70 results about "Hepatic tumour" patented technology

The invention relates to the technical field of minimally invasive ablation, in particular to a liver tumor microwave ablation postoperative contraction deformation prediction method, which comprises the following steps: performing unified standardization processing on different periods of liver MRI images; accurately marking a preoperative liver tumor area, a postoperative ablation area and a postoperative liver tumor ghost area of the liver MRI image to obtain masks of the corresponding areas; designing a distance perception function as an attention parameter of different areas of the liver MRI image; constructing a multi-sequence distance guide complementary network model; selecting a loss function; selecting an elastic registration method to perform pre-operation and post-operation registration; and calculating an ablation safety boundary through the ablation area mask and the adjusted tumor mask. The liver tumor microwave ablation postoperative curative effect evaluation accuracy can be effectively improved, and a foundation is laid for formulating a subsequent treatment plan of a patient.

The invention discloses a liver tumor image real-time segmentation method and system based on a YOLO algorithm, and the method comprises the steps: obtaining CT images and MRI images, and calculating the contrast indexes and signal-to-noise ratio indexes of a plurality of CT images; preprocessing the CT image, and dynamically adjusting an image enhancement strategy; a residual attention module is added on the basis of the YOLOv8 network, a multi-scale mask branch is introduced, and an optimized YOLO-Med segmentation network is constructed; inputting the enhanced CT image into a segmentation network, and training the segmentation network in combination with a loss function; and when an MRI image is input, through a multi-modal feature fusion mechanism, features of the MRI image and the enhanced CT image are aligned and fused and then are input into the segmentation network, and the segmentation network outputs a pixel-level segmentation mask for real-time segmentation of the liver and the tumor. According to the invention, by fusing the density characteristic of CT and the soft tissue resolution capability of MRI, the small tumor (diameter lt; 5 mm).

The invention provides a liver tumor early diagnosis method and system based on artificial intelligence, and relates to the technical field of biomedical engineering, and the method comprises the steps: obtaining original information metadata related to the liver, the original information metadata comprises image data original information metadata and biomarker original information metadata, the image data comprises liver CT (computed tomography) and MRI (magnetic resonance imaging) image data, the biomarkers comprise serum tumor marker levels, and the physiological and environmental factors of the patients comprise age, gender, dietary habits and genetic backgrounds of the patients. According to the method, the problems that a tumor in a small and complex area is easily missed or misdiagnosed in the prior art are solved, the path density is predicted through denoising, segmentation and feature extraction of image data preprocessing in combination with a path simulation model based on a graph theory and a Monte Carlo simulation method, and a potential tumor area can be accurately judged.

The invention discloses a 2.5 D dual-scale wavelet SAM-GAN liver tumor target region segmentation method, which comprises the following steps: establishing a segmentation model which is composed of a preprocessing module, a generation module and a discrimination module; an original image enters the preprocessing module and is subjected to neighborhood slice feature aggregation and anatomical perception to generate a 2.5 D image, the 2.5 D image is optimized and enhanced in the generation module and then input into the judgment module, and the judgment module evaluates the authenticity of the image output by the generation module and provides feedback training for the generation module. According to the method, the defects in the prior art can be overcome, the feature extraction capability on the liver tumor data set is improved, and the training stability and the model generalization capability are enhanced.

The present application provides a pharmaceutical combination of a quinoline derivative and an antibody, which comprises an immune checkpoint inhibitor and a tyrosine kinase inhibitor, wherein the tyrosine kinase inhibitor is a compound of formula I or a pharmaceutically acceptable salt thereof. The pharmaceutical combination in the present application shows good anti-lung tumor and anti-liver tumor activities.

The invention provides a self-driven micro-robot for a liver tumor targeted therapy and real-time monitoring system, and relates to the technical field of biomedical engineering. The monitoring main system comprises a self-driven navigation and positioning module, an intelligent drug delivery module, a tumor monitoring and state evaluation module, a multi-modal data fusion module, a real-time self-learning decision module, an advanced molecular simulation and drug optimization module, a safety communication module, a power grid environment adaptation module and a fault detection and recovery module; the self-driven navigation and positioning module adopts a mode of combining a quantum genetic algorithm, a fuzzy constraint optimization algorithm and a self-adaptive simulated annealing algorithm, so that the navigation accuracy and positioning efficiency of the robot in a complex environment in the liver are remarkably improved, and the quantum genetic algorithm has the advantages that by means of the strong parallelism of quantum calculation, the positioning accuracy of the robot is improved; according to the method, numerous potential path combinations can be quickly explored, the path planning time is greatly shortened, and meanwhile, the path planning accuracy is improved.

