ObjectiveThe purpose of this study was to better delineate the clinical spectrum of periventricular nodular heterotopia (PNH) in a large patient population to better understand social support in people with PNH and epilepsy in west China. Specifically, this study aimed to relate PNH subtypes to clinical or epileptic outcomes and epileptic discharges by analyzing anatomical features.
MethodsThe study included 70 patients with radiologically confirmed nodular heterotopias and epilepsy. We also recruited healthy controls from nearby urban and rural areas. People with PNH and epilepsy and healthy controls were gender-and age-matched. Two-sided Chi-square test and Fisher's exact t-test were used to assess associations between the distribution of PNHs and specific clinical features.
ResultsBased on imaging data, patients were subdivided into three groups: (a) classical (bilateral frontal and body, n=25), (b) bilateral asymmetrical or posterior (n=9) and (c) unilateral heterotopia (n=36). Most patients with classical heterotopia were females, but were mostly seizure-free. Patients with unilateral heterotopia were prone to develop refractory epilepsy.
ConclusionsEach group's distinctive genetic mutations, epileptic discharge patterns and overall clinical outcomes confirm that the proposed classification system is reliable. These findings could not only be an indicator of a more severe morphological and clinical phenotype, but could also have clinical implications with respect to the epilepsy management and optimization of therapeutic options.
Objective To evaluate the clinical value of cardiac MRI for the diagnosis of viral myocarditis (VMC). Methods Such databases as PubMed (1950 to 2009), EMbase (1974 to 2009), and The Cochrane Library (December 2009) were searched to include clinical research reports of diagnosing viral myocarditis with MRI. QUADAS items were used to evaluate the quality of the included studies. The Meta-disc software was used to conduct merger analyses on sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. The Heterogeneity test was performed and summary receiver operating characteristic curve (SROC) was completed. Results Five trials were included. The value of merger sensitivity, specificity, and diagnostic odds ratio (DOR) were 0.94, 0.69, 2.76, and 28.11, respectively. The area under of SROC curve (AUC) was 0.871 9. Conclusion The current evidence shows that cardiac MRI has high sensitivity (94%) and moderate specificity (69%) in the diagnosis of viral myocarditis. The positive rate in the viral myocarditis group is 28.11 times as high as that in the non-viral myocarditis group, so Cardiac MRI has good diagnostic values for viral myocarditis.
OBJECTIVE:To investigate the diagnostic meaning of MRI in intraocular tumors.
METHODS:Forty-six cases of confirmed intraocular tumors,including choroidal melanoma(20 cases),retinoblastoma(18 cases),Coats disease(6 cases)and choroidal hemangioma(2 cases),were studied with MRI and compared with ultrasonography and CT.
RESULTS:In making discoveries about intraocular tumors,there were no sighificant difference between MRI and B-ultrasonography or CT （P＞0.03，chi;2=1.0716）while there were highly statistic sighificance in dediding characters and position （P＜0.01，deceding character chi;2=29.8314，positionchi;2=13.659）of them.
CONCLUSION:Among the examinations to find out about the position,character and secondary pathological insults of in traocular tumors MRI might be more available than CT and ultrasonography.
（Chin J Ocul Fundus Dis,1997,13:93-95 ）
Focus on the inconsistency of the shape, location and size of brain glioma, a dual-channel 3-dimensional (3D) densely connected network is proposed to automatically segment brain glioma tumor on magnetic resonance images. Our method is based on a 3D convolutional neural network frame, and two convolution kernel sizes are adopted in each channel to extract multi-scale features in different scales of receptive fields. Then we construct two densely connected blocks in each pathway for feature learning and transmission. Finally, the concatenation of two pathway features was sent to classification layer to classify central region voxels to segment brain tumor automatically. We train and test our model on open brain tumor segmentation challenge dataset, and we also compared our results with other models. Experimental results show that our algorithm can segment different tumor lesions more accurately. It has important application value in the clinical diagnosis and treatment of brain tumor diseases.
Objective
The method of metabonomics based on nuclear magnetic resonance (NMR) imaging was used to explore the difference in metabolites of serum and bile, and to analyze the metabolic variation related to the pathogenesis of gallbladder stones between normal people/liver transplantation donors and patients with gallbladder stones.
Methods
Prospectively collected the serum samples (17 cases) and bile samples (19 cases) in 19 patients with gallbladder stones who underwent surgery in West China Hospital form March 2016 to December 2016, as well as the serum samples of 10 healthy persons and the bile samples of 15 liver transplantation donors at the same time period. The differences of metabolites in the blood and bile in these 3 groups were compared by using 1H-NMR metabonomics technology and chemometric methods.
Results
The concentrations of valine, alanine, lysine, glutamine, glutamate, pyruvate, creatinine, choline, alpha-glucose, beta-glucose, tyrosine, histidine, and hypoxanthine in serum of patients with gallbladder stones decreased significantly, comparing with those of healthy people without gallbladder stones (P<0.05), while 1, 2-propanediol, acetoacetate, and lactate increased significantly in the serum of patients with gallbladder stones (P<0.05). The concentrations of taurine conjugated bile acids, glycine conjugated bile acids, choline, and phosphatidylcholine decreased significantly in the bile of patients with gallbladder stones when compared with those of liver transplantation donors (P<0.05), while cholesterol increased significantly in the bile of patients with gallbladder stones (P<0.05).
Conclusions
There are significant differences of the serum and bile metabolites between patients with gallbladder stones and healthy men without gallbladder stones/liver transplantation donors. 1H-NMR metabonomics is helpful to investigate the pathogenesis of gallbladder stones.
