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Altered trends of local brain function in classical trigeminal neuralgia patients after a single trigger pain BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-18 Juncheng Yan, Luoyu Wang, Lei Pan, Haiqi Ye, Xiaofen Zhu, Qi Feng, Haibin Wang, Zhongxiang Ding, Xiuhong Ge
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 single pain stimulate. All patients underwent three times resting-state functional MRI (before stimulation (baseline), after stimulation within 5 s (triggering-5 s), and in the 30th min of stimulation
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Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-18 Qiong Qin, Xiangyu Gan, Peng Lin, Jingshu Pang, Ruizhi Gao, Rong Wen, Dun Liu, Quanquan Tang, Changwen Liu, Yun He, Hong Yang, Yuquan Wu
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. This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training
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Combining radiomics with thyroid imaging reporting and data system to predict lateral cervical lymph node metastases in medullary thyroid cancer BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-18 Zhiqiang Liu, Xiwei Zhang, Xiaohui Zhao, Qianqian Guo, Zhengjiang Li, Minghui Wei, Lijuan Niu, Changming An
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 patients receive the most appropriate and effective surgery. To our knowledge, no studies have been published that use radiomics analysis to forecast LCLNM in MTC patients. The purpose of this study
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Unified deep learning models for enhanced lung cancer prediction with ResNet-50–101 and EfficientNet-B3 using DICOM images BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-18 Vinod Kumar, Chander Prabha, Preeti Sharma, Nitin Mittal, S. S. Askar, Mohamed Abouhawwash
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, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and
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Hybrid transformer convolutional neural network-based radiomics models for osteoporosis screening in routine CT BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-14 Jiachen Liu, Huan Wang, Xiuqi Shan, Lei Zhang, Shaoqian Cui, Zelin Shi, Yunpeng Liu, Yingdi Zhang, Lanbo Wang
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 radiomics model for osteoporosis screening in routine CT. To investigate the HTCNN algorithm for vertebrae and trabecular segmentation, 92 training subjects and 45 test subjects were employed. Furthermore
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Improving the diagnosis and treatment of congenital heart disease through the combination of three-dimensional echocardiography and image guided surgery BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-13 Yong Jiang
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 diagnostic and therapeutic level of chd. This article first collected patient data through real-time imaging and body surface probes, and then diagnosed 150 patients using three-dimensional echocardiography
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A noninvasive method for predicting clinically significant prostate cancer using magnetic resonance imaging combined with PRKY promoter methylation level: a machine learning study BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-11 Yufei Wang, Weifeng Liu, Zeyu Chen, Yachen Zang, Lijun Xu, Zheng Dai, Yibin Zhou, Jin Zhu
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 methylation of the PRKY promoter were found to be associated with prostate cancer. Fifty-four Patients who underwent prostate biopsy or photoselective vaporization of the prostate (PVP) from 2022 to 2023
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Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-08 Burcu Tiryaki, Kubra Torenek-Agirman, Ozkan Miloglu, Berfin Korkmaz, İbrahim Yucel Ozbek, Emin Argun Oral
This study aims to classify tongue lesion types using tongue images utilizing Deep Convolutional Neural Networks (DCNNs). A dataset consisting of five classes, four tongue lesion classes (coated, geographical, fissured tongue, and median rhomboid glossitis), and one healthy/normal tongue class, was constructed using tongue images of 623 patients who were admitted to our clinic. Classification performance
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A rapid multi-parametric quantitative MR imaging method to assess Parkinson’s disease: a feasibility study BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-05 Min Duan, Rongrong Pan, Qing Gao, Xinying Wu, Hai Lin, Jianmin Yuan, Yamei Zhang, Lindong Liu, Youyong Tian, Tong Fu
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. This study aimed to investigate its feasibility of clinical application in Parkinson’s disease (PD). 27 PD patients and 23 healthy control (HC) were recruited and underwent a MULTIPLEX scanning