The invention discloses a preparation method of a nuclear magnetic resonance imaging contrast agent based on surface hepatocyte targeting molecule modification ferroferric oxide nanoparticles, which comprises the following steps: synthesizing ferroferric oxide nanoparticles (Fe3O4 NPs) by a thermal decomposition method, and connecting the Fe3O4 NPs with a ligand dopamine-PEG2000-EOB carrying hepatocyte targeting molecules by a ligand exchange method to obtain the nuclear magnetic resonance imaging contrast agent. The nanoparticles Fe3O4-EOB-PEG capable of carrying out selective T2 nuclear magnetic resonance imaging on the hepatocytes are formed. The method is simple and easy to operate, the controllability of the preparation process is high, the biocompatibility of the obtained product is good, T2 signals of hepatocytes can be remarkably improved, the good early diagnosis effect on liver tumors is achieved, and wide application prospects are achieved.

The invention discloses an enhanced Swinin-Unet liver tumor segmentation method fusing blood vessel guidance and adaptive deformation attention. Through combination of elastic B-spline registration and affine transformation, nonlinear deformation is compensated, a multi-modal image space alignment error is reduced, and tumor boundary blur and feature dislocation are reduced. Topological features are extracted in combination with a blood vessel reconstruction module, and model attention is effectively guided. The encoder adopts a convolution pool and a deformable attention SADR down-sampling, and a lightweight deformable attention module is integrated through jump connection, so that effective multi-scale feature fusion and space alignment are realized, and the model perceptibility is enhanced. The graph theory model is embedded into liver lobe partition and blood vessel curvature constraint, and it is ensured that the segmentation result is strictly matched with blood vessel blood supply topology. In order to solve the class imbalance problem, an FA loss function is provided and combined with a structure correction algorithm (SGDR), so that incomplete segmentation and false positive artifacts are effectively reduced. Experimental verification on a LiTS data set shows that the method is superior to an existing method in liver and tumor segmentation, and Dice coefficients reach 93.38% and 91.39% respectively.

The invention discloses a navigation system for a liver tumor resection operation, and aims to solve the problems that in the prior art, the accuracy of the liver tumor resection operation is insufficient, the risk in the operation is high, and the evaluation after the operation is incomplete. The system comprises a preoperative data acquisition module, a preoperative three-dimensional modeling module, an intraoperative positioning module, an operation navigation module, a risk prompt module and a postoperative data analysis module. Through preoperative CT or MRI image data acquisition, automatic segmentation and three-dimensional reconstruction of liver, tumor and blood vessels are carried out by using a deep learning algorithm, and a high-precision anatomical model is generated. During the operation, the position of the surgical instrument is obtained in real time by using an optical tracking or electromagnetic positioning technology, spatial precise alignment of the surgical instrument with a tumor and surrounding tissues is realized in combination with a model registration algorithm, and a surgical path is displayed through a real-time navigation interface. And the risk prompting module calculates an excision risk coefficient and prompts a doctor in real time based on the shortest distance between the tumor and the blood vessel, the relative distance between the instrument and the target area and the trajectory change.

The invention discloses a liver tumor diagnosis method based on a self-supervision and multi-phase contrast enhanced CT image. The method comprises the following steps: collecting and preprocessing a CT image of a liver tumor; training a target detection model by using the marked CT image; processing the preprocessed CT image by using the trained target detection model, obtaining a focus detection frame, performing cutting and data enhancement according to the focus detection frame, performing resampling, adding a real joint label to the focus detection frame, and constructing a second training set and a test set; constructing a classification diagnosis model based on multiphase data based on self-supervision and knowledge distillation, performing iterative training by using the second training set, and adjusting parameters according to a total loss function of the classification diagnosis model in the training process to obtain a final liver tumor classification diagnosis model; and inputting the CT image in the test set into the final liver tumor classification diagnosis model to obtain a diagnosis result. The precision, accuracy and efficiency of liver tumor diagnosis can be improved.