ObjectiveTo investigate the CT and MR imaging manifestation of solid-pseudopapillary neoplasm of pancreas (SPNP), deepen the understanding of imaging and clinical pathological characteristics of SPNP and improve the level of diagnosis.
MethodsBetween Jan 2010 and Dec 2015, the CT and MR imaging data of seven patients with SPTP proved by surgery and histopathologically were analyzed retrospectively. The following imaging features were reviewed: tumor size, location, shape, margin, encapsulation, calcification, hemorrhage, solid-cystic ratio, pancreatic and bile duct dilatation, the manifestation of plain scan and dynamic pattern of enhancement.
ResultsThe population comprised 7 women, the average age was 28.3 years oldwith a median tumor size of 5.7 cm. Tumors were located at body tail of pancreas in 5 cases, at the head in 1 case, and at the tail in 1 case. The tumor were exogenous in 5 cases, endogenous in 2 cases. Five tumors showed the regular margin, inregular in 2 cases. Four cases of plain and enhanced CT scan showed cystic-solid tumors, the solid and encapsulation part ofSPNP presented as hipo-, iso-density, and gradually enhancement after injecting contrast medium. Three cases were examined by MRI, 2 cases appeared hemorrhage, tumor located in the head of pancreas leaded to the secondary ducts dilatations in 1 case. Conciusions There are some characteristics in CT and MRI manifestation of SPNP. Accurate diagnosis meybe created by the imaging study combined with the clinical feature.
When applying deep learning algorithms to magnetic resonance (MR) image segmentation, a large number of annotated images are required as data support. However, the specificity of MR images makes it difficult and costly to acquire large amounts of annotated image data. To reduce the dependence of MR image segmentation on a large amount of annotated data, this paper proposes a meta-learning U-shaped network (Meta-UNet) for few-shot MR image segmentation. Meta-UNet can use a small amount of annotated image data to complete the task of MR image segmentation and obtain good segmentation results. Meta-UNet improves U-Net by introducing dilated convolution, which can increase the receptive field of the model to improve the sensitivity to targets of different scales. We introduce the attention mechanism to improve the adaptability of the model to different scales. We introduce the meta-learning mechanism, and employ a composite loss function for well-supervised and effective bootstrapping of model training. We use the proposed Meta-UNet model to train on different segmentation tasks, and then use the trained model to evaluate on a new segmentation task, where the Meta-UNet model achieves high-precision segmentation of target images. Meta-UNet has a certain improvement in mean Dice similarity coefficient (DSC) compared with voxel morph network (VoxelMorph), data augmentation using learned transformations (DataAug) and label transfer network (LT-Net). Experiments show that the proposed method can effectively perform MR image segmentation using a small number of samples. It provides a reliable aid for clinical diagnosis and treatment.
To address the issues of difficulty in preserving anatomical structures, low realism of generated images, and loss of high-frequency image information in medical image cross-modal translation, this paper proposes a medical image cross-modal translation method based on diffusion generative adversarial networks. First, an unsupervised translation module is used to convert magnetic resonance imaging (MRI) into pseudo-computed tomography (CT) images. Subsequently, a nonlinear frequency decomposition module is used to extract high-frequency CT images. Finally, the pseudo-CT image is input into the forward process, while the high-frequency CT image as a conditional input is used to guide the reverse process to generate the final CT image. The proposed model is evaluated on the SynthRAD2023 dataset, which is used for CT image generation for radiotherapy planning. The generated brain CT images achieve a Fréchet Inception Distance (FID) score of 33.159 7, a structure similarity index measure (SSIM) of 89.84%, a peak signal-to-noise ratio (PSNR) of 35.596 5 dB, and a mean squared error (MSE) of 17.873 9. The generated pelvic CT images yield an FID score of 33.951 6, a structural similarity index of 91.30%, a PSNR of 34.870 7 dB, and an MSE of 17.465 8. Experimental results show that the proposed model generates highly realistic CT images while preserving anatomical accuracy as much as possible. The transformed CT images can be effectively used in radiotherapy planning, further enhancing diagnostic efficiency.
Objective
To analyze and summarize advantages, disadvantages, and limitations of present imaging techniques in assessing efficacy of immunotherapy for patient with hepatocellular carcinoma in order to provide strong evidence for drug therapy evaluation and clinical decision-making for it.
Method
The relevant literatures about imaging evaluation of immunotherapy for hepatocellular carcinoma were collected to make a review, then analyze and summarize the value of different imaging techniques.
Results
The immunotherapy, characterized by the noninvasive, high specificity and good curative effect, had been playing an important role in the treatment of hepatocellular carcinoma in recent years. The imaging techniques currently used to assess the immunotherapy results of hepatocellular carinoma mainly included the CT, MRI, ultrasound, and nuclear medicine, which could be used to better evaluate the effectiveness of immunotherapy. It was very important for screening of the patients’ treatment options and judging of the survival and prognosis. The most of the evaluation indicators were based on the anatomic evaluation criteria and it had some certain limitations in evaluating the immunotherapy efficacy of patient with hepatocellular carcinoma.
Conclusions
As an emerging biological therapy, immunotherapy has gradually become a hotspot in diagnosis and treatment of hepatocellular carcinoma in future. Present imaging techniques and evaluation criteria have certain limitations. More multifunctional imaging indicators need to be improved and developed so as to provide strong evidence for drug therapy evaluation and clinical decision of patient with hepatocellular carcinoma.
