30 results about "Prognostic factor" patented technology

The invention discloses a lung cancer lifetime prediction system based on prognosis factor multi-data fusion, and belongs to the technical field of lung cancer prognosis prediction, and the system comprises a multi-source data collection module which is used for collecting prognosis multi-source data of a patient; the multi-source data processing module is used for carrying out preprocessing and feature extraction on the prognosis multi-source data of the patient; the multi-source data fusion module is used for carrying out cross-modal alignment and fine-grained fusion on the extracted multi-modal feature vectors, and capturing a dependency relationship between modals based on a hierarchical attention mechanism to form patient prognosis fusion data; and the survival analysis and prediction module is used for analyzing the prognosis fusion data of the patient according to the lung cancer lifetime prediction model, automatically predicting the lifetime of the patient and displaying the lifetime in a visual form. The problems that existing lung cancer lifetime prediction is low in accuracy and cannot provide support for personalized treatment are solved. The lung cancer lifetime prediction accuracy can be improved, and support can be provided for personalized treatment.

The invention belongs to the technical field of biological medicine, and particularly relates to application of GLUT5 serving as a colorectal cancer biomarker in development of an in-vitro detection kit, a prognosis model and an inhibitor. The GLUT5 is highly expressed in colorectal cancer tumor-related fibroblasts (CAFs), the expression level of the GLUT5 is remarkably related to poor prognosis of a patient, and the GLUT5 can be used as a molecular basis for auxiliary diagnosis and an independent prognosis factor. In mechanism, the GLUT5 promotes tumor progression by mediating fructose metabolism reprogramming of the CAFs. A full-chain solution from early diagnosis and precise prognosis to targeted therapy is provided for colorectal cancer, and the clinical transformation value is remarkable.

The invention discloses application of a lactic acid modified gene ENO1 inhibitor in preparation of a medicine for treating nasopharynx cancer, and relates to the technical field of biological medicines. A head and neck squamous cell carcinoma prognosis model containing seven lactylation related genes is constructed by analyzing a TCGA database, the model can effectively distinguish patient risks, the total lifetime of a high-risk group is remarkably shortened, and a risk score is an independent prognosis factor; the functions of the key gene ENO1 are deeply studied through in-vitro experiments, and experimental results show that silencing of the ENO1 gene in nasopharynx cancer 5-8F cells causes up-regulation of cell pan-lactylation level, up-regulation of MMP2 expression and down-regulation of E-cadherin expression, and migration and invasion ability of tumor cells is significantly enhanced. The prognosis model provided by the invention has an important clinical prediction value, inhibition of ENO1 can promote tumor progression through abnormal lactylation, and a new thought is provided for taking ENO1 as a treatment target.

The invention relates to the technical field of medical prognosis evaluation, in particular to a sex-specific advanced young non-small cell lung cancer prognosis model construction method, which collects clinical data of young metastatic non-small cell lung cancer patients, including sex, age, body mass index, D-2 polymer level, gene mutation state, metastasis site, tumor marker and hematology index. Data are divided into male and female queues, male specific prognostic factors (body mass index, gene mutation state and liver metastasis state) and female specific prognostic factors (D-2 polymer level and gene mutation state) are identified through Cox proportional risk regression analysis, and male and female column map prognostic models are respectively constructed based on the factors. All prognosis factors are converted into risk scores, the corresponding relation between the total score and the survival probability is established, finally, the model can output individualized survival probability prediction results of patients of different genders, and important reference is provided for clinical treatment and prognosis evaluation.

The application relates to the technical field of postoperative early warning, and particularly discloses a postoperative risk early warning method and system based on machine learning, which belongs to the technical field of postoperative early warning, and the method comprises the following steps: acquiring historical patient data of a plurality of patients who have undergone a to-be-early-warned operation, determining an adverse prognosis set of the to-be-early-warned operation and adverse data of each adverse prognosis in the adverse prognosis set; determining an influence factor vector of each adverse prognosis in the adverse prognosis set and an independent prognosis factor set; determining a plurality of preset non-independent factor sets of each adverse prognosis in the adverse prognosis set; determining a non-independent prognosis factor set of each adverse prognosis in the adverse prognosis set; and realizing postoperative risk early warning. The postoperative risk early warning method can comprehensively cover the postoperative risk influence dimension, accurately capture data correlation, realize intelligent transformation from data to risk prediction, provide more accurate and comprehensive postoperative risk early warning for the clinic, and improve the scientificity, accuracy and reliability of postoperative risk early warning.

