52 results about "Neoadjuvant treatment" patented technology

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 disclosure relates to methods of assessing the success of treatment with durvalumab and chemotherapy for resectable non-small cell lung cancer (R-NSCLC) in patients based on circulating tumor DNA (ctDNA) detection, ctDNA clearance and / or partial clearance and / or variant allele fraction (VAF) before and after such treatment. The disclosure also relates to methods for treating patients with R-NSCLC based on ctDNA detection, ctDNA clearance and / or partial molecular clearance and / or VAF before and after such treatment.

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.

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 application provides an autoantibody marker for predicting the effect of lung cancer immunneoadjuvant therapy on lung cancer patients in the third stage, a series of autoantibody marker molecules are screened by detecting autoantibodies against different antigen targets in blood of lung cancer patients in the third stage, and the autoantibody marker molecules have great correlation with the prediction of the effect of lung cancer immunneoadjuvant therapy, and three autoantibody biomarkers with better prediction of the effect of lung cancer immunneoadjuvant therapy on lung cancer patients in the third stage are further screened by combining with a CART decision tree strategy; the combination of the autoantibody biomarkers can be used for efficiently predicting whether the lung cancer immunneoadjuvant therapy on lung cancer patients in the third stage is effective, providing a reference basis for a clinician to decide a treatment scheme, providing a new prediction means for the effect of lung cancer immunneoadjuvant therapy on lung cancer patients in the third stage, and having important scientific significance and clinical application value.

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.

The present invention relates to the treatment of locally advanced and metastatic muscle-invasive bladder cancers (MIBC). The present invention provides compositions to be used in immunotherapy of MIBC, especially in combination with an anti-PD-1 / PD-L1 / PD-L2 antibody-based therapy. The present invention also provides biomarkers of response to anti-PD-1 / PD-L1 / PD-L2 immune checkpoint blocking antibodies (ICBs), alone or together with anti-CTLA4 antibodies and / or chemotherapy, for best guiding their neoadjuvant use and avoid unefficient administration of potentially toxic drugs to patients with localized bladder cancers.

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 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).

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

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.

The present application belongs to the tumor precision medicine and artificial intelligence medical technology field, and particularly relates to a non-small cell lung cancer neoadjuvant immunotherapy efficacy prediction method based on transfer learning. The method first pretreats the non-small cell lung cancer immunotherapy cohort and the neoadjuvant immunotherapy cohort, selects immunotherapy and neoadjuvant immunotherapy characteristic genes, and based thereon, carries out deep learning training on the immunotherapy cohort to obtain a basic model, finally, the basic model is fine-tuned to the neoadjuvant immunotherapy training data set through transfer learning, and finally used to the neoadjuvant immunotherapy cohort to predict the neoadjuvant immunotherapy effect. The present application improves the stability and generalization ability of the model in the small sample neoadjuvant treatment scene through gene selection and transfer fine-tuning, and can be used for clinical auxiliary decision and population stratification.

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 invention provides a colorectal cancer chemoradiotherapy feedback curative effect evaluation method based on a mixed feature library. Firstly, an MRI image of a patient after new adjuvant therapy is input into a segmentation module, and a tumor lesion area mask is extracted; secondly, according to the tumor lesion area mask, lesion areas and clinical information are input into a feature library construction module, and depth features, radiomics features and clinical features are extracted to construct patient multi-modal mixed features; and further inputting the multi-modal mixed features of the patient into a treatment evaluation module to obtain a neoadjuvant chemotherapy stage staging result of the patient. And finally, for data from a special individual, the doctor inputs the modified mask and score into the man-machine feedback module according to the mask of the tumor lesion area and the score of the diagnosis result, and updates the encoder weight of the segmentation module. The device is effectively suitable for different parts, diseases and special patients. According to the method, a targeted deep learning framework and comprehensive patient characteristics are utilized, and the result of neoadjuvant chemoradiotherapy can be accurately and effectively evaluated. And targeted improvement can be provided after man-machine feedback so as to adapt to special individuals and images.

