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Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an u...
This study investigated and compared the effects of Gd enhancement on brain tumours with a half-dose of contrast medium at 5.0 T and with a full dose at 3.0 T.
Fibrosis has important pathoetiological and prognostic roles in chronic liver disease. This study evaluates the role of radiomics in staging liver fibrosis.
Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important...
1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in...
Differentiating chronic total occlusion (CTO) from subtotal occlusion (SO) is often difficult to make from coronary computed tomography angiography (CCTA). We developed a CCTA-based radiomics model to differen...
The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such ...
Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent researc...
The original article was published in BMC Medical Imaging 2024 24:57
To exploit the improved prediction performance based on dynamic contrast-enhanced (DCE) MRI by using dynamic radiomics for microvascular invasion (MVI) in hepatocellular carcinoma (HCC).
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research int...
To investigate the feasibility of Diffusion Kurtosis Imaging (DKI) in assessing renal interstitial fibrosis induced by hyperuricemia.
To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) statu...
It is challenging to identify residual or recurrent fistulas from the surgical region, while MR imaging is feasible. The aim was to use dynamic contrast-enhanced MR imaging (DCE-MRI) technology to distinguish ...
Based on a longitudinal cohort design, the aim of this study was to investigate whether individual-based 18F fluorodeoxyglucose positron emission tomography (18F-FDG-PET) regional signals can predict dementia con...
The objective of this research was to create a deep learning network that utilizes multiscale images for the classification of follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) through pr...
The original article was published in BMC Medical Imaging 2022 22:200
Quantitative determination of the correlation between cognitive ability and functional biomarkers in the older brain is essential. To identify biomarkers associated with cognitive performance in the older, thi...
This study aims to analyze the characteristics of high frame rate contrast-enhanced ultrasound (H-CEUS) in renal lesions and to improve the ability for differential diagnosis of renal tumors.
Perfusion MRI is of great benefit in the post-treatment evaluation of brain tumors. Interestingly, dynamic susceptibility contrast-enhanced (DSC) perfusion has taken its place in routine examination for this p...
MRI has been widely used to predict the preoperative proliferative potential of pituitary adenoma (PA). However, the relationship between the cyst/tumor volume ratio (C/T ratio) and the proliferative potential...
Contrast-enhanced ultrasound (CEUS) is considered as an efficient tool for focal liver lesion characterization, given it allows real-time scanning and provides dynamic tissue perfusion information. An accurate...
Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gad...
To investigate the altered trends of regional homogeneity (ReHo) based on time and frequency, and clarify the time-frequency characteristics of ReHo in 48 classical trigeminal neuralgia (CTN) patients after a ...
To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model.
Medullary Thyroid Carcinoma (MTC) is a rare type of thyroid cancer. Accurate prediction of lateral cervical lymph node metastases (LCLNM) in MTC patients can help guide surgical decisions and ensure that patie...
Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, a...
Early diagnosis of osteoporosis is crucial to prevent osteoporotic vertebral fracture and complications of spine surgery. We aimed to conduct a hybrid transformer convolutional neural network (HTCNN)-based rad...
The paper aimed to improve the accuracy limitations of traditional two-dimensional ultrasound and surgical procedures in the diagnosis and management of congenital heart disease (chd), and to improve the diagn...
Traditional process for clinically significant prostate cancer (csPCA) diagnosis relies on invasive biopsy and may bring pain and complications. Radiomic features of magnetic resonance imaging MRI and methylat...
This study aims to classify tongue lesion types using tongue images utilizing Deep Convolutional Neural Networks (DCNNs).
MULTIPLEX is a single-scan three-dimensional multi-parametric MRI technique that provides 1 mm isotropic T1-, T2*-, proton density- and susceptibility-weighted images and the corresponding quantitative maps. T...
The morphological information of the pulmonary vein (PV) and left atrium (LA) is of immense clinical importance for effective atrial fibrillation ablation. The aim of this study is to examine the consistency i...
The Correction to this article has been published in BMC Medical Imaging 2024 24:81
This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in com...
Transcranial color-coded duplex ultrasonography (TCCD) is an important diagnostic tool in the investigation of cerebrovascular diseases. TCCD is often hampered by the temporal window that ultrasound cannot pen...
To introduce a three-dimensional convolutional neural network (3D CNN) leveraging transfer learning for fusing PET/CT images and clinical data to predict EGFR mutation status in lung adenocarcinoma (LADC).
The diameter of the ostial and proximal left main coronary artery can be greater than 5.0 mm. However, the diameters of the mostly available coronary drug-eluting stents (DESs) are ≤ 4.0 mm. Whether high-press...
This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the ...
Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limit...
Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) ...
Unenhanced abdominal CT constitutes the diagnostic standard of care in suspected urolithiasis. Aiming to identify potential for radiation dose reduction in this frequent imaging task, this experimental study c...
The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features.
Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a l...
To study the value of ultrasound in the diagnosis of juxtaglomerular cell tumor (JGCT).
Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of ...
To investigate whether CT-based radiomics can effectively differentiate between heterotopic pancreas (HP) and gastrointestinal stromal tumor (GIST), and whether different resampling methods can affect the mode...
A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mappi...
Parathyroid glands are important endocrine glands, and the identification of normal parathyroid glands is crucial for their protection. The aim of this study is to explore the sonographic characteristics of no...
18F-FDG brain PET is clinically used for differential diagnosis in cognitive dysfunction of unclear etiology and for exclusion of a neurodegenerative cause in patients with cognitive impairment in late-life psyc....
Both of extracellular extravascular volume (EEV) and extracellular volume fraction (ECV) were proposed to quantify enlargement of myocardial interstitial space due to myocardium loss or fibrosis. The study aim...
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
Usage 2023
Downloads: 951,496
Altmetric mentions: 161
The following summary describes the peer review process for this journal:
Identity transparency: Single anonymized
Reviewer interacts with: Editor
Review information published: Review reports. Reviewer Identities reviewer opt in. Author/reviewer communication