The application discloses a colorectal cancer IHC staining image tumor infiltration front immune grading method, system and storage medium, and the method comprises the following steps: acquiring a colorectal cancer IHC staining image; classifying the staining image by using a trained convolutional neural network classifier to obtain nine types of tissues, namely tumor epithelium, tumor interstitium, tumor necrosis, mucus, muscle, normal gland, lymph aggregation, fat and background; merging the nine types of tissue categories into five categories to automatically determine a tumor infiltration front and a tumor center area; performing cell segmentation and counting on the above-mentioned areas to obtain CD3 and CD8 positive cell densities, and then performing immune grading; the application quantifies the CD3 and CD8 positive cell densities in the tumor infiltration front and the tumor center area by automatically determining the tumor infiltration front and the tumor center area, evaluates the CD3 and CD8 positive cell densities in the tumor infiltration front as a prognostic factor, and thus assists pathologists in diagnosing and treating colorectal cancer patients.

The application provides a gout recurrence risk assessment method and system based on a neutrophil-to-lymphocyte ratio, comprising: data acquisition, data preprocessing, and generating gout recurrence risk assessment sample data through data standardization processing; establishing a joint model of a Cox proportional hazards regression model and a competing risk model to evaluate the correlation between the neutrophil-to-lymphocyte ratio (NLR) and gout recurrence; adding NLR as a characteristic variable of a prognosis factor of gout recurrence risk to a baseline model to construct multiple gout recurrence risk candidate prediction models; screening to obtain a target gout recurrence risk assessment model; generating an evaluation result; and visual display. The method can effectively handle the problems of incomplete and skewed distribution of clinical data, improve the quality and stability of the data, improve the accuracy of evaluation and prediction by screening out an effective target gout recurrence risk assessment model, and provide reliable auxiliary decision support for clinicians.

The invention provides a gout recurrence risk assessment method and system based on the ratio of neutrophil to lymphocyte. The method comprises the steps of data collection, data preprocessing and generation of gout recurrence risk assessment sample data through data standardization processing; establishing a combined model of a Cox proportional risk regression model and a competitive risk model, and evaluating the correlation between the ratio NLR of neutrophil to lymphocyte and gout recurrence; taking the NLR as a characteristic variable of a gout recurrence risk prognosis factor, adding the NLR into the baseline model, and constructing a plurality of gout recurrence risk candidate prediction models; screening to obtain a target gout recurrence risk assessment model; generating an evaluation result; and performing visual display. According to the method, the problems of incomplete and skewed distribution of clinical data can be effectively solved, the quality and stability of the data are improved, the accuracy of evaluation and prediction is improved by screening out the effective target gout recurrence risk evaluation model, and reliable auxiliary decision support is provided for clinicians.

The invention relates to the technical field of biomedicine, and discloses application of an FASN inhibitor in preparation of a medicine for treating KAT2B low-expression renal cell carcinoma. According to the invention, development of a lipid metabolism related biomarker is realized, it is determined that the biomarker is an independent prognosis factor KAT2B in RCC, high methylation of a KAT2B promoter region causes down-regulation of expression of the biomarker, then lipid generation and development of the RCC are enhanced, a mechanism of KAT2B / HDAC5 / LSD1 / FASN axis for inhibiting the lipid generation and development of the RCC in vivo is proposed for the first time, KAT2B is acetylated at a K726 site of HDAC5, and KAT2B and LSD1 / FASN are used for inhibiting the lipid generation and development of the RCC. The FASN inhibitor can be used for inhibiting cancer cell targeting and promoting extranuclear transport of the FASN inhibitor, so that the FASN inhibitor and LSD1 cannot form a compound in a cell nucleus, the histone methylation level is increased, FASN expression is reduced, and a new thought is provided for research and development of products for inhibiting cancer cell targeting through the FASN inhibitor.