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Evaluating the consistency in different methods for measuring left atrium diameters BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-05 Jun-Yan Yue, Kai Ji, Hai-Peng Liu, Qing-Wu Wu, Chang-Hua Liang, Jian-Bo Gao
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 in different LA diameter measurement techniques. Retrospective imaging data from 87 patients diagnosed with PV computed tomography angiography were included. The patients consisted of 50 males
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Deep learning–based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-05 Liping Yang, Tianzuo Wang, Jinling Zhang, Shi Kang, Shichuan Xu, Kezheng Wang
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 comparison to manual segmentations. Another purpose of our work was to develop a radiomics model based on the radiomics features extracted from automatic segmentation to differentiate low- and high-grade
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Development of a prediction model for facilitating the clinical application of transcranial color-coded duplex ultrasonography BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-05 Jieyu Duan, Pengfei Wang, Haoyu Wang, Wei Zhao
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 penetrate. Rapidly determine whether ultrasound can penetrate the temporal window in order to determine whether to use other acoustic windows to complete the examination process. In this study, Skull
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Transfer learning–based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-04 Xiaonan Shao, Xinyu Ge, Jianxiong Gao, Rong Niu, Yunmei Shi, Xiaoliang Shao, Zhenxing Jiang, Renyuan Li, Yuetao Wang
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). Retrospective data from 516 LADC patients, encompassing preoperative PET/CT images, clinical information, and EGFR mutation status, were divided into training (n = 404) and test sets (n = 112). Several
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Evaluation of post-dilatation on longitudinal stent deformation and postprocedural stent malapposition in the left main artery by optical coherence tomography (OCT): an in vitro study BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-01 Qing He, Yuqi Fan, Zuojun Xu, Junfeng Zhang
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-pressure dilatation can increase the diameter of stents from 4.0 to 5.0 mm and whether post-dilatation leads to longitudinal stent deformation (LSD) of 4.0-mm-diameter stents have rarely been studied
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Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-01 Jacqueline Matthew, Alena Uus, Leah De Souza, Robert Wright, Abi Fukami-Gartner, Gema Priego, Carlo Saija, Maria Deprez, Alexia Egloff Collado, Jana Hutter, Lisa Story, Christina Malamateniou, Kawal Rhode, Jo Hajnal, Mary A. Rutherford
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 first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models
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PulmoNet: a novel deep learning based pulmonary diseases detection model BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-28 AbdulRahman Tosho Abdulahi, Roseline Oluwaseun Ogundokun, Ajiboye Raimot Adenike, Mohd Asif Shah, Yusuf Kola Ahmed
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-limiting such as the common cold and catarrh, to life-threatening ones, such as viral pneumonia (VP), bacterial pneumonia (BP), and tuberculosis, as well as a severe acute respiratory syndrome, such
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Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-27 Minyue Yin, Chao Xu, Jinzhou Zhu, Yuhan Xue, Yijia Zhou, Yu He, Jiaxi Lin, Lu Liu, Jingwen Gao, Xiaolin Liu, Dan Shen, Cuiping Fu
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) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Asymptomatic carriers were from Yangzhou Third People’s Hospital from August 1st, 2020
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Influence of spectral shaping and tube voltage modulation in ultralow-dose computed tomography of the abdomen BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-23 Philipp Feldle, Jan-Peter Grunz, Andreas Steven Kunz, Pauline Pannenbecker, Theresa Sophie Patzer, Svenja Pichlmeier, Stephanie Tina Sauer, Robin Hendel, Süleyman Ergün, Thorsten Alexander Bley, Henner Huflage
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 compares the effect of spectral shaping and tube voltage modulation on image quality. Using a third-generation dual-source CT, eight cadaveric specimens were scanned with varying tube voltage settings
