9 results about "Recurrence prediction" patented technology

This invention relates to an intelligent decision support system for rectal cancer prognosis integrating digital pathological images, belonging to the field of medical image processing technology. The system includes a data acquisition module for collecting multiple sets of sample data; a feature selection module for identifying multiple key medical imaging features of rectal cancer based on the multiple sets of sample data; a model building module for acquiring a pre-trained deep learning model and constructing a rectal cancer prognosis prediction model based on transfer learning, the pre-trained deep learning model, the multiple sets of sample data, and the multiple key medical imaging features of rectal cancer; an image acquisition module for acquiring pre- and post-operative medical images of the rectal cancer patient to be evaluated; and a recurrence prediction module for predicting the recurrence probability of the rectal cancer patient to be evaluated based on the pre- and post-operative medical images of the patient using the rectal cancer prognosis prediction model. This system has the advantage of improving the accuracy of non-invasive prediction of postoperative recurrence of rectal cancer.

The application provides a laryngeal squamous cell carcinoma prognosis gene methylation marker and application thereof, and relates to the technical field of clinical medicine. The methylation marker is CORO1C and MAPK11, and a recurrence risk prediction model is constructed based on the two sites. The prediction model is independent of factors such as stage, age and differentiation of patients, proving the independence and universality of the prediction model. Through the RRBS technology, the methylation changes of the genes can be comprehensively analyzed at high resolution, and the prognosis value of the markers in laryngeal squamous cell carcinoma is verified. Compared with traditional single clinical pathological indicators, the methylation marker based on the molecular level provided by the application has high accuracy and sensitivity, and can provide more accurate basis for the recurrence prediction of laryngeal squamous cell carcinoma.

The application relates to an ablation evaluation method, an ablation evaluation system and a storage medium. The method comprises the following steps: determining a lesion area from a preoperative image of a target object, and determining an ablation area from a postoperative ablation image of the target object; performing difference analysis according to the lesion area and the ablation area to determine an ablation rate; the ablation rate represents the ablation degree of the lesion; inputting related information of the lesion corresponding to the lesion area and the ablation rate into a preset recurrence prediction model to perform evaluation, and obtaining an ablation evaluation result; the ablation evaluation result comprises recurrence information and / or supplementary ablation reference information determined based on the recurrence information. In one aspect, the ablation effect is evaluated based on the lesion area and the ablation area obtained immediately after the operation, and supplementary ablation is performed in time, so that the ablation effect is improved, and the number of ablation times is reduced. In another aspect, the related information of the lesion and the ablation rate are evaluated and analyzed based on a recurrence prediction model, so that more accurate evaluation results can be obtained, and the accuracy of recurrence prediction is improved.

The application discloses a lung adenocarcinoma recurrence prediction method and system based on multi-omics data analysis, relates to the technical field of precision medicine, and comprises the following steps: collecting folate metabolism group data, a transcription group expression spectrum and methylation level data of a target gene promoter region of tumor tissue of a lung adenocarcinoma patient; constructing a metabolism-epigenetic correlation topology graph based on a spatial adjacent relationship, screening a significant causal relationship between methylation variation and metabolism fluctuation through causal analysis, and generating a methylation metabolism significant causal edge set; fusing simulation metabolism channel characteristics and the methylation metabolism significant causal edge set, constructing a lung adenocarcinoma recurrence risk prediction model, triggering a high-risk early warning signal through time sequence analysis of risk factor fluctuation; through the construction of the metabolism-epigenetic correlation topology graph and the screening of the significant causal edge set, the causal correlation modeling of multi-omics data in the spatial dimension is realized, the robustness of the recurrence prediction model is improved, the prediction performance is synergistically enhanced, and the accuracy of lung adenocarcinoma recurrence prediction is significantly improved.