The invention relates to the technical field of medical artificial intelligence and prognosis prediction, and discloses a construction method of a prognosis risk prediction model for prostate cancer patients. According to the method, a diagnosis and treatment state transition topology is formed by creating a hierarchical knowledge network and mining diagnosis and treatment path time sequence dependence, and a whole disease course time sequence data segment of a patient is depicted according to the topology. Synchronously operating a feature selection cycle of simulated annealing optimization, and performing dynamic interactive training with a depth prediction architecture containing a multi-scale time sequence attention mechanism to realize online adaptive screening of feature subsets; and constructing a feature influence propagation graph according to a feature selection process, identifying a key feature cluster, and performing guided correction on the attention weight of the model according to the key feature cluster. And integrating the components to form a prediction model, and establishing an online self-adaptive fine tuning loop. According to the method, feature combination can be effectively optimized, the focusing capability and prediction robustness of the model for key prognosis factors are improved, and increment adjustment according to new data is supported.

The invention relates to the technical field of medical information, and discloses a non-small cell lung cancer immunotherapy prognosis risk assessment method and system and a medium, and the method comprises the steps: obtaining clinical data including clinical variables, progression-free lifetime (PFS) and total lifetime (OS); taking each clinical variable as a hierarchical index to divide patient subgroups, and screening out hierarchical indexes with significant differences; then, aiming at each subgroup divided by the screened hierarchical indexes, quantifying prognosis influence of other clinical variables on the corresponding subgroup by adopting a risk regression model; and finally, screening out determinants with prognosis values according to a quantification result, dividing the determinants into different prognosis recommendation levels, and constructing a risk assessment matrix. According to the method, through a two-stage screening strategy of firstly identifying the effective hierarchical indexes and then analyzing the prognosis factors, evaluation deviation caused by generalization of local effective factors or neglecting of global non-significant factors is avoided, so that the accuracy and clinical practicability of prognosis evaluation are remarkably improved.

This invention discloses a risk stratification assessment model, device, and construction method for RUNX1::RUNX1T1-positive children with acute myeloid leukemia. The construction method includes: acquiring sample clinical data; assessing the correlation between clinical predictors and overall survival (OS) and event-free survival (ORS); for continuous variables, determining the optimal risk cutoff value for predicting poor prognosis; converting the continuous variables into binary variables; performing univariate Cox proportional hazards regression analysis to screen for factors significantly associated with OS and ORS; and incorporating these factors into a multivariate Cox proportional hazards regression model to confirm independent prognostic factors. This invention systematically integrates the clinical variable MRD1 and the percentage of peripheral blood blasts at diagnosis as core predictors, constructing a model capable of accurately assessing ORS. + A prognostic model for pAML risk was developed and rigorously validated. This model demonstrated superior predictive performance, effectively and accurately identifying patients with a high actual risk of relapse and death from the traditionally low-risk patient population.

This invention belongs to the field of biomedical technology and discloses the application of exosomal lncOSLMT in the diagnosis and treatment of osteosarcoma lung metastases. This invention discloses the application of reagents for detecting lncOSLMT in the preparation of products for risk diagnosis or prognostic assessment of osteosarcoma lung metastases. Through research, this invention found that lncOSLMT selectively accumulates in exosomes released by osteosarcoma cells with high metastatic potential. Its elevated expression is positively correlated with the incidence of lung metastasis and serves as an independent prognostic factor for poor prognosis in osteosarcoma patients. This invention elucidates the regulatory role of lncOSLMT in the pre-metastatic microenvironment of osteosarcoma lung metastases and its effect on lung metastasis, clarifying that targeting lncOSLMT has significant clinical translational value for targeted treatment of osteosarcoma lung metastases, especially in osteosarcoma liquid biopsy and early metastasis prediction, and is of great importance in ensuring adequate clinical safety.

The invention discloses a cerebral hemorrhage clinical data multi-dimensional mining and knowledge discovery method and system, and the method comprises the steps: carrying out the standardization and integration of multi-source heterogeneous data, and constructing a unified clinical data feature set; multi-dimensional feature extraction is carried out on the clinical data feature set, time window association rule mining is carried out, and an association rule knowledge base is formed through association mode screening and evaluation; prognosis-oriented patient subgroup clustering is carried out based on the association rule knowledge base, treatment scheme feature components are extracted through treatment scheme similarity calculation, and a treatment mode map is constructed; then, an individualized factor weight threshold value is determined through factor interaction effect detection, and factor importance ranking is executed to form a prognosis key factor combination; and finally, the follow-up visit data is used for verifying and correcting the prognosis key factor combination and implementing evidence-based grade labeling, and a clinical decision support result is output, so that systematic identification and individualized evaluation of the prognosis factors are realized, and the clinical decision accuracy is improved.