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Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-19 Xiaodan Chen, Qingyong Guo, Xiaorong Chen, Wanjing Zheng, Yaqing Kang, Dairong Cao
The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features. We retrospectively evaluated clinical and MRI findings in 17 patients with UCS and 34 patients with EAC proven by histologically. The following clinical and pathological features were evaluated: post- or pre-menopausal, clinical
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MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-19 Wei Li, Jiaye Liu, Shanshan Wang, Chaolu Feng
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 long scanning time, which causes patients’ discomfort in the scanning process. Therefore, to reduce the time, the multi temporal feature fusing neural network with Co-attention (MTFN) is proposed
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How to identify juxtaglomerular cell tumor by ultrasound: a case series and review of the literature BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-16 Li Wang, Meiying Li, Siqi Jin, Yunshu Ouyang, Fenglan Wang, Ke Lv, Jianchu Li, Yuxin Jiang, He Liu, Qingli Zhu
To study the value of ultrasound in the diagnosis of juxtaglomerular cell tumor (JGCT). From January 2005 to July 2020, fifteen patients diagnosed as JGCT by surgical pathology in Peking Union Medical College Hospital were collected. All patients underwent preoperative ultrasound examination. The clinical, laboratory, ultrasound, computed tomography (CT), surgical, and pathological features of the
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CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-15 Jiexiao Wang, Jialiang Wang, Xiang Huang, Yanfei Zhou, Jian Qi, Xiaojun Sun, Jinfu Nie, Zongtao Hu, Shujie Wang, Bo Hong, Hongzhi Wang
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 advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC.
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Development of a multi-phase CT-based radiomics model to differentiate heterotopic pancreas from gastrointestinal stromal tumor BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-14 Kui Sun, Shuxia Yu, Ying Wang, Rongze Jia, Rongchao Shi, Changhu Liang, Ximing Wang, Haiyan Wang
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 model’s performance. Multi-phase CT radiological data were retrospectively collected from 94 patients. Of these, 40 with HP and 54 with GISTs were enrolled between April 2017 and November 2021. One
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Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-13 Aleksandra M. Paciorek, Claudio E. von Schacky, Sarah C. Foreman, Felix G. Gassert, Florian T. Gassert, Jan S. Kirschke, Karl-Ludwig Laugwitz, Tobias Geith, Martin Hadamitzky, Jonathan Nadjiri
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-mapping and late gadolinium phase sensitive inversion recovery (PSIR) sequences were used. Subjects in this study were either diagnosed with cardiac pathology (n = 137) including acute and chronic
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Ultrasound characteristics of normal parathyroid glands and analysis of the factors affecting their display BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-13 Cuiping Wu, Binyang Zhu, Song Kang, Shiyu Wang, Yingying Liu, Xue Mei, He Zhang, Shuangquan Jiang
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 normal parathyroid glands and analyze the factors affecting their display. Seven hundred three subjects who underwent physical examination at our hospital were included. The number, location, size
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In vivo PET of synaptic density as potential diagnostic marker for cognitive disorders: prospective comparison with current imaging markers for neuronal dysfunction and relation to symptomatology - study protocol BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-12 Greet Vanderlinden, Charles Carron, Rik Vandenberghe, Mathieu Vandenbulcke, Koen Van Laere
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 psychiatric disorders. 18F-FDG PET measures regional glucose metabolism, which represents a combination of neuronal/synaptic activity but also astrocytic activity and neuroinflammation. Recently
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Quantitating myocardial fibrosis using extracellular extravascular volume determined from computed tomography myocardial perfusion imaging BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-12 Na Li, Xin Zhang, Jin Gu, Ming Yang, Lina Chen, Jie Yu, Heshui Shi
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 aimed to investigate the feasibility of using EEV derived from myocardial computed tomography (CT) perfusion imaging (VPCT) and extracellular volume quantification with single-energy subtraction
