130 results about "Radiomics" 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 belongs to the technical field of medical image processing, and particularly relates to an esophageal cancer treatment effect and survival combined prediction method and system based on multiple modalities, and the method comprises the steps: obtaining a preoperative CT image and Hamp of an esophageal squamous cell carcinoma patient; e, the dyed digital pathological image, transcriptome data and clinical diagnosis and treatment information are preprocessed and subjected to feature extraction, and radiomics embedding representation, pathomics embedding representation and pipeline branch embedding representation are obtained respectively; the radiomics embedded representation, the pathomics embedded representation and the pipeline branch embedded representation are aligned and input into a multi-modal fusion module based on a multi-head self-attention mechanism for feature fusion, and fusion feature representation is generated; and based on the fusion feature representation, synchronously outputting a curative effect prediction result and a survival prediction result by using a multi-task output module, and evaluating model prediction performance by using a curative effect evaluation index and a survival analysis index.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention discloses a method for constructing a machine learning model based on cerebellar subregion multi-modal radiomics. According to the method, [18F] FDG PET metabolic features and 3DT1 W MRI structural features of a cerebellar subregion are extracted, a random forest classification model is constructed after feature selection, and high-precision identification of the Alzheimer's disease (AD) and a cognitive normal (CN) is realized in combination with SHAP analysis. According to the method, the multi-modal radiomics characteristics of the cerebellar subregion are integrated for the first time, the accuracy and interpretability of AD early diagnosis are improved, and a non-invasive and efficient diagnosis tool is provided for clinic.

The invention discloses a Crohn disease focus automatic segmentation and activity evaluation system based on deep learning, which belongs to the field of medical artificial intelligence and comprises a data preprocessing unit, a focus automatic segmentation unit, a radiomics feature extraction unit, a feature screening and dimension reduction unit and an activity classification unit. According to the method, an nnU-Net deep learning segmentation model is combined with image omics feature extraction, multi-stage feature screening and machine learning classification technologies, so that full-process automation from CTE image preprocessing, focus automatic segmentation, feature extraction and screening to activity classification is realized. The system can efficiently and accurately segment the focus of Crohn's disease, automatically assesses the disease activity based on the screened key radiomics characteristics, significantly improves the consistency, objectivity and efficiency of diagnosis, and is suitable for clinical auxiliary diagnosis and scientific research analysis.

A method for determining a recurrence of a disease in a patient includes generating a medical image of an organ of the patient and then extracting an invasive edge around an area of interest in the medical image. A plurality of radiomics features is obtained from the invasive edge and the recurrence of the disease is determined based on the plurality of radiomics features.

A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.

The invention relates to the technical field of medical data analysis, in particular to a urinary system tumor big data analysis system which comprises a target kernel generation module, a kernel matrix calculation module, a parameter optimization module and a risk division module. According to the method, a target kernel matrix reflecting clinical prognosis differences is constructed and serves as an optimization reference, a radiomics and genomics feature kernel matrix is generated through hardware acceleration parallel computing, kernel function width parameters are dynamically iteratively updated based on an alignment degree numerical value so as to ensure that multi-modal feature distribution is highly matched with a prognosis label, and the accuracy of the multi-modal feature distribution is improved. A multi-dimensional feature space containing rich pathological information is constructed by combining a weighted fusion mechanism after centralization processing, so that a support vector machine is trained to determine a high-robustness decision boundary, and precise division of tumor risk levels is realized while high-dimensional data calculation delay is greatly reduced; and the reliability and timeliness of auxiliary diagnosis and treatment results in a complex pathological environment are effectively improved.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention discloses a rectal cancer postoperative recurrence risk prediction system and method based on multi-modal time sequence data, and relates to the technical field of medical artificial intelligence. The system comprises a data acquisition and preprocessing module, a feature extraction module and a multi-modal feature fusion and modeling module. The method comprises the following steps: constructing a cross-modal data set containing time sequence clinical data, a time sequence MR image and a biopsy digital pathological image; respectively extracting clinical features, radiomics and deep learning features of the MR image, and nucleus morphology and spatial distribution features of the pathological image; and fusing all the features by using a Transform network, and constructing a prediction model. According to the method, macroscopic images, micropathology and dynamic time sequence information are integrated, tumor heterogeneity is comprehensively quantified, the problem that prediction of a single-mode static model is not accurate is solved, the postoperative recurrence risk of the stage III rectal cancer patient can be evaluated more accurately, and clinical treatment decision making is assisted.