The invention discloses an application of a CGREF1 inhibitor in preparation of a medicine for preventing and / or treating colorectal cancer metastasis and an application of CGREF1 in prognosis prediction of a colorectal cancer patient. It is found that inhibition of CGREF1 expression has no significant influence on proliferation of colorectal cancer cells, but migration and invasion of the colorectal cancer cells can be significantly inhibited. Besides, it is found for the first time that high expression of the CGREF1 is related to remarkable shortening of the survival time of the colorectal cancer patient, univariate analysis and multivariate Cox regression analysis both show that the expression of the CGREF1 is an independent prognosis factor for survival of the colorectal cancer patient, and the above discovery prompts that the CGREF1 is potentially applied to prognosis prediction of the colorectal cancer patient as a biomarker, and the application prospect is wide. Clinical doctors can be assisted in judging the prognosis of the patient, so that a personalized treatment scheme is formulated.

The invention provides a method of detecting or predicting a relapse of multiple sclerosis in an individual having a form of multiple sclerosis, comprising: (a) providing a blood sample of the individual; (b) testing the blood sample to determine a protein activity or protein level, wherein the protein is Factor VIII, von Willebrand Factor, or Protein C; and (c) detecting or predicting a relapse of multiple sclerosis in the individual if the protein activity or protein level is elevated compared to the protein activity or protein level in an individual not having the form of multiple sclerosis and the patient's own baseline value. The invention also provides a method of treating an individual having multiple sclerosis who is experiencing a relapse or is predicted to experience a relapse, comprising treating the individual by administering a dose of a steroid or an anticoagulant compound effective to reduce symptoms of multiple sclerosis.

The invention discloses application of ALYREF in treatment and prognosis prediction of multiple myeloma, and belongs to the field of biomedicine. According to the application disclosed by the invention, high expression of the ALYREF in myeloid tissue of a patient with multiple myeloma is found, and differential expression analysis and survival analysis of the ALYREF find that the ALYREF is an independent prognosis factor of the patient with multiple myeloma, and high expression of the ALYREF is related to poor prognosis of multiple myeloma. Then starting from a multiple myeloma cell strain, through targeted down-regulation of expression of ALYREF, it is found that down-regulation of the expression level of ALYREF can inhibit the multiplication capacity of multiple myeloma cells and promote death of tumor cells, and finally multiple myeloma is improved or treated. Therefore, the invention provides effective treatment and prognosis evaluation molecules for multiple myeloma.

The invention discloses a children pneumonia prognosis condition prediction system and method based on a BP neural network, and relates to the technical field of data processing, and the system comprises a multi-source data fusion module which is used for receiving electronic medical records, laboratory data, imaging features and environment features, and building a multi-source fusion data set; the self-adaptive feature selection module is used for extracting key prognosis factors based on hybrid feature screening; the construction optimization module is used for training a prognostic risk scoring model by using the key prognostic factors and optimizing the prognostic risk scoring model through a gradient direction correction strategy; and the prediction module is used for performing child risk scoring and prognosis management. The technical problems that the prior art depends on a simple model and single data, illness state changes cannot be dynamically tracked, and the accuracy and timeliness of prognosis evaluation are poor are solved, and the technical effects that personalized prognosis management is carried out through the BP neural network optimized through multi-source data fusion, and the accuracy and timeliness of prognosis risk evaluation are improved are achieved.

The application relates to the technical field of medical artificial intelligence and prognosis prediction, and discloses a construction method of a prostate cancer patient prognosis risk prediction model. The method forms a diagnosis and treatment state transition topology by creating a hierarchical knowledge network and mining diagnosis and treatment path time sequence dependence, and accordingly depicts a patient whole-course time sequence data segment. A simulated annealing optimization feature selection cycle is synchronously run, and a deep prediction architecture containing a multi-scale time sequence attention mechanism is dynamically interactively trained, so that online adaptive screening of a feature subset is realized; a feature influence propagation graph is constructed according to the feature selection process, key feature clusters are identified, and guided correction of attention weights of the model is carried out accordingly. The above components are integrated to form a prediction model, and an online adaptive fine-tuning loop is established. The method can effectively optimize feature combination, improve the focusing ability and prediction robustness of the model on key prognosis factors, and support incremental adjustment according to new data.