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Diagnostic performance of coronary computed tomography angiography stenosis score for coronary stenosis BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-09 Qing-feng Xiong, Xiao-rong Fu, Lei-zhi Ku, Di Zhou, Sheng-peng Guo, Wen-sheng Zhang
Coronary computed tomography angiography stenosis score (CCTA-SS) is a proposed diagnosis score that considers the plaque characteristics, myocardial function, and the diameter reduction rate of the lesions. This study aimed to evaluate the diagnostic performance of the CCTA-SS in seeking coronary artery disease (CAD). The 228 patients with suspected CAD who underwent CCTA and invasive coronary angiography
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Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-08 Saravanan Srinivasan, Kirubha Durairaju, K. Deeba, Sandeep Kumar Mathivanan, P. Karthikeyan, Mohd Asif Shah
Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall
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Liver fat volume fraction measurements based on multi-material decomposition algorithm in patients with nonalcoholic fatty liver disease: the influences of blood vessel, location, and iodine contrast BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-07 Liuhong Zhu, Funan Wang, Heqing Wang, Jinhui Zhang, Anjie Xie, Jinkui Pei, Jianjun Zhou, Hao Liu
In recent years, spectral CT-derived liver fat quantification method named multi-material decomposition (MMD) is playing an increasingly important role as an imaging biomarker of hepatic steatosis. However, there are various measurement ways with various results among different researches, and the impact of measurement methods on the research results is unknown. The aim of this study is to evaluate
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Automated classification of liver fibrosis stages using ultrasound imaging BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-06 Hyun-Cheol Park, YunSang Joo, O-Joun Lee, Kunkyu Lee, Tai-Kyong Song, Chang Choi, Moon Hyung Choi, Changhan Yoon
Ultrasound imaging is the most frequently performed for the patients with chronic hepatitis or liver cirrhosis. However, ultrasound imaging is highly operator dependent and interpretation of ultrasound images is subjective, thus well-trained radiologist is required for evaluation. Automated classification of liver fibrosis could alleviate the shortage of skilled radiologist especially in low-to-middle
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EOS® is reliable to evaluate spinopelvic parameters: a validation study BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-06 Mohammadreza Shakeri, Seyed Mani Mahdavi, Masih Rikhtehgar, Mohammad Soleimani, Hasan Ghandhari, Behnam Jafari, Seyedehsan Daneshmand
Sagittal and coronal standing radiographs have been the standard imaging for assessing spinal alignment. However, their disadvantages include distortion at the image edges and low interobserver reliability in some parameters. EOS® is a low-dose biplanar digital radiographic imaging system that can avoid distortion by obtaining high-definition images. This study aimed to evaluate spinopelvic parameters
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Artifact suppression for breast specimen imaging in micro CBCT using deep learning BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-06 Sorapong Aootaphao, Puttisak Puttawibul, Pairash Thajchayapong, Saowapak S. Thongvigitmanee
Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen
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Machine learning-based MRI radiomics for assessing the level of tumor infiltrating lymphocytes in oral tongue squamous cell carcinoma: a pilot study BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-05 Jiliang Ren, Gongxin Yang, Yang Song, Chunye Zhang, Ying Yuan
To investigate the value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in assessing tumor-infiltrating lymphocyte (TIL) levels in patients with oral tongue squamous cell carcinoma (OTSCC). The study included 68 patients with pathologically diagnosed OTSCC (30 with high TILs and 38 with low TILs) who underwent pretreatment MRI. Based on the regions of interest encompassing
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Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-05 B. Uma Maheswari, Dahlia Sam, Nitin Mittal, Abhishek Sharma, Sandeep Kaur, S. S. Askar, Mohamed Abouhawwash
Chest radiographs are examined in typical clinical settings by competent physicians for tuberculosis diagnosis. However, this procedure is time consuming and subjective. Due to the growing usage of machine learning techniques in applied sciences, researchers have begun applying comparable concepts to medical diagnostics, such as tuberculosis screening. In the period of extremely deep neural nets which