An existing liver tumor automatic segmentation method is insufficient in expression in small focus, low-contrast edge and long-range space dependence modeling, and consequently high false positive and boundary deficiency are caused. Pure CNN is limited by a receptive field, pure Mama easily loses local details, multi-level attention stacking significantly increases parameter quantity, and traditional side supervision differential correction is difficult to accurately focus an uncertain area. The invention provides an end-to-end network, parallel ResNet and Mamba dual-coding and direct reaching a decoder after AFF fusion at the same scale, bottom features are accessed to a multi-scale feature fusion module to complete deep semantic enhancement, and a decoding side forms a GARS module concentration boundary difficult-to-distinguish pixel by matching an MSCB-EUCB-LGAG lightweight chain with four-stage Gaussian attenuation residual self-correction. Clinical level, the method can significantly reduce leak detection of small tumors, reduce false positive, and maintain geometric integrity of edges.

The application discloses a liver tumor CT image segmentation device and system and a storage medium. The application reads a liver tumor CT image, pre-processes the liver tumor CT image to obtain a target liver tumor feature image, and segments the target liver tumor feature image through a preset liver tumor segmentation model based on compressed attention to obtain a liver tumor segmentation image. The liver tumor segmentation model based on compressed attention is embedded with a gradient centering optimizer, and the optimizer is used to standardize a weight space and an output feature space and improve the generalization performance of the model. Compared with the existing liver tumor segmentation image segmentation mode, the liver tumor segmentation model based on compressed attention embedded with the gradient centering optimizer can learn more representative features in a semantic segmentation task and obtain a liver tumor segmentation image with higher segmentation accuracy.

The application provides an unsupervised liver tumor CT image segmentation method based on manual features, relates to the technical field of deep learning, and first collects normal liver CT images as a data set, and selects a tumor position in combination with clinical knowledge. A texture similar to real imaging is generated through three-dimensional simple noise binary mask, and histological features are used for morphological modeling. Then, the tumor texture is superimposed with the liver CT image at the selected position to synthesize a new liver tumor CT image. A segmentation model is trained by using the CT image with annotations, and the synthesized image is used for medical image segmentation. Finally, the segmentation results are evaluated by using the Dice coefficient, the Hausdorff distance, the standardized surface distance and the surface distance index. The application is helpful for artificially synthesizing tumor lesion images and generating voxel-level annotations, and provides a new perspective for solving the medical image annotation challenge and promoting liver tumor diagnosis and treatment.

The application belongs to the technical field of biotechnology, and particularly relates to a colorectal cancer high liver metastasis cell line and a preparation method and application thereof. The cell line was preserved in the China Center for Type Culture Collection on January 14, 2026, and the preservation number is CCTCC NO: C202618. The cell line is derived from a mouse colorectal cancer cell line MC38, and is constructed by lentivirus transfection to express luciferase stably, and is obtained by continuously performing at least five rounds of liver metastasis tumor orthotopic iteration screening in C57BL / 6 mice through rectal submucosal injection. The MC38-P06 cell line provided by the application has a significantly enhanced liver metastasis ability, a shorter MC38-P01 model time, a higher liver tumor load, and a shorter mouse survival period, and can be used for screening and evaluating anti-liver metastasis drugs, researching liver microenvironment regulation mechanisms, and identifying liver metastasis related diagnostic markers.

The invention provides a preparation method and an extraction method of a tissue in-situ magnetic material-protein crown complex, and belongs to the technical field of protein crown extraction. The method comprises the following steps: injecting a magnetic material into a tumor-bearing mouse, and separating spleen, liver, tumor and bone marrow or collecting peripheral blood; wherein the spleen, the liver or the tumor is digested, centrifuged and filtered, and then filtrate is collected; flushing and filtering the bone marrow, and taking filtrate; centrifuging peripheral blood and taking serum; and placing the magnetic column in a magnetic field, adding a filtrate or serum into the magnetic column, magnetically adsorbing the magnetic material-protein crown complex, washing the magnetic column after the filtrate or serum completely flows through the magnetic column, separating the magnetic column from the magnetic field, eluting the magnetic column adsorbed with the magnetic material-protein crown complex, performing ultrafiltration and centrifugation on an eluent, and collecting a concentrated solution, thereby obtaining the product. According to the method disclosed by the invention, efficient extraction and quantitative analysis of in-situ protein crowns of various tissues are realized, and the magnetic material-protein crown complex can be efficiently enriched. The extraction method is simple and rapid.