The invention relates to the technical field of biological medicine, in particular to application of Hsacirc0002688 as a prognostic marker and a treatment target of triple negative breast cancer. The triple negative breast cancer patients are equally divided into a high expression group and a low expression group according to the expression level of the Hsacirc0002688, and single-factor and multi-factor Cox regression analysis further confirms that the Hsacirc0002688 is an independent prognostic factor influencing the total lifetime and disease-free lifetime of the triple negative breast cancer patients, so that the Hsacirc0002688 can be used as a prognostic marker of the triple negative breast cancer. The migration and invasion of triple negative breast cancer cells are obviously inhibited by silencing or inhibiting the expression of Hsacirc0002688; the overexpression of the Hsacirc0002688 can improve the migration and invasion of the triple negative breast cancer cells, so that the Hsacirc0002688 can be used as a treatment target of the triple negative breast cancer.

The application relates to an oral squamous cell carcinoma auxiliary chemical immunotherapy response method and device, which comprises the following steps: acquiring image data and baseline clinical factors of a target user, the image data comprising metabolic parameters and image features of a tumor region; performing clinical parameter standardization processing on the metabolic parameters to obtain a standardized metabolic parameter set related to treatment response; performing stability screening on the image features by using an intragroup correlation coefficient to obtain key features related to treatment response; performing regression analysis on the baseline clinical factors to identify independent prognostic factors related to treatment response in the baseline clinical factors; and performing feature fusion on the standardized metabolic parameter set, the key features and the independent prognostic factors to obtain a response rate and a prognosis prediction result of the target user. The method can more accurately and comprehensively predict the response rate and the prognosis of the target user to the auxiliary chemical immunotherapy.

This invention relates to the field of rehabilitation data management technology, specifically to a method, device, and medium for predicting postoperative rehabilitation stages for osteoarthritis. The method includes: acquiring multi-dimensional prognostic factors corresponding to the patient; outputting a personalized postoperative rehabilitation stage for the patient through a pre-trained machine learning model; periodically updating physical function parameters; updating psychosocial parameters; and updating the personalized postoperative rehabilitation stage based on the updated parameters of the multi-dimensional prognostic factors. By integrating multi-dimensional prognostic factors such as basic physical characteristics, intraoperative parameters, functional status, and psychosocial factors, this invention overcomes the limitations of traditional template-based rehabilitation stages, generating a rehabilitation stage prediction tailored to each patient's individual circumstances, thus achieving personalized assessment of the patient's rehabilitation stage.

The invention discloses a hepatocellular carcinoma prognosis evaluation marker TKFC and application thereof, and belongs to the technical field of prognosis markers. By comparing tumor tissues of patients with different prognosis, the invention discovers that the HCC subtype with relatively strong invasiveness shows a systematic state imbalance, that is, the oxidative metabolism function represented by mitochondrial oxidative phosphorylation and tricarboxylic acid cycle is weakened, and the intracellular proliferation procedure related to chromatin remodeling and cell cycle progress is abnormally active. The state of low oxidative metabolism-high proliferation output may form the metabolism and epigenetic basis of malignant HCC. The low expression of the TKFC is obviously related to high-risk clinical pathological characteristics such as later tumor staging and higher occurrence rate of vascular invasion, and is an independent poor prognosis factor influencing the total lifetime of a patient, which indicates that the TKFC can be used as a prognosis biomarker of HCC.

The invention provides an application of PLAC8 as a detection target in preparation of a tumor diagnosis product, and relates to the technical field of biomedicine, the technical key points are as follows: the application of the PLAC8 as the detection target in preparation of the tumor diagnosis product and a tumor prognosis condition inspection product integrates three gallbladder cancer clinical queues, and has the advantages of high accuracy, high sensitivity and high reliability. By combining a cross-species model and technologies of multiple immunofluorescence, single cell RNA sequencing and the like, the PLAC8 is found to be a cross-species conservative M2 macrophage gene with significant difference. PLAC8 is abnormally enriched in gallbladder cancer tissues, and normal gallbladder epithelium is almost not expressed and can be stably detected through tumor tissues and peripheral blood. Expression of the gene is limited to a macrophage cluster, the gene is in positive correlation with M2 markers CD206 and CD163, and M2 polarization is promoted by regulating cholesterol metabolism. Clinical verifications show that increase of PLAC8 + CD163 + macrophages in tumors is an independent prognosis factor, high expression is closely related to short lifetime of patients and increase of chemotherapy non-response rate, and PLAC8 can be used as a potential biomarker for evaluating drug resistance of gallbladder cancer chemotherapy and tumor progression.

The invention relates to the technical field of rehabilitation data management, in particular to a postoperative rehabilitation stage estimation method and device for osteoarthritis and a medium, and relates to the field of rehabilitation data management.The method comprises the steps that a multi-dimensional prognosis factor corresponding to a patient is obtained; outputting a personalized postoperative rehabilitation stage corresponding to the patient through a pre-trained machine learning model; the body function parameters are updated regularly; updating the psychological and social parameters; the personalized postoperative rehabilitation stage is updated through the updating parameters of the multi-dimensional prognosis factors. By integrating basic body characteristics, intraoperative parameters, functional states, psychological society and other multi-dimensional prognosis factors, the limitation of a traditional templated rehabilitation stage is broken through, rehabilitation stage estimation fitting personal actual conditions can be generated for each patient, and personalized evaluation of the rehabilitation stage of the patient is achieved.

The application discloses non-coding RNA tRF-18-EY0VWUD2 as a biomarker for predicting breast cancer CDK4 / 6 inhibitor drug resistance. The application firstly finds that tRNA-derived small RNA, i.e., tRF-18-EY0VWUD2, can be used as a biomarker for predicting breast cancer CDK4 / 6 inhibitor drug resistance. Research results show that tRF-18-EY0VWUD2 is significantly highly expressed in CDK4 / 6 inhibitor drug resistance cell lines and patient serum, the PFS of patients with high expression of tRF-18-EY0VWUD2 is significantly poorer than that of patients with low expression of tRF-18-EY0VWUD2, and tRF-18-EY0VWUD2 is an independent prognostic factor for the curative effect of CDK4 / 6 inhibitor treated patients.

The application discloses a kind of risk stratification model construction method and system for EGFR mutant non-small cell lung cancer meningeal metastasis patient, belong to tumor prognosis evaluation technical field.The multicenter retrospective clinical database is first constructed and data preprocessing is completed, then the predictor is screened by LASSO Cox regression, and the five core prognostic factors of ECOG PS score, brain metastasis, MRI meningeal enhancement, CSFC result and intracranial pressure are determined by multi-factor Cox proportional risk regression and linear predictor is constructed, then it is converted into integer type weighted risk score, finally the best cutoff value is determined based on survival function, and the patient is divided into low-risk group and high-risk group.The application can clearly distinguish the patient group with significant difference in prognosis, the required indicators are all clinical routine items, the operation is simple, the accessibility is high, can accurately guide individualized treatment decision and clinical trial stratification, and has important clinical application value.

The invention belongs to the technical field of bioinformatics, and discloses a method and device for predicting osteosarcoma survival prognosis and electronic equipment, which are used for predicting osteosarcoma survival prognosis by incorporating more cases and detailed clinical baseline data and combining clinical prognosis factors such as primary focus part factor values, tumor staging factor values, tumor diameter factor values and serum alkaline phosphatase factor values. And one or more of the screened super enhancer driving genes LACTB, CEP55, SRSF3, TCF7L2 and FOXP1 with better prediction efficiency are used as gene combinations to jointly construct a prognosis prediction model for survival risk assessment, so that prognosis prediction of a patient corresponding to a to-be-detected sample is realized, the prediction model is more reliable in efficiency, and the prediction efficiency is higher. The prediction accuracy of osteosarcoma survival prognosis can be obviously improved.

The invention discloses a prognosis model for predicting the curative effect of treating a patient with non-resectable hepatocellular carcinoma by combining transcatheter arterial chemoembolism with truditinib and revatinib. The research finds that four indexes of AFP, ALP, DBIL and NLR are all independent prognosis factors of a uHCC patient which is treated by a TACE (triplex angiotensin converting enzyme) combined Czodimab and lenvatinib triplex therapy. By constructing the AADN scoring model, the prognosis of the patient can be comprehensively evaluated. The AADN score has the advantages that a baseline index before treatment is easy to obtain; the treatment response is directly related; the score is dynamic and can be adjusted according to index changes in the treatment process. According to the AADN scoring model, blood indexes (AFP, ALP, DBIL and NLR) with low cost and high accessibility are adopted, the prognosis of the uHCC patient can be accurately layered, and a practical tool is provided for formulating an individualized scheme for treatment by combining TACE with the Czodimab and the lenvatinib.