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Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-02 Hua Qian, Xiaojing Ren, Maosheng Xu, Zhen Fang, Ruixin Zhang, Yangyang Bu, Changyu Zhou
The tumor immune microenvironment is a valuable source of information for predicting prognosis in breast cancer (BRCA) patients. To identify immune cells associated with BRCA patient prognosis from the Cancer Genetic Atlas (TCGA), we established an MRI-based radiomics model for evaluating the degree of immune cell infiltration in breast cancer patients. CIBERSORT was utilized to evaluate the degree
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A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review BMC Med. Imaging (IF 2.7) Pub Date : 2024-02-01 Sunil Kumar, Harish Kumar, Gyanendra Kumar, Shailendra Pratap Singh, Anchit Bijalwan, Manoj Diwakar
Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET)
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Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-27 Yuhui Deng, Dawei Yang, Xianzheng Tan, Hui Xu, Lixue Xu, Ahong Ren, Peng Liu, Zhenghan Yang
To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre. A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images)
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Performance of node reporting and data system (node-RADS): a preliminary study in cervical cancer BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-26 Qingxia Wu, Jianghua Lou, Jinjin Liu, Linxiao Dong, Qingxia Wu, Yaping Wu, Xuan Yu, Meiyun Wang
Node Reporting and Data System (Node-RADS) was proposed and can be applied to lymph nodes (LNs) across all anatomical sites. This study aimed to investigate the diagnostic performance of Node-RADS in cervical cancer patients. A total of 81 cervical cancer patients treated with radical hysterectomy and LN dissection were retrospectively enrolled. Node-RADS evaluations were performed by two radiologists
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A gadoxetic acid-enhanced MRI-based model using LI-RADS v2018 features for preoperatively predicting Ki-67 expression in hepatocellular carcinoma BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-25 Yingying Liang, Fan Xu, Qiuju Mou, Zihua Wang, Chuyin Xiao, Tingwen Zhou, Nianru Zhang, Jing Yang, Hongzhen Wu
To construct a gadoxetic acid-enhanced MRI (EOB-MRI) -based multivariable model to predict Ki-67 expression levels in hepatocellular carcinoma (HCC) using LI-RADS v2018 imaging features. A total of 121 patients with HCC who underwent EOB-MRI were enrolled in this study. The patients were divided into three groups according to Ki-67 cut-offs: Ki-67 ≥ 20% (n = 86) vs. Ki-67 < 20% (n = 35); Ki-67 ≥ 30%
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Application of contrast-enhanced ultrasound in diagnosis and grading of bladder urothelial carcinoma BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-25 Hui-ping Zhang, Rong-xi Liang, Xue-ying Lin, En-sheng Xue, Qin Ye, Yi-fan Zhu
To explore the application of contrast-enhanced ultrasound (CEUS) for the diagnosis and grading of bladder urothelial carcinoma (BUC). The results of a two-dimensional ultrasound, color Doppler ultrasound and CEUS, were analyzed in 173 bladder lesion cases. The ultrasound and surgical pathology results were compared, and their diagnostic efficacy was analyzed. There were statistically significant differences
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Deep learning in computed tomography to predict endotype in chronic rhinosinusitis with nasal polyps BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-24 Weidong Du, Weipiao Kang, Shixin Lai, Zehong Cai, Yaowen Chen, Xiaolei Zhang, Yu Lin
As treatment strategies differ according to endotype, rhinologists must accurately determine the endotype in patients affected by chronic rhinosinusitis with nasal polyps (CRSwNP) for the appropriate management. In this study, we aim to construct a novel deep learning model using paranasal sinus computed tomography (CT) to predict the endotype in patients with CRSwNP. We included patients diagnosed
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Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-24 Yifeng Peng, Haijun Deng
With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing
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Elective one-minute full brain multi-contrast MRI versus brain CT in pediatric patients: a prospective feasibility study BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-24 Francesca De Luca, Annika Kits, Daniel Martin Muñoz, Åsa Aspelin, Ola Kvist, Yords Österman, Sandra Diaz Ruiz, Stefan Skare, Anna Falk Delgado
Brain CT can be used to evaluate pediatric patients with suspicion of cerebral pathology when anesthetic and MRI resources are scarce. This study aimed to assess if pediatric patients referred for an elective brain CT could endure a diagnostic fast brain MRI without general anesthesia using a one-minute multi-contrast EPI-based sequence (EPIMix) with comparable diagnostic performance. Pediatric patients