The application provides a non-muscular invasive bladder cancer postoperative recurrence prediction method and system, the method comprising: obtaining preoperative enhanced CT images of a target object, urine SIM2 gene methylation detection results and clinical pathological characteristics; pre-processing the preoperative enhanced CT images and delineating a tumor region of interest, and extracting imageomics features and deep learning features to construct a preliminary multi-modal feature set; performing feature screening and dimension reduction processing on the imageomics features, and fusing the processed imageomics features with the preliminary multi-modal feature set to obtain a multi-modal feature set; inputting the multi-modal feature set into a pre-constructed prediction model to output a non-muscular invasive bladder cancer postoperative recurrence risk probability and risk level of the target object, wherein a first layer of the prediction model is constructed by using a plurality of heterogeneous base learners, and a second layer is constructed by using a meta learner. The application can realize fine stratified management of non-muscular invasive bladder cancer patients.

This invention provides a method and system for predicting the recurrence risk of nasopharyngeal carcinoma (NPC) using multimodal data, belonging to the field of medical data processing technology. The method includes acquiring NPC data of different modalities, including whole-section pathological images, magnetic resonance imaging (MRI) images, and clinical test data; extracting features from the whole-section pathological images, MRI images, and clinical test data respectively; cascading the extracted features according to channels to achieve feature fusion; mapping the fused feature vectors to obtain NPC recurrence prediction probability data for different timeframes. This forms a unified decision-making path, providing data support for NPC recurrence prediction and facilitating the planning of subsequent treatment strategies.

The application provides a multi-omics joint detection system for prostate cancer recurrence risk assessment, the application synchronously acquires genomic, transcriptomic, epiproteomic and metabolomic data of different regions of a tumor through spatially resolved in situ capture technology, and integrates multi-dimensional information such as circulating tumor DNA epigenetic memory, urological microbiome-host interaction, single-cell clone evolution and tumor microenvironment three-dimensional topology. The system uses a dynamic Bayesian fusion engine to perform probabilistic risk calculation, combines digital twin technology to simulate treatment response, and realizes model adaptive updating through longitudinal follow-up data. The output result has high interpretability and can directly show key driving factors and their clinical interventional properties. The system breaks through the limitations of traditional static and single-omics models, significantly improves the prediction accuracy of recurrence, especially in low-risk populations, and provides intelligent support for individualized auxiliary treatment decisions.

The application discloses a liver cancer prognosis evaluation model generation method and related devices, and relates to the technical field of data processing. The method obtains medical multi-modal data of a liver cancer patient, the medical multi-modal data including macroscopic magnetic resonance imaging data and microscopic pathological whole slice image data; based on a graph neural network, the medical multi-modal data is converted into multi-scale graph structure data and is subjected to cross-scale topological alignment through a GroMoVe optimal transport algorithm to obtain topological fusion features; the topological fusion features are input into a causal intervention module constructed based on a structural causal model, a counterfactual generation is performed to eliminate confounding factors, and causal invariant features are extracted; a continuous time evolution component is trained based on the causal invariant features, the continuous time evolution component represents time dynamic changes of a tumor recurrence risk based on a neural ordinary differential equation, and a liver cancer prognosis evaluation model is obtained. The application improves liver cancer recurrence prediction accuracy, enhances model generalization robustness and interpretability.

The present invention relates to an OLFML2B biomarker for treatment of hepatocellular carcinoma and prediction of recurrence, and use thereof. Specifically, OLFML2B exhibits a high AUC in hepatocellular carcinoma diagnostic performance and is more useful for predicting recurrence, and has been identified to have consistent performance in various public data such as MDACC and GSE series. In Cox regression analysis, OLFML2B has been identified to be a significant factor in survival and recurrence, and has shown a more distinct increase in expression in malignant hepatocytes, PVTT, and recurrent tumor tissue than in normal tissue or primary tumor. In addition, it has been identified that the invasiveness and proliferation of hepatocellular carcinoma cell lines are significantly reduced when OLFML2B is inhibited. Therefore, the present invention can ultimately promote an improvement in survival rate and treatment outcomes of hepatocellular carcinoma patients.