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Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-16 Wenci Liu, Wubiao Chen, Jun Xia, Zhendong Lu, Youwen Fu, Yuange Li, Zhi Tan
The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. Two cohorts
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Construction of a nomogram for predicting compensated cirrhosis with Wilson’s disease based on non-invasive indicators BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-16 Yan Li, Jing Ping Wang, Xiaoli Zhu
Wilson’s disease (WD) often leads to liver fibrosis and cirrhosis, and early diagnosis of WD cirrhosis is essential. Currently, there are few non-invasive prediction models for WD cirrhosis. The purpose of this study is to non-invasively predict the occurrence risk of compensated WD cirrhosis based on ultrasound imaging features and clinical characteristics. A retrospective analysis of the clinical
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Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-15 Yangchun Du, Wenwen Guo, Yanju Xiao, Haining Chen, Jinxiu Yao, Ji Wu
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 ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. We randomised data from 849 patients with ovarian tumours into
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The feasibility of half-dose contrast-enhanced scanning of brain tumours at 5.0 T: a preliminary study BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-13 Zhiyong Jiang, Wenbo Sun, Dan Xu, Hao Mei, Jianmin Yuan, Xiaopeng Song, Chao Ma, Haibo Xu
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. Twelve subjects diagnosed with brain tumours were included in this study and underwent MRI after contrast agent injection at 3.0 T (full dose) or 5.0 T (half dose) with a 3D T1-weighted gradient echo sequence. The postcontrast images were compared
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Role of radiomics in staging liver fibrosis: a meta-analysis BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-12 Xiao-min Wang, Xiao-jing Zhang
Fibrosis has important pathoetiological and prognostic roles in chronic liver disease. This study evaluates the role of radiomics in staging liver fibrosis. After literature search in electronic databases (Embase, Ovid, Science Direct, Springer, and Web of Science), studies were selected by following precise eligibility criteria. The quality of included studies was assessed, and meta-analyses were
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Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-10 Rui Sun, Zhengyin Zhang, Yajun Liu, Xiaohang Niu, Jie Yuan
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 role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for
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Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-10 Andong Ma, Xinran Yan, Yaoming Qu, Haitao Wen, Xia Zou, Xinzi Liu, Mingjun Lu, Jianhua Mo, Zhibo Wen
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 LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. This retrospective study included 90 patients histopathologically diagