10 results about "Tumor size" patented technology

The invention belongs to the technical field of medical image processing, and particularly relates to a non-functional pancreatic neuroendocrine tumor preoperative index prediction method and system. According to the method, on the basis of abdomen CT image lesion segmentation, pathological grading (low risk / high risk) and lymph node metastasis (LNM) risks are jointly predicted through a multi-task deep learning framework; according to the method, tumor morphological features, radiomics features and clinical parameters (such as tumor size and position) extracted by a segmentation network are combined, and high-precision prediction of preoperative key indexes is realized by using a feature fusion module and a lightweight classification head; through a cross-modal attention mechanism and multi-center data verification, the model achieves the pathologic classification AUC of 0.75 on an internal verification set, achieves the LNM prediction AUC of 0.78, and is significantly superior to traditional clinical experience judgment. According to the method, reliable decision support is provided for preoperative precise operation planning and personalized treatment.

The utility model discloses a model animal tumor size scaleplate device which comprises a scaleplate, a plurality of first measuring rulers and a plurality of second measuring rulers. A plurality of rectangular placing frames are distributed on the surface of the scale plate; the first measuring rulers extend in the x-axis direction and are arranged on the lower sides of the rectangular containing frames respectively. Each first measuring ruler is provided with a first calibration claw and a first sliding groove, a first sliding block is movably installed on the first sliding groove, a third calibration claw is arranged on the first sliding block, and x-axis scale marks are distributed on the surface of the first measuring ruler. The first measuring rulers extend in the x-axis direction and are arranged on the lower sides of the rectangular containing frames respectively. Each second measuring ruler is provided with a second calibration claw and a second sliding groove, a second sliding block is movably installed on the second sliding groove, a fourth calibration claw is arranged on the second sliding block, and y-axis scale marks are distributed on the surface of the second measuring ruler. The tumor detector can quickly obtain the length and width of the tumor and is suitable for taking pictures for evidence.

This invention discloses a two-stage controllable liver CT image generation method combining GAN and Diffusion models. The first step involves segmenting a real CT image using a pre-trained nnU-Net to obtain liver and tumor masks. The second step involves training a VQ-VAE model to establish a mapping between pixel space and latent space; training a conditional LDM to perform incisive generation of liver regions: predicting noise under the guidance of multiple hot tags of tumor size, unmasked region features, and the mask. The third step involves rapidly generating a full-frame CT image with global anatomical consistency using GAN; refining liver regions lacking texture detail using the trained LDM; and replacing the liver regions in the full-frame CT image after VQ-VAE decoding. This method combines the global synthesis efficiency of GAN with the local fine-grained generation capability of the diffusion model, effectively improving the realism and diversity of the final synthesized image while significantly reducing computational resources.

This invention discloses a tumor volume detection device for gastrointestinal endoscopy based on Time-of-Flight (TOF) technology, specifically in the field of tumor volume detection technology. It includes a gastrointestinal endoscope body. The front end of the endoscope body includes a CCD cable, two illumination sources, an air supply pipe, a water supply pipe, and a forceps pipe. The air supply pipe and water supply pipe are located between the two illumination sources. The CCD cable is located at the top of the forceps pipe. A mounting plate is fitted onto the outer side of the front end of the endoscope body. This invention uses an electric telescopic rod to retract a toothed plate, which, through meshing with gears, drives a drive shaft and a support plate to rotate, rotating the TOF sensor between the two illumination sources. The tumor is then irradiated by the illumination sources. During irradiation, the TOF sensor measures the size of the tumor by calculating the time difference between the time of light emission and the time of light reflection. The overall structure facilitates the measurement of tumor size.

The invention discloses an intelligent prediction system and method for early recurrence risk after liver cancer thermal ablation, and aims to solve the technical problem that prediction is not timely and inaccurate due to the fact that an existing prediction model ignores intraoperative dynamic information. According to the method, dynamic time sequence data such as tissue impedance and power in the thermal ablation process are collected in real time, and preoperative static data such as tumor size and alpha fetoprotein level are combined, so that a double-branch fusion prediction model is constructed. The model comprises a time sequence processing branch used for learning dynamic data time dependence and a static processing branch used for extracting static data features. By fusing the features of the two branches, the model can output a probability value representing the postoperative early recurrence risk. According to the method and the system, instant accurate risk stratification after operation is realized, and further, intraoperative intervention can be guided through real-time feedback, so that the early recurrence risk is actively reduced, and the prognosis of a patient is improved.