This invention discloses a liver tumor segmentation method, system, and storage medium based on plain CT scans, belonging to the field of medical image processing technology. It addresses the problem of the lack of existing liver tumor segmentation methods that can both improve the clarity of liver tumor boundaries and preserve detailed information. The key technical points of this invention include: Step 1, acquiring plain CT scan data of the liver from liver tumor patients; Step 2, segmenting the plain CT images using a pre-trained segmentation model to obtain a liver region mask; the segmentation model is based on the UNet model, using PVT-V2 as the backbone network of the encoder, and the decoder adopts a cascaded upsampling path, fusing a residual denoising module, a gated attention module, a multi-scale feature fusion module, and a depth supervision mechanism in each stage; Step 3, further segmenting the liver region mask using the segmentation model to obtain a liver tumor mask; Step 4, extracting features and classifying the liver region mask using a classification model to obtain the benign or malignant tumor result.

The invention provides a full-autonomous ultrasonic robot real-time liver tumor tracking system, and relates to the technical field of medical images and robots. According to the invention, through deep fusion of a multi-mode force touch sensing closed loop and an image segmentation visual closed loop, full-autonomous ultrasonic tracking of liver tumors is realized. Mechanical sensing ensures that the probe is always in stable contact with a patient in a proper posture and force, and visual segmentation feedback ensures that the probe is continuously aligned with and focuses on a tumor target. Under the synergistic effect of the two paths of feedback, the robot can automatically complete the whole process from probe placement, stable attachment, target recognition to real-time tracking, the uncertainty of manual operation is greatly relieved, and the safe, efficient and intelligent liver tumor real-time ultrasonic monitoring robot system is achieved.

The invention discloses an automatic liver tumor segmentation method based on a UNet model fused with an SENet attention mechanism. The method comprises the following steps that an encoder of the UNet model receives input image data; the method comprises the following steps: gradually extracting the features of an input image through an encoder of a UNet model, reducing the spatial resolution, enabling the encoder to specifically extract the features of the input image with higher resolution capability by using an SENet attention mechanism, and screening out effective representative image feature information; the bottom convolution block performs convolution processing on the image feature information output by the encoder and then inputs the image feature information into the decoder; and performing up-sampling operation through a decoder of the UNet model, and generating a segmentation result through a 1 * 1 convolution kernel. According to the method, the subject Unet model and the channel attention mechanism SENet are included, and the targeted recognition and prediction capability of the model on the tumor existence area can be effectively improved through the attention mechanism, so that the diagnosis accuracy and efficiency of a doctor are improved, and better diagnosis and treatment experience is brought to a patient.

The present invention provides a method and system for evaluating the response after liver tumor intervention based on a generative adversarial network. A method for evaluating the response after liver tumor intervention based on a generative adversarial network includes the following steps: S1. Training a deep learning model to obtain an evaluation model capable of giving a prognosis evaluation result; S2. The evaluation model takes the plain CT scan after liver surgery and the postoperative serological test information as inputs and outputs a prognosis evaluation result. The present invention does not require liver puncture, which can reduce the trauma risk and medical resources for patient prognosis estimation; after the operation, the patient of the present invention does not need to take enhanced CT, and there will be no radiation risk and medical resource consumption caused by taking enhanced CT. At the same time, it can give a prognosis evaluation result based on enhanced CT, ensuring that the CT image used as the evaluation basis can clearly show the lesion, having the effect of enhanced CT, and being able to give a more accurate evaluation result.

Provided is a method and apparatus of intelligent analysis for liver tumors, including an analysis module for receiving YOLOR-based training to acquire sufficient intelligence to detect and locate liver tumor automatically and attain a mAP score as high as 0.56 required to distinguish lesions of benignant and malignant liver tumors in medical images from each other, attaining a mAP score of 0.628 for tumors at least 5 cm in size or a mAP score of 0.33 for tumors less than 5 cm in size. Thus, the area under the liver tumor differentiation curve of the analysis module and the mAP score reach 0.9 and 0.56 respectively. The values equal those of the effect of the diagnosis rate of liver tumors with CT and MRI in practice. The method is advantageous in terms of higher speed and thus can diagnose liver tumors earlier, preclude delays and radiation, but incur low cost.