The invention relates to a kidney lump benign and malignant analysis method and device based on a laparoscopic ultrasound image, and belongs to the technical field of medical image processing.The method comprises the steps that radiomics characteristics of the laparoscopic ultrasound image are obtained, and radiomics scores of lump malignant risks are calculated according to the radiomics characteristics; determining an independent risk factor corresponding to the patient clinical variable, and constructing a clinical prediction model according to a mapping relationship between the patient clinical variable and the independent risk factor; and according to the radiomics score and the clinical prediction model, constructing a kidney lump benign and malignant analysis model. The technical problem that in the prior art, massive quantitative image features cannot be efficiently and accurately mined from medical images, and the most valuable iconography features cannot be screened out for analyzing clinical information is solved.

The invention relates to the technical field of federated learning, and discloses an image omics-oriented cross-domain federated machine learning training method, which comprises the following steps that: a server distributes a global orthogonal semantic base to establish a shared gradient space recessive semantic coordinate system, and when the stability of a local training process meets a preset condition, a client sends the global orthogonal semantic base to the client; a local gradient is projected to a coordinate system and uploaded together with a digital fingerprint representing the physical characteristics of imaging equipment, and a server groups clients according to the digital fingerprint and performs weighted aggregation based on the information concentration degree on received projection vectors in each group so as to reconstruct a global gradient to update a model. According to the method, through an aggregation mode of firstly performing grouping according to physical causes and then performing weighting in groups according to information values, systematic deviation from equipment differences and real pathological semantics from data contents can be effectively stripped, and sensitivity to key clinical features is kept.

The invention provides a breast cancer adjuvant therapy pathology complete remission prediction method and electronic device.The breast cancer adjuvant therapy pathology complete remission prediction method comprises the steps that at the first stage, based on multi-time-point and multi-parameter MRI images of a patient before and after adjuvant therapy, the breast cancer adjuvant therapy pathology complete remission prediction result is obtained; training an image feature encoder with a tumor dynamic change identification capability through a dynamic supervision pre-training mode; and a second stage of constructing a multi-modal deep fusion network based on the image feature encoder to integrate the image features, radiomics features and clinical pathological features of the patient, and outputting a prediction result of complete pathology remission, the radiomics features being extracted from the MRI image. And the purposes of high precision and strong generalization capability are achieved.

The invention provides computer equipment for executing a sub-solid pulmonary nodule growth prediction method based on CT imageomics, and the computer equipment comprises a memory, a processor and a computer program, and when the processor executes the program, density value extraction is carried out on a sub-solid pulmonary nodule region through multi-stage CT images, the internal structure of a nodule is segmented by adopting a convolutional neural network, and the internal structure of the nodule is obtained; obtaining nodule internal density distribution data from the segmentation result; calculating a cavity expansion rate and an edge density increase rate in unit time by combining the density amplitude data according to a focus characteristic influence degree evaluation result; according to the lesion cavity expansion rate and the edge density growth rate, the malignant progress risk level is judged, and malignant progress time window estimation is obtained; according to the malignant progress time window estimation, the matching degree between the density distribution data and the development speed index is calibrated through clinical feedback data, and sub-solid pulmonary nodule growth prediction output is obtained.

The invention discloses a lung disease diagnosis and grading method based on quantitative CT image omics and deep learning. According to the method, through multi-dimensional information integration and model optimization, two core tasks of disease judgment and disease condition grading can be completed at the same time, and comprehensive support is provided for clinical diagnosis and treatment. The model sets feature priorities by referring to clinical diagnosis logic in the training process, the judgment result and the grading standard are completely matched with clinical general specifications, and the model can be directly applied to diagnosis and treatment decision-making without secondary conversion of doctors. After multi-center clinical verification and iterative optimization, the stability and the accuracy of the model are fully guaranteed, subjective errors caused by manual film reading can be effectively reduced, the diagnosis efficiency can be improved, the chronic obstructive pulmonary disease screening capability of primary medical institutions can be remarkably improved, early diagnosis and early treatment of more patients can be helped, and the clinical application prospect is wide. Therefore, the morbidity and disability rate of diseases are reduced, a clinical management path is optimized, and the overall disease burden is relieved.

This invention provides a method, device, and equipment for preoperative risk stratification assessment of endometrial cancer. It involves segmenting and annotating preoperative ultrasound images to extract radiomics feature vectors, and simultaneously performing structured encoding and mapping of pathological biopsy data to generate pathological feature vectors. These two feature vectors are then input into a fusion network containing a cross-modal attention gating module. This gating module automatically generates dynamic weight vectors based on the consistency between the image and pathological features. The two feature vectors are then weighted and modulated separately before being concatenated to obtain a joint feature representation. This automatically reduces the weight of the lower-confidence modality and amplifies the weight of the higher-confidence modality when their assessment conclusions are inconsistent. Finally, the joint feature representation is input into a multi-task prediction network to output the probability of myometrial invasion depth and the probability of lymph node metastasis risk, thereby generating preoperative risk stratification results and surgical plan recommendations.

The invention relates to a radiotherapy oral mucosa reaction early warning and nursing system and a use method thereof, and belongs to the technical field of oral nursing. The system comprises a data acquisition module, a risk assessment module, an early warning prompt module and a nursing scheme generation module; the data acquisition module acquires electronic medical record data, radiotherapy plan data and radiomics characteristic data of a patient; the risk assessment module is based on data acquired by the data acquisition module. In the invention, by integrating electronic medical records, radiotherapy plan parameters and dose-specific radiomics characteristics, a multi-source data fusion prediction model is established, so that the limitation that a traditional method only depends on doctor experience and simple dosimetry parameters is overcome, and particularly, dose-specific mucous membrane subregions are generated through spatial Boolean operation; radiomics features capable of reflecting tissue heterogeneity of different dose regions are extracted, the accuracy and biological significance of a prediction model are remarkably improved, and early recognition of high-risk patients is achieved.

The application discloses a classification method and device for pancreatic neuroendocrine tumors. The classification method comprises the following steps: segmenting tumor masks from enhanced CT images; for each tumor mask, generating a first peritumoral region and a second peritumoral region; dividing the tumor into multiple habitats for each tumor mask; extracting 3D radiomics features, multiple peritumoral microenvironment features, multiple tumor habitat features, multiple local pathological features and multiple global context features; selecting multiple target features for each enhanced CT image and calculating a radiomics score; inputting the radiomics score and clinical variables into a logistic regression model for training to obtain a trained logistic regression model; and using the trained logistic regression model to classify the enhanced CT images to be classified. The application can quickly and accurately realize G-grade classification of pancreatic neuroendocrine tumors, and provides a reliable non-invasive evaluation tool for clinical decision-making.

The invention belongs to the technical field of tumor prognosis prediction, and relates to a colorectal cancer allotropic liver metastasis prediction method and system.The method comprises the steps that colorectal adenocarcinoma cases are collected, the volume of an intra-tumor region-of-interest is delineated and automatically expanded to generate the volume of a peritumor region-of-interest, image omics characteristics are extracted through a pyradiomics packet, core omics characteristics are screened out, and the colorectal cancer allotropic liver metastasis prediction result is obtained. The method comprises the following steps: respectively constructing an intratumoral model and a peritumoral model, analyzing and integrating intratumoral and peritumoral core omics characteristics through logistic regression to form a combined radiomics model, integrating the combined radiomics model and clinical risk factors, establishing a column diagram for predicting the non-hepatic metastasis lifetime, and dividing patients into a low-risk group and a high-risk group according to a column diagram score threshold value; according to the method, LMFS prediction results of 1-5 years can be quickly output, and 0.6911 is set as a standardized risk stratification cut-off value so as to support risk stratification and personalized treatment decision of a patient.

The invention provides an image-clinical characterization-combined multi-endpoint prognosis evaluation method for transjugular vein intrahepatic portal vein shunt, which comprises the following steps of: constructing a few-label portal vein segmentation module to obtain a preoperative CT portal vein label of a full-dose patient, and extracting deep learning features and radiomics features of the region; establishing a multi-modal interactive representation learning module for implementation, and performing cross-modal fusion with clinical features to form unified representation; and designing a multi-endpoint prognosis prediction module, inputting the data to a plurality of prognosis task decoders for postoperative survival, portal vein pressure gradient, hepatic encephalopathy prediction and the like, and adopting a multi-task learning optimization model to obtain a postoperative multi-endpoint prognosis evaluation result. According to the method, efficient fusion and multi-endpoint prognosis prediction of images and clinical information can be realized under limited labeling, clinical doctors can be assisted in preoperative patient screening and treatment scheme making, and the method has good clinical application value.