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Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-20 Guangying Zheng, Jie Hou, Zhenyu Shu, Jiaxuan Peng, Lu Han, Zhongyu Yuan, Xiaodong He, Xiangyang Gong
Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics
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A hybrid deep CNN model for brain tumor image multi-classification BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-19 Saravanan Srinivasan, Divya Francis, Sandeep Kumar Mathivanan, Hariharan Rajadurai, Basu Dev Shivahare, Mohd Asif Shah
The current approach to diagnosing and classifying brain tumors relies on the histological evaluation of biopsy samples, which is invasive, time-consuming, and susceptible to manual errors. These limitations underscore the pressing need for a fully automated, deep-learning-based multi-classification system for brain malignancies. This article aims to leverage a deep convolutional neural network (CNN)
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Study on the diagnostic value of MDCT extramural vascular invasion in preoperative N staging of gastric cancer patients BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-19 Zhengqi Zhu, Mimi Mao, Anyi Song, Haipeng Gong, Jianan Gu, Yongfeng Dai, Feng Feng
To explore the diagnostic value of multidetector computed tomography (MDCT) extramural vascular invasion (EMVI) in preoperative N Staging of gastric cancer patients. According to the MR-defined EMVI scoring standard of rectal cancer, we developed a 5-point scale scoring system to evaluate the status of CT-detected extramural vascular invasion(ctEMVI), 0–2 points were ctEMVI-negative status, and 3–4
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Recognition of eye diseases based on deep neural networks for transfer learning and improved D-S evidence theory BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-18 Fanyu Du, Lishuai Zhao, Hui Luo, Qijia Xing, Jun Wu, Yuanzhong Zhu, Wansong Xu, Wenjing He, Jianfang Wu
Human vision has inspired significant advancements in computer vision, yet the human eye is prone to various silent eye diseases. With the advent of deep learning, computer vision for detecting human eye diseases has gained prominence, but most studies have focused only on a limited number of eye diseases. Our model demonstrated a reduction in inherent bias and enhanced robustness. The fused network
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Value of blood oxygenation level-dependent magnetic resonance imaging in early evaluation of the response and prognosis of esophageal squamous cell carcinoma treated with definitive chemoradiotherapy: a preliminary study BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-12 Huanhuan Zheng, Hailong Zhang, Yan Zhu, Xiaolei Wei, Song Liu, Wei Ren
To find a useful hypoxia non-invasive biomarker for evaluating early treatment response and prognosis to definitive chemoradiotherapy (dCRT) in patients with esophageal squamous cell carcinoma (ESCC), using blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI). The R2* values were obtained pre- and 2–3 weeks post-dCRT in 28 patients with ESCC using BOLD MRI. Independent samples
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SEA-NET: medical image segmentation network based on spiral squeeze-and-excitation and attention modules BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-11 Liangli Xiong, Chen Yi, Qiliang Xiong, Shaofeng Jiang
Medical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are required for them. The current deep neural network based medical segmentation methods have good effect on image with balanced foreground and background, but it will loss the characteristics of small targets on image with imbalanced foreground and background after multiple
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T1 mapping as a quantitative imaging biomarker for diagnosing cervical cancer: a comparison with diffusion kurtosis imaging BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-10 Zanxia Zhang, Jie Liu, Yong Zhang, Feifei Qu, Robert Grimm, Jingliang Cheng, Weijian Wang, Jinxia Zhu, Shujian Li
T1 mapping can potentially quantitatively assess the intrinsic properties of tumors. This study was conducted to explore the ability of T1 mapping in distinguishing cervical cancer type, grade, and stage and compare the diagnostic performance of T1 mapping with diffusion kurtosis imaging (DKI). One hundred fifty-seven patients with pathologically confirmed cervical cancer were enrolled in this prospectively