The bending-adjustable embolism double-balloon intervention catheter comprises a catheter body and a first balloon arranged at the far end of the catheter body, the far end of the catheter body can be bent, a second balloon is further arranged at the far end of the catheter body, and a balloon cavity capable of filling the first balloon and the second balloon is formed in the catheter body. A through hole is formed in the portion, located between the first balloon and the second balloon, of a catheter body of the catheter, a medicine cavity channel is formed in the catheter and communicated with the through hole, a guide wire cavity channel is further formed in the catheter, and the far end of the guide wire cavity channel penetrates through the catheter. Compared with the prior art, the medicine is prevented from flowing back and flowing to normal blood vessels, the problem that the medicine flows back in the liver tumor interventional embolism or precise chemotherapy process is solved, the problem that the medicine gathers and converges to normal branch blood vessels for tumor blood supply is solved, the medicine only gathers to the tumor blood supply last branch blood vessels, the accuracy of embolism or chemotherapy is improved, and the treatment effect is improved. And the ability of smoothly passing through the liver blood vessel and quickly reaching the diseased region is achieved.

The application discloses a multi-branch liver tumor segmentation method based on UNet, and comprises the following steps: first, a multi-branch liver tumor segmentation network based on UNet is constructed; then, a training sample set constructed by CT images in which liver tumors have been segmented in advance is used to perform deep learning training on the multi-branch liver tumor segmentation network based on UNet, so that a trained multi-branch liver tumor segmentation network based on UNet is obtained; finally, a CT image currently in need of liver tumor segmentation is sent to the trained multi-branch liver tumor segmentation network based on UNet, so that the CT image in which the liver tumor has been segmented is obtained. By strengthening the receptive field of the segmentation network feature map and fully utilizing the channel and spatial structure information, pixel-level details and spatial information can be better captured, so that the segmentation performance of the network on medical images is improved. Different from existing methods, the network architecture proposed in the application can capture fine details of local edges and global multi-scale information at the same time, so that spatial consistency is ensured.

The application relates to a preoperative and intraoperative liver point cloud data registration system and method, a terminal and a storage medium. The method comprises the following steps: extracting local mixed features of preoperative point cloud data and intraoperative point cloud data respectively; fusing the local mixed features to obtain global features of the preoperative point cloud and global features of the intraoperative point cloud; fusing the local mixed features of the preoperative point cloud, the global features of the preoperative point cloud and the global features of the intraoperative point cloud to obtain fusion features of the preoperative point cloud; similarly, fusion features of the intraoperative point cloud are obtained; fusing the fusion features of the preoperative point cloud and the fusion features of the intraoperative point cloud to obtain respective overlapping area masks and decoding features; obtaining a spatial transformation matrix of the preoperative point cloud and the intraoperative point cloud; and applying the spatial transformation matrix to the preoperative point cloud data to obtain a registration result of the preoperative point cloud data and the intraoperative point cloud data. The application can provide convenience for accurate positioning of liver tumors, shorten the operation time, and improve the accuracy and safety of the operation.

The invention discloses vacuum auxiliary ablation and rotary cutting integrated equipment, and relates to the field of ablation and rotary cutting equipment.The vacuum auxiliary ablation and rotary cutting integrated equipment comprises a system host, a rotary cutting handle, a rotary cutting tool bit, an ablation needle, a coaxial needle, a negative pressure module, a cooling module and a foot switch; the rotary cutting tool bit is connected to the outer portion of the rotary cutting handle, and the coaxial needle is connected with the negative pressure module. The touch screen is used for receiving user input and displaying system information, the first interface is connected with the rotary cutting handle through a cable, the vacuum auxiliary ablation and rotary cutting integrated device can achieve first ablation and then rotary cutting or first rotary cutting and then ablation of thyroid / liver tumors, and the first ablation and the second rotary cutting can be freely matched according to operation requirements; the control interface is automatically switched according to the consumable types, the output power is adjusted, and the ablation temperature is monitored and fed back in real time.

The application provides a liver tumor segmentation method, and belongs to the field of medical image segmentation. In view of the starting point of how to improve the global context feature extraction ability and how to efficiently combine with local information, two parallel encoders are used, wherein a VGG convolutional neural network branch is introduced to extract local features, and an axial decomposition self-attention branch is designed to extract global features, and then a shared residual fusion decoder is used to effectively integrate the information of the two branches. While reducing the model calculation complexity and reducing the training parameter amount, the application can significantly improve the segmentation effect of liver tumors and reduce the false segmentation or misclassification phenomenon.