This invention relates to an MRI-based method for classifying benign and malignant intraductal lesions (IDLs) of the breast, comprising the following steps: Step 1) Extracting a set of clinical features from MRI images: age, ADC value, BI-RADS category, lesion shape, margin clarity, and TIC curve type; Step 2) Extracting radiomics features from the 3D lesion segments of the T1 DCE-MRI sequence; Step 3) Reducing the dimensionality of the radiomics features using the Lasso algorithm; Step 4) Fusing the dimensionality-reduced radiomics features with the clinical features into an input vector; Step 5) Outputting benign / malignant probability values ​​through a logistic regression model; Step 6) Applying stratification rules to BI-RADS 4A lesions: older patients or those with low ADC values ​​are marked as high-risk and biopsy is recommended; younger patients with high ADC values ​​are marked as low-risk and follow-up is recommended. This method solves the problem of current dynamic contrast-enhanced MRI's difficulty in accurately identifying BI-RADS categories, especially the BI-RADS 4A high-risk subgroup, thereby reducing missed diagnoses and over-biopsy. It is of urgent significance for overcoming the bottleneck of preoperative risk stratification in IPLs and guiding individualized clinical decision-making.

This invention provides a method for analyzing the radiomics characteristics of medullary thyroid carcinoma based on ultrasound, relating to the field of image or video recognition or understanding technology. The method includes: obtaining data from thyroid surgeries at various target hospitals to form a backup dataset; filtering the backup dataset to obtain a target dataset; labeling the data in the target dataset with Regions of Interest (ROIs); performing feature extraction and feature filtering on the data in the target dataset to obtain training and validation sets; constructing a target radiomics feature model; calculating radiomics feature scores based on the target radiomics feature model; obtaining a clinical feature model, an ultrasound feature model, a comprehensive model, and a scoring model based on logistic regression analysis; and performing radiomics feature analysis based on the clinical feature model, ultrasound feature model, comprehensive model, and scoring model. This invention solves the problems of low diagnostic accuracy and high requirements for experience of image interpreters in existing technologies for thyroid nodules.

The application discloses a 7T magnetic resonance head and neck tumor radiotherapy response evaluation system, belongs to the field of medical imaging and tumor radiotherapy, and comprises an ultrahigh-field multi-modal image acquisition module, a multi-phase accurate registration and radiomics feature extraction module, an early response biomarker identification module and an adaptive treatment decision support module. The high resolution, high signal-to-noise ratio and multi-nuclide imaging capability of the 7T ultrahigh-field magnetic resonance are utilized to acquire multi-modal images before and during treatment, more than 200 features are extracted through accurate registration and radiomics analysis, the treatment response is predicted based on the Delta feature and the deep learning model 2 to 3 weeks after the start of radiotherapy, the prediction accuracy reaches 87.5%, the prediction is 3 to 4 weeks earlier than the conventional evaluation, decision support is provided for timely adjustment of the treatment strategy, a closed-loop feedback mechanism is established, the clinical verification results are used to optimize the parameters of each module reversely, and continuous improvement is realized.

The present disclosure relates to representation learning for treatment response prediction of risk organs and total tumor volume. For predicting the response of radiation therapy, radiomics is used for unsupervised machine training of an encoder-decoder network to predict based on input of image data, such as computed tomography image data, and from segmentation. Then, the trained encoder is used to generate latent representations to be used as input to different classifiers or regressors for predicting the treatment response, such as one classifier for predicting the response for a risk organ and another classifier for predicting another type of response for a risk organ or predicting the response for a tumor.

The invention discloses a deep learning-based lung cancer adjuvant chemotherapy benefited patient identification method and system, and relates to the technical field of lung cancer precise treatment and artificial intelligence crossing, and the method comprises the steps: analyzing and preprocessing preoperative and postoperative registration logs of a patient to obtain basic information of the patient, a pathological image of the patient and a treatment strategy of the patient; the method comprises the following steps: performing segmentation processing on a patient pathological image by adopting nnformer to obtain a focus region segmented image, performing feature extraction and enhancement on the focus region segmented image to obtain image omics features, and constructing an improved 3D survival analysis model; and analyzing the basic information of the patient, the radiomics characteristics and the treatment strategy of the patient by adopting an improved 3D survival analysis model to obtain an identification result of the lung cancer adjuvant chemotherapy benefited patient. According to the method, the quantitative survival risk score and the adjuvant chemotherapy sensitivity conclusion are output, and an interpretable reference basis is provided for clinical decision making.

