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The study aimed to investigate the predictive value of delta-radiomics derived from computed tomography (CT) for pathological complete response (pCR) to neoadjuvant immunochemotherapy (NICT) among patients wit...
Evaluating myocardial function using cardiac magnetic resonance (CMR) feature tracking provides a comprehensive cardiac assessment, particularly a detailed evaluation for patients with repaired tetralogy of Fa...
Ductal carcinoma in situ (DCIS) of the breast has a wide disease spectrum with risks of progression to invasive cancer linked to pathological factors. High-grade histology, large tumor volume, and comedonecros...
Myocardial infarction (MI) is a life-threatening medical condition that necessitates both timely and precise diagnosis. The enhancement of automated method to detect MI diseases from Normal patients can play a...
We aimed to quantitatively analyze the perfusion characteristics of pancreatic neuroendocrine tumors (pNETs) utilizing dual-source CT imaging.
Microvascular ultrasound imaging techniques such as Angio PLanewave UltraSensitive (Angio-PL.U.S.) have been used to detect microvascular blood flow in various organs and tissues. However, the advantage of Ang...
This study evaluated the impact of contrast-enhanced ultrasonography (CEUS) combined with CT or MRI fusion imaging on percutaneous radiofrequency ablation (RFA) outcomes for hepatocellular carcinoma (HCC) inco...
Lymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic featu...
This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity.
A deep learning (DL) model that can automatically detect and classify cervical canal and neural foraminal stenosis using cervical spine magnetic resonance imaging (MRI) can improve diagnostic accuracy and effi...
This study aimed to perform a comprehensive morphometric analysis of the epiglottis using cone-beam computed tomography (CBCT) images, including the determination of epiglottis dimensions, the investigation of...
Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relie...
Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. This study aims to develop and evalua...
The treatment of preinvasive lesions is more effective than treating invasive disease, hence detecting cancer at its early stages is crucial. However, currently, available screening methods show various limita...
Ovarian cancer remains a leading cause of death among women, largely due to its asymptomatic early stages and high mortality when diagnosed late. Early detection significantly improves survival rates, and the ...
In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyroid nodules due to its high real-time capabilities and low ...
Esophageal fistula (EF), a rare and potentially fatal complication, can be better managed with predictive models for personalized treatment plans in esophageal cancers. We aim to develop a clinical-deep learni...
Tumor bed (TB) is the residual cavity of resected tumor after surgery. Delineating TB from CT is crucial in generating clinical target volume for radiotherapy. Due to multiple surgical effects and low image co...
Thyroid nodules diagnosed in children pose a greater risk of malignancy compared to those in adults. However, there is no ultrasound thyroid nodule evaluation system aimed at children. The objective of this re...
Systemic immunity is essential for driving therapeutically induced antitumor immune responses, and the spleen may reflect alterations in systemic immunity. This study aimed to evaluate the predictive value of ...
3D brachial plexus MRI scanning is prone to examination failure due to the lengthy scan times, which can lead to patient discomfort and motion artifacts. Our purpose is to investigate the efficacy of artificia...
T1 mapping can quantify the longitudinal relaxation time of tissues. This study aimed to investigate the repeatability and reproducibility of T1 mapping radiomics features of lung cancer and the feasibility of...
To explore the value of computed tomography based radiomics in the differential diagnosis of drug-sensitive and drug-resistant pulmonary tuberculosis.
The early detection and diagnosis of gastrointestinal tract diseases, such as ulcerative colitis, polyps, and esophagitis, are crucial for timely treatment. Traditional imaging techniques often rely on manual ...
Speckle tracking echocardiography (STE) provides quantification of left ventricular (LV) deformation and is useful in the assessment of LV function. STE is increasingly being used clinically, and every effort ...
To compare the performance of whole tumor and habitats-based computed tomography (CT) radiomics for predicting immunophenotyping in laryngeal squamous cell carcinomas (LSCC) and further evaluate the stratified...
This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI).
Crohn’s disease is a severe chronic and relapsing inflammatory bowel disease. Although contrast-enhanced computed tomography enterography is commonly used to evaluate crohn’s disease, its imaging findings are ...
The original article was published in BMC Medical Imaging 2024 24:288
Decision-making in the management of Crohn’s disease (CD)-related spontaneous intra-abdominal abscess (IAA) is challenging. This study aims to reveal predictive factors for percutaneous drainage and/or surgery...
Follicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland and has a greater propensity for haematogenous metastasis. However, the preoperative differentiation of FTC from follicu...
To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or sem...
Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural vie...
Although there is a high incidence of hematogenous infections in melioidosis, a tropical infectious disease, there are few systematic analyses of hematogenous melioidosis in imaging articles. A comprehensive c...
Preoperative evaluation of inferior vena cava (IVC) wall invasion is very important to improve outcomes of patients with renal cell carcinoma (RCC), and may allow surgical urologists to treat the IVC more effe...
We explored whether the feature aggregation and refinement network (FARNet) algorithm accurately identified posteroanterior (PA) cephalometric landmarks.
This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtu...
Rebleeding after endoscopic treatment for esophagogastric varices (EGVs) in cirrhotic patients remains a significant clinical challenge, with high mortality rates and limited predictive tools. Current methods,...
This study aims to investigate the risk factors for lymph node metastasis (LNM) in synchronous multiple primary lung cancer (sMPLC) using clinical and CT features, and to offer guidance for preoperative LNM pr...
Multiple models intravoxel incoherent motion (IVIM) based 18F-fluorodeoxyglucose positron emission tomography-magnetic resonance(18F-FDG PET/MR) could reflect the microscopic information of the tumor from multipl...
To construct and compare merged models integrating clinical factors, MRI-based radiomics features and deep learning (DL) models for predicting pathological complete response (pCR) to neoadjuvant chemoradiother...
This study aims to assess the consistency of various CT-FFR software, to determine the reliability of current CT-FFR software, and to measure relevant influence factors. The goal is to build a solid foundatio...
The Correction to this article has been published in BMC Medical Imaging 2024 24:301
The original article was published in BMC Medical Imaging 2024 24:274
This study aimed to conduct a systematic review and meta-analysis to summarize the available evidence comparing the diagnostic accuracy of periapical radiography (PA) and cone-beam computed tomography (CBCT) f...
Automatic diagnosis and brain hemorrhage segmentation in Computed Tomography (CT) may be helpful in assisting the neurosurgeon in developing treatment plans that improve the patient’s chances of survival. Beca...
Conventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased ...
The increasing use of CT imaging in emergency departments, despite efforts of reducing low-value imaging, is not fully understood, especially during and after the COVID-19 pandemic. The aim of this study was t...
It is challenging to correctly identify and diagnose breast nonmass lesions. This study aimed to explore the multimodal ultrasound features associated with malignant breast nonmass lesions (NMLs), and evaluate...
The objective of this study was to evaluate the feasibility of using Artificial Intelligence (AI) to measure the long-diameter of tumors for evaluating treatment response.
To evaluate the effect of the contrast-enhancement-boost (CE-boost) postprocessing technique on improving the image quality of obese patients in computed tomography pulmonary angiography (CTPA) compared to hyb...
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 2024
Submission to first editorial decision (median days): 13
Submission to acceptance (median days): 127
Usage 2024
Downloads: 1,190,622
Altmetric mentions: 86
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