The invention discloses a method for creating a tumor personalized treatment simulation model based on overlapped microwave ablation in the technical field of biomedical engineering and computer simulation, and the method comprises the steps: constructing a three-dimensional geometric model containing a liver and a tumor based on medical image data of a patient; and establishing an overlapping microwave ablation finite element model coupling electromagnetic wave propagation and biological tissue heat transfer according to the three-dimensional geometric model. According to the method, the overlapping ablation process is dispersed into a plurality of continuous'ablation-cooling-re-ablation 'stages, the thermal field of the previous stage is inherited as the initial condition of the next stage, and the tissue characteristics changed due to temperature change are dynamically updated, so that accurate simulation of the overlapping thermal field cumulative effect is realized, and the accuracy of the thermal field cumulative effect is improved. Therefore, the prediction precision of the form of the final solidification area of overlapping ablation of the liver tumor is improved, and a doctor can clearly know the possible effect of ablation treatment according to the predicted visual solidification area and the quantitative evaluation index.

The invention relates to a liver tumor segmentation method and device based on axial deep convolution and Transform, and the method comprises the steps: carrying out the training of a liver tumor segmentation model based on a model training data set, and obtaining a trained liver tumor segmentation model; and inputting a to-be-segmented liver tumor image into the trained liver tumor segmentation model to obtain a segmented image of the to-be-segmented liver tumor image. According to the constructed liver tumor segmentation model, the axial deep convolution module is used for replacing original traditional convolution, so that the calculation complexity is reduced; an interactive attention Transform module is designed to jointly utilize a plurality of small-scale feature maps, richer global features are obtained with lower calculation complexity, and a mark perception MLP module is adopted to process features of different dimensions through displacement axial operation, so that the learning ability of local detail information is improved.

The present application relates to the technical field of image reconstruction, in particular to a liver tumor interventional preoperative blood vessel three-dimensional model reconstruction system, which can realize the following steps through the mutual cooperation between multiple modules: obtaining a liver CTA three-dimensional matrix of a target patient before liver tumor intervention; determining the blood vessel confidence corresponding to each voxel in the tumor peripheral region and the normal liver region; determining the gradient distortion degree corresponding to each voxel in the tumor peripheral region; determining the effective blood vessel proportion in the tumor peripheral region; performing adaptive blood vessel fracture completion screening processing according to the gradient distortion degree and the blood vessel confidence to obtain the tumor peripheral blood vessel region; and performing blood vessel reconstruction based on the effective blood vessel proportion in the tumor peripheral region and the gray value corresponding to the voxel in the tumor peripheral blood vessel region. The present application realizes the identification of the tumor peripheral blood vessel region and improves the accuracy of identification, thereby improving the rationality of liver tumor interventional preoperative blood vessel three-dimensional model reconstruction.

This invention discloses a semantic segmentation method for liver tumor CT images based on VIT-CNN and frequency awareness, belonging to the fields of medical image processing and computer vision technology. The method first preprocesses the liver tumor CT images to eliminate interference and standardize their dimensions. Then, it constructs a VIT-CNN hybrid feature extraction architecture, capturing local detail features through CNN branches and global context features through VIT branches. A frequency-aware mechanism is introduced to enhance and adaptively fuse the two types of features in the frequency domain, highlighting effective features and suppressing interference. An improved U-Net segmentation head and a hybrid loss function are designed, the model is trained, and the CT images are segmented. Finally, the segmentation performance is evaluated using multiple metrics. This invention solves the problems of unbalanced global and local feature capture, weak anti-interference ability, and insufficient segmentation accuracy in existing methods, achieving accurate segmentation of liver tumor CT images and providing reliable support for clinical diagnosis and treatment planning.

A single-domain monoclonal antibody with high affinity for the tumor antigen glypican-3 (GPC3), which was functionalized with a chelating group and radiolabeled with a positron-emitting radionuclide (18F), is described. The radiolabeled GPC3-specific single-domain antibody exhibited an exceptionally high tumor to liver binding ratio, enabling its use to accurately detect GPC3-positive liver tumors, such as hepatocellular carcinoma (HCC). The radiolabeled GPC3-specific single-domain antibody can be used, for example, as an imaging agent in positron-emission tomography (PET)-based detection of GPC3-positive tumors.