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Liver shape analysis using statistical parametric maps at population scale BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-09 Marjola Thanaj, Nicolas Basty, Madeleine Cule, Elena P. Sorokin, Brandon Whitcher, Jimmy D. Bell, E. Louise Thomas
Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and
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A diagnostic model based on 18F-FDG PET/CT parameters in improving the differential diagnosis of invasive thymic epithelial tumors and anterior mediastinal lymphomas BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-08 Shuo Zhou, Pokwan Tsui, Meifu Lin, Guobao Chen, Wenxin Chen, Xiangran Cai
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 TET is sometimes difficult to distinguish from anterior mediastinal lymphoma. To assess the application of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography/computer tomography
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CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-05 Yaxin Deng, Haoru Wang, Ling He
To investigate the role of CT radiomics in distinguishing Wilms tumor (WT) from clear cell sarcoma of the kidney (CCSK) in pediatric patients. We retrospectively enrolled 83 cases of WT and 33 cases of CCSK. These cases were randomly stratified into a training set (n = 81) and a test set (n = 35). Several imaging features from the nephrographic phase were analyzed, including the maximum tumor diameter
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Assessing the effect of scanning parameter on the size and density of pulmonary nodules: a phantom study BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-05 Donghua Meng, Zhen Wang, Changsen Bai, Zhaoxiang Ye, Zhipeng Gao
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 protocols on the detected tumor size. The aim of this study was to assess the effect of various CT scanning parameters on tumor size and densitometry based on a phantom study and to investigate
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Assessment of water enema PET/CT: an effective imaging technique for the diagnosis of incidental colorectal 18F-FDG uptake BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-03 Rongqin Zhang, Meilinuer Abudurexiti, Wanglin Qiu, Pinbo Huang, Ping Hu, Wei Fan, Zhanwen Zhang
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. We retrospectively analysed the electronic records of 338 patients undergoing common PET/CT and WE-PET/CT at our hospital. PET/CT results were correlated with colonoscopy pathology and follow-up results. The ROC contrast curve was
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The value of color doppler ultrasonography combined with computed tomography angiography and magnetic resonance angiography in the preoperative quantification and classification of carotid body tumors: a retrospective analysis BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-03 Li Zhiqiang, Wang Yihua, Fu Ying, Zhu Shiwei, Zeng Xiangzhu, Cui Ligang
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 simple, safe, noninvasive, and reproducible imaging. We therefore investigated the role of preoperative CDU combined with CTA and MRA in the quantification, typing, and diagnosis of carotid body
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Prognostic significance of 18F-Fluorodeoxyglucose positron-emission tomography parameters in patients with biliary tract cancers: a meta-analysis BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-02 Xia Zheng, Yue Shi, Delida Kulabieke, Zihao Wang, Ying Cheng, Jun Qian
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. The present study aims to determine the predictive value of 18F FDG PET in BTC patients via a meta-analysis. The underlying studies related to 18F FDG PET and BTC patients` outcomes were searched
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Comparison of the diagnostic performance of the Swischuk line method and the anterior atlantodental interval method in atlantodental subluxation BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-02 Eun Ji Lee, Yeo Ju Kim, Sung Oh Song, Seunghun Lee, Jeongah Ryu, Nayeon Choi
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 region can make diagnosis difficult, especially for beginners. Therefore, we would like to use a simpler method, the Swischuk line method, to diagnose ADS. The purpose of our study was to
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Survival analysis of patients with extrahepatic cholangiocarcinoma: a nomogram for clinical and MRI features BMC Med. Imaging (IF 2.7) Pub Date : 2024-01-02 Yanyan Zeng, Xiaoyong Wang, Jiaojiao Wu, Limin Wang, Feng Shi, Jian Shu
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. A total of 104 patients with ECC confirmed by surgery and pathology were enrolled from January 2013 to July 2021, whose preoperative clinical, laboratory, and MRI data were retrospectively collected and examined