The invention discloses an artificial intelligence platform for comprehensive radiological image analysis across CT, MRI, and X-ray modalities. The system automatically detects and quantifies cardiac, pulmonary, and oncological abnormalities through multiple AI modules including heart localization, cardiac MRI quantification, pneumonia classification, and tumor segmentation / grading. The platform provides automated tumor detection, 3D volumetric segmentation, and malignancy grading and treatment planning. The system integrates hybrid CNN-Transformer networks, attention-based 3D segmentation, and multi-modal feature fusion, offering precise quantitative metrics for tumor size, volume, shape, and malignancy probability. It also a cardiac MRI analysis module measures atrial and ventricular volumes and detects atrial fibrillation or mitral valve stenosis. It calculates the cardiothoracic ratio and detects heart displacement. DICOM / PACS integration and federated learning enable clinical deployment and cross-institutional adaptability while preserving patient data privacy and assists clinicians in accurate and rapid diagnosis while minimizing human error. This first-in-world platform facilitates early detection, staging, grading, and treatment planning with high reproducibility and explainable outputs.

A method to develop one or more morphometric classifiers to identify a mismatch repair deficiency (MMRD). The method provides a non-invasive method of characterizing MMRD that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.

The invention relates to the field of tumor prognosis evaluation, and discloses an image and pathology-based breast cancer 70 gene detection risk evaluation method, which comprises the following steps of: selecting an HR < + > / HER2-early breast cancer patient meeting a condition as a research object, and acquiring an ultrasonic image and clinical pathology information of the HR < + > / HER2-early breast cancer patient; the method comprises the following steps: carrying out ROI (Region of Interest) sketching on a tumor region through ImageJ software, and extracting image omics characteristics by utilizing a Pyradiomics tool; after z-score standardization is carried out on the features, LASSO regression is adopted to carry out feature screening, and a radiomics score and a tumor size are calculated; combining clinical parameters such as age, lymph node metastasis number, ER, PR, Her-2, Ki67, histological grading, vessel invasion and the like to construct a column graph model; a recurrence risk probability value is further calculated by using a linear predictor, and the patients are classified into high-risk or low-risk categories. By integrating multi-dimensional structured data, a recurrence risk prediction model with excellent performance and high clinical applicability is established.

Device (2) and method for determining a value quantifying a risk of relapse of breast cancer for a patient A device for determining a value quantifying a risk of relapse of breast cancer for a patient comprises a data storage (114) adapted to store patient data associating, for each given patient at a least one whole histological slide image and patient clinical data comprising at least a tumor size, a number of positive lymph nodes, a tumor grade, and progesterone receptor (PR) positivity value, a feature extractor (1210, 1220) arranged to receive a whole histological slide image and associated patient clinical data, to derive tumor architecture features comprising a tumor area, an invasive tumor nest density, a tumor density at the invasive front, and an in situ tumor nest density, tumor microenvironment features comprising a border composition of in situ tumor, a variance of healthy gland size, an inflammatory stroma area, and an average nuclei size of stromal cells, and mitosis features comprising a mitotic hotspot count and a mitotic density, and to return a patient feature vector comprising said tumor architecture features, said tumor microenvironment features, said mitosis features, and features derived from said associated patient clinical data, a risk classifier (1230) using a machine learning module which is arranged to receive a patient feature vector as an input and to return a value quantifying a risk of relapse of breast cancer for a patient, said machine learning module having been trained with a dataset of labelled patient feature vectors and being arranged to return a risk value. This device is arranged to receive a set of patient data of a given patient, to provide at least some of said given patient's whole histological slide image and said given patient's clinical data as inputs to the feature extractor, to provide at least some of the resulting patient feature vectors to the risk classifier, and to return a value quantifying a risk of relapse of breast cancer for a patient for said given patient based on the outputs of the risk classifier.

The invention discloses a real-time intelligent measurement method for the size of tumors under gastrointestinal mucosa based on an ultrasonic endoscope, intelligent measurement of the size of SMTs under EUS is realized by using an artificial intelligence technology, and the method has the characteristics of real-time performance, standardization and homogeneity. According to the method, the manual measurement deviation is reduced, the energy and time investment of an endoscopic physician is reduced, and a quantitative and objective decision basis is provided for diagnosis, treatment and follow-up visit of SMTs. According to the method, a multi-scale feature fusion and self-attention enhanced EUS image tumor segmentation model is utilized, the same tumor region under different EUS cross sections is identified, the area of the tumor cross section is quantified, the maximum tumor cross section is automatically screened in real time, and the difference of subjective selection of an endoscopic physician is reduced; and the long diameter and the transverse diameter of the tumor section under the EUS are determined by using a standardized pixel-level dynamic cutting method, so that the standardization of the measured diameter is realized, and the manual operation steps of an endoscopic physician are reduced.