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 present disclosure relates to methods for treating a tumor or inhibiting tumor proliferation comprising administering to a cancer patient in need thereof a therapeutically effective amount of a programmed death ligand 1 (PD-L1) inhibitor (e.g., an anti-PD-L1 antibody) as a neoadjuvant therapy followed by surgical resection. In particular, the present disclosure relates to methods for administering a PD-L1 inhibitor as a pre-operative therapy to a patient suffering from resectable upper gastrointestinal cancer (e.g., resectable localized gastric, esophageal or liver cancer). Administration of a PD-L1 inhibitor, in particular an anti-PD-L1 antibody, has proved to exhibit an excellent therapeutic effect as a pre-operative therapy for patients suffering from resectable localized gastric, esophageal and liver cancer.

The present application relates to the technical field of medical decision and artificial intelligence, and discloses an artificial intelligence driven method and system for optimizing a neoadjuvant therapy scheme for gastric antrum cancer, comprising: S1: obtaining clinical data of a patient with gastric antrum cancer and preprocessing; S2: screening core features and constructing compound features by using an iterative ReliefF algorithm; S3: constructing a prediction model based on a K nearest neighbor algorithm of Bayesian optimization, and taking the compound features as inputs of the prediction model to predict a treatment response, and outputting a tumor regression grade probability distribution of the patient; S4: taking the tumor regression grade probability distribution as a core component of a state space, inputting into a double-layer reinforcement learning framework based on a punishment mechanism to optimize a treatment decision, and outputting a set of personalized treatment strategy parameters; and S5: performing real-time scanning and checking on drug dosage parameters in the set of strategy parameters, and outputting a final approved personalized treatment scheme. The present application improves the precision and intelligence level of gastric antrum cancer treatment.

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.

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.

The invention discloses application of anlotinib combined chemotherapy in preparation of a medicine for treating esophagus cancer in a new adjuvant therapy mode, experimental verification shows that the anlotinib combined chemotherapy has high clinical and pathological remission rate in the new adjuvant therapy of esophagus cancer patients, and meanwhile compared with chemotherapy combined radiotherapy, the operation difficulty can be reduced, the operation time can be shortened, and the esophagus cancer treatment efficiency can be improved. In addition, the occurrence rate of complications in the perioperative period is not increased, and assistance is provided for accelerating postoperative rehabilitation (ERAS). Therefore, in the neoadjuvant therapy of the esophageal cancer patient, the micromolecular multi-target angiogenesis inhibitor combined chemotherapy can improve the feasibility of the neoadjuvant therapy of the esophageal cancer and reduce the surgical technical threshold after the neoadjuvant therapy, so that the technology can be more widely popularized and applied; according to the application, the micromolecular multi-target angiogenesis inhibitor is used as a new selection mode for new adjuvant therapy of esophageal cancer patients.

The present disclosure concerns fixed dosing of anti-clusterin antibodies, or an antigen binding fragments thereof for treatment of cancer. Methods of treating cancer with a combination therapy comprising an anti-clusterin antibody or an antigen binding fragment thereof and a chemotherapeutic combination comprising two or more antineoplastic agents are also disclosed herein. The present disclosure also provides treatment of metastatic colorectal cancer with anti-clusterin antibodies or antigen binding fragments thereof as a single agent or in combination with FOLFOX for resection of metastases.

This invention discloses an artificial intelligence deep learning model for predicting the sensitivity of neoadjuvant chemotherapy in colorectal cancer, belonging to the field of colorectal cancer treatment prediction technology. Its key technical points include the following steps: S1, data collection; S2, patient grouping; S3, screening predictive factors; S4, artificial neural network modeling; and S5, model validation. This invention successfully screens common clinical indicators for predicting the sensitivity of neoadjuvant chemotherapy in colorectal cancer and constructs an artificial neural network risk prediction model. This model can be applied to clinical practice, providing predictions for the sensitivity of neoadjuvant chemotherapy in colorectal cancer, thereby assisting clinicians in achieving more accurate predictions of chemotherapy efficacy. This invention helps optimize clinical treatment strategies, promotes the development of individualized neoadjuvant therapy regimens for colorectal cancer, and provides new ideas and methods for precision medicine.

The disclosure provides methods of reducing or eliminating the need for ovarian function suppression (OFS) in a premenopausal or perimenopausal female patient with hormone receptor-positive (HR+) breast cancer. The method comprises the administration of an effective amount of lasofoxifene, or a pharmaceutically acceptable salt or a functional derivative thereof in neoadjuvant and adjuvant treatment.

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