Liver shape analysis using statistical parametric maps at population scale
Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals.
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Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals.
Accurately distinguishing between invasive thymic epithelial tumors (TETs) and anterior mediastinal lymphoma before surgery is crucial for subsequent treatment choices. But currently, the diagnosis of invasive...
To investigate the role of CT radiomics in distinguishing Wilms tumor (WT) from clear cell sarcoma of the kidney (CCSK) in pediatric patients.
Lung cancer remains a leading cause of death among cancer patients. Computed tomography (CT) plays a key role in lung cancer screening. Previous studies have not adequately quantified the effect of scanning pr...
To validate the feasibility of water enema PET/CT (WE-PET/CT) in incidental colorectal 18F-FDG uptake and improve the accuracy of diagnosing colorectal neoplastic lesions.
Computed tomography angiography (CTA) and magnetic resonance angiography (MRA) provide accurate vascular imaging information, but their use may be contraindicated. Color Doppler ultrasonography (CDU) provides ...
Numerous previous studies have assessed the prognostic role of 18F-fluorodeoxyglucose positron-emission tomography (18F FDG PET) in patients with biliary tract cancer (BTC), but those results were inconsistent...
Atlantodental subluxation (ADS) is a serious condition that can result in sudden death. Measuring the anterior atlantodental interval (AADI method) is the gold standard for diagnosis but the complex anatomy of...
This study aimed to establish a predictive model to estimate the postoperative prognosis of patients with extrahepatic cholangiocarcinoma (ECC) based on preoperative clinical and MRI features.
In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial...
Convolutional neural network-based image processing research is actively being conducted for pathology image analysis. As a convolutional neural network model requires a large amount of image data for training...
Susac syndrome (SuS) is a rare autoimmune disease that leads to hearing impairment, visual field deficits, and encephalopathy due to an occlusion of precapillary arterioles in the brain, retina, and inner ear....
Glioblastoma with multiple foci (mGBM) and multiple brain metastases share several common features on magnetic resonance imaging (MRI). A reliable preoperative diagnosis would be of clinical relevance. The aim...
In some patients with nonischemic cardiomyopathy (NICM), left ventricular (LV) function improves with medical assistance, resulting in left ventricular reverse remodeling (LVRR). However, predictors of LVRR ar...
Deep learning is a highly significant technology in clinical treatment and diagnostics nowadays. Convolutional Neural Network (CNN) is a new idea in deep learning that is being used in the area of computer vis...
To conduct a systematic review looking into the possibility of US imaging to anticipate and identify future patellar or Achilles tendinopathy symptoms.
Due to the highly heterogeneity of the breast cancer, it would be desirable to obtain a non-invasive method to early predict the treatment response and survival outcome of the locally advanced breast cancer (L...
Retroperitoneal liposarcoma (RLPS) poses a challenging scenario for surgeons due to its unpredictable biological behavior. Surgery remains the primary curative option for RLPS; however, the need for additional...
To discuss the value of computed tomography (CT) iterative reconstruction technique combined with target scanning in the diagnosis of solid pseudopapillary tumor of the pancreas (SPTP).
To investigate the diagnostic value of computed tomography (CT) and magnetic resonance imaging (MRI) in ovarian malignant mesothelioma (OMM).
Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis.
This retrospective study aims to evaluate the diagnostic value of volume measurement of central pulmonary arteries using computer tomography pulmonary angiography (CTPA) for predicting pulmonary hypertension (...
Mutated KRAS may indicate an invasive nature and predict prognosis in locally advanced rectal cancer (LARC). We aimed to establish a radiomic model using pretreatment T2W MRIs to predict KRAS status and explor...
Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray.
The gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based ...
The purpose of this study was to investigate the clinical utility of three-dimension (3D) high-resolution inversion recovery (IR)-prepared fast spoiled gradient-recalled (SPGR) magnetic resonance imaging (MRI)...
