjuvant therapy of the tumors and the like.

The invention discloses a liver cancer clinical new auxiliary curative effect prediction system and medium, and the prediction system comprises an input unit which is used for inputting the value of a diagnostic serological detection index of a patient; the prediction unit is used for generating a prediction result of whether the new adjuvant therapy is invalid to the patient or not based on the prediction model and the diagnostic serological detection indexes; and the output unit is used for outputting the prediction result. According to the present invention, the diagnosis serological detection index of the patient can be conveniently and rapidly obtained based on the detection results of the blood biochemical detection, the specific protein detection and the like during the liver cancer treatment process so as to predict whether the new adjuvant therapy of the tiranibizumab combined with the lenvatinib is invalid to the patient based on the diagnosis serological detection index; therefore, the delay of the operation process of a part of patients with invalid new adjuvant therapy is avoided, and the method has wide application value clinically.

The application discloses a triple negative breast cancer post-neoadjuvant therapy postoperative recurrence risk prediction system and method based on multi-modal time sequence medical image data, and belongs to the medical image analysis field.The system comprises: a data processing module for constructing a multi-modal data set; a multi-modal feature extraction and screening module for extracting deep learning, imageomics and tumor habitat features from the region, and performing feature screening; a model training module for constructing a time sequence model based on a Transformer architecture, and training through a multi-task learning strategy of integrating time consistency constraints and gene association auxiliary loss; and a recurrence risk prediction module for loading the trained model, and outputting a recurrence probability and a risk level.The application realizes dynamic and accurate quantification of the recurrence risk of triple negative breast cancer patients by fusing multi-modal time sequence images and gene information, and provides support for clinical individualized treatment decisions.

Disclosed is a medical bead string-type positioning and marking device, which comprises a long cylindrical needle tube for puncturing, a marker body for tumor positioning, and a limiting device acting between the marker body and the needle tube. The marker body comprises a bead string and a hook part, the hook part is arranged at a front end of the bead string, the bead string consists of a strand and a plurality of bead bodies uniformly fixed on the strand, and the plurality of bead bodies are spaced apart from each other. The limiting device comprises a limiting block, and an upper shell and a lower shell that are snap-fitted, and an elastic clamping groove corresponding to the position of the bead string is formed in a contact surface of the snap-fitted upper shell and lower shell. This device enables multi-directional marking and positioning of the tumor position of a patient by means of the marker body, thereby providing a basis and guarantee for subsequent precise tumor excision that takes the bead string as a reference. It is particularly suitable for tumor marking and positioning of a breast cancer patient receiving neoadjuvant therapy and planning to conserve the breast.