The invention discloses a cerebral apoplexy prognosis early warning system based on CTP image omics and machine learning, and the system comprises a data collection module which is used for obtaining and standardizing the CTP image data and clinical data of a patient; the radiomics feature extraction module is used for extracting a high-dimensional feature set containing morphological features, textural features and hemodynamic functional features; the machine learning prognosis model module is used for fusing the high-dimensional feature set and the clinical data into a training feature matrix; and a prognosis early warning output module. According to the method, a multi-dimensional and high-dimensional feature set including form, texture and functional parameters is automatically extracted from a CTP image through a radiomics technology, focus microcosmic heterogeneity and hemodynamic information which cannot be recognized by human eyes are deeply excavated, deep features and clinical data are fused, learning is performed by using a machine learning model, and the accuracy and accuracy of the fusion of the deep features and the clinical data are improved. Therefore, the dependence of doctors on personal experience is effectively reduced, and the prognosis early warning result is more objective.

The invention discloses a method for classifying hysteromyoma and hysterosarcoma by using MRI (Magnetic Resonance Imaging) imageomics, which comprises the following steps of: acquiring MRI original image data of two types of patients with lesions through a three-dimensional image processing workstation, and extracting feature data; inputting the image data into an MRI image perception graph convolutional network, dynamically adjusting and extracting features through a multi-scale convolution kernel, and generating an initial image feature graph; inputting the initial feature map into a generative adversarial feature unwrapping network, and separating calibration features and interference features through adversarial training of a generator and a discriminator; the calibration features are input into a multi-instance learning attention model, lesion area feature representation is enhanced through a weight distribution mechanism, and weighted features are obtained; and constructing a lesion classification feature set in combination with MRI image omics features, and outputting a classification result by a classifier according to the optimized feature set. According to the method, the feature quality is cooperatively improved through a multi-link technology, the classification accuracy and objectivity are guaranteed, subjective interference is reduced, different MRI image characteristics are adapted, and clinical precise classification requirements can be met.

A system, computer readable medium, apparatus and / or method for non-invasive, non-surgical, digital biopsy. The system, computer readable medium, apparatus and / or method accurately predicts benign kidney lesions from cancers in a patient. A processor may receive patient clinical factors, texture analysis of computer-tomographic imaging, and an artificial intelligence learning model. By implementing artificial intelligence, the processor may then predict or determine a probability of kidney cancer in the patient using the patient clinical factors, the texture analysis of computer-tomographic imaging, and the artificial intelligence learning model. Notably, the prediction is performed without needing invasive biopsy surgery and subsequent pathology analysis to arrive at a diagnosis but relies on radiomics metrics.

The invention provides a radiomics feature extraction and analysis method for a liver cancer CT image, and the method comprises the steps: obtaining a multi-time-phase computed tomography image sequence, extracting contrast agent concentration distribution data of each time phase from the sequence, and obtaining a concentration change rate sequence through pixel value difference calculation; for the rapid change stage, applying a feature extraction algorithm to obtain high-density texture features from the image data of the stage, and obtaining quantitative description of subtle instantaneous change; determining the distribution position and intensity of the abnormal texture in the rapid change stage through the quantitative description, and generating an abnormal texture distribution diagram; and according to the abnormal texture distribution diagram and the overall trend summarization sequence, fusing the abnormal texture distribution diagram and the overall trend summarization sequence to generate a comprehensive dynamic change model, and determining an adjustment parameter of the analysis fineness degree of each stage.

The invention relates to the technical field of medical image processing, in particular to a pancreatic cancer focus classification method based on transfer learning, which comprises the following steps of: segmenting a pancreatic cancer intra-tumor region of interest and a pancreatic cancer peritumor region of interest in a CT (Computed Tomography) image; extracting intra-tumor, peritumor and intra-tumor-peritumor radiomics features from the intra-tumor region of interest and the peritumor region of interest; establishing a single-region classification model on the image omics characteristics in the tumor and around the tumor through a random forest RF model; and carrying out transfer learning on the single-region classification model by using the intratumoral-peritumoral radiomics characteristics to obtain a multi-region classification model for predicting the recurrence condition of the focus according to the intratumoral-peritumoral radiomics characteristics. According to the invention, the method for predicting the postoperative early recurrence condition of the PDAC focus by using the radiomics characteristics of the intra-tumor-peritumor region is established, the accuracy of the prediction result of the intra-tumor-peritumor region is optimized, and the accurate prediction of the early recurrence condition is realized.