We aimed to evaluate the added value of inversion imaging in differentiating between benign and malignant breast masses when combined with the Breast Imaging Reporting and Data System (BI-RADS).
Prostate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distingu...
This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Pa...
This study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high-resolution isotropic 3D T1-Gd Magnetic resonance imaging (M...
To summarize our single-center experience with percutaneous ultrasound (US)-guided radiofrequency ablation (RFA) for pediatric recurrent hepatocellular carcinoma (RHCC).
The role of threshold growth, as one of the major features (MFs) of hepatocellular carcinoma (HCC) in the Liver Imaging Reporting and Data System (LI-RADS) is inconsistent. This study evaluated the LI-RADS dia...
Deep learning has been used to detect or characterize prostate cancer (PCa) on medical images. The present study was designed to develop an integrated transfer learning nomogram (TLN) for the prediction of PCa...
Chest radiography (CXR) is an adjunct tool in treatment planning and monitoring of the disease course of COVID-19 pneumonia. The purpose of the study was to describe the radiographic patterns and severity scor...
Maxillary morphology has long been a subject of interest due to its possible impact on palatally and labially displaced canines. This study aims to conduct a comparison of the palate morphology between individ...
In recent years, there has been a growing trend towards utilizing Artificial Intelligence (AI) and machine learning techniques in medical imaging, including for the purpose of automating quality assurance. In ...
To develop a deep learning (DL) model to measure the sagittal Cobb angle of the cervical spine on computed tomography (CT).
The purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach call...
To evaluate the repeatability and agreement of Fourier-domain optical coherence tomography (AOCT-1000 M and RTVue XR) and partial coherence interferometry biometer (IOL Master 500) in measuring corneal thickne...
2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography (18F-FDG PET) could help evaluate metabolic abnormalities by semi-quantitative measurement to identify autoimmune encephalitis (AE). Few studies hav...
Due to the lack of corresponding clinical symptoms, small calcified gastric gastrointestinal stromal tumors (GISTs) are often overlooked in clinical practice. Therefore, there is an unmet need to define the im...
There are no specific magnetic resonance imaging (MRI) features that distinguish pilocytic astrocytoma (PA) from adamantinomatous craniopharyngioma (ACP). In this study we compared the frequency of a novel enh...
This study aimed to compare the diagnostic efficiency of Ovarian-Adnexal Reporting and Data System (O-RADS) and doctors’ subjective judgment in diagnosing the malignancy risk of adnexal masses.
Since lung tumors are in dynamic conditions, the study of tumor growth and its changes is of great importance in primary diagnosis.
Although it is generally thought that disturbance of perfusion in the anterior lobe of the pituitary gland leads to complete or partial hypopituitarism, the gadolinium (Gd) enhancement findings on Magnetic Res...
Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its ...
Renal cell carcinoma (RCC) is a heterogeneous group of cancers. The collagen fiber content in the tumor microenvironment of renal cancer has an important role in tumor progression and prognosis. A radiomics mo...
1H magnetic resonance spectroscopy (1H-MRS) can be used to study neurological disorders because it can be utilized to examine the concentrations of related metabolites. However, the diagnostic utility of differen...
To explore the value of magnetic resonance angiography (MRA) and high resolution magnetic resonance vessel wall imaging (HRMR-VWI) in cervicocranial artery dissection (CCAD) for the disease diagnosis, course s...
There is a lack of understanding of the mechanisms by which the CNS is injured in multiple sclerosis (MS). Since Theiler’s murine encephalomyelitis virus (TMEV) infection in SJL/J mice is an established model ...
Citation Impact 2023
Journal Impact Factor: 2.9
5-year Journal Impact Factor: 2.8
Source Normalized Impact per Paper (SNIP): 1.157
SCImago Journal Rank (SJR): 0.701
Speed 2023
Submission to first editorial decision (median days): 13
Submission to acceptance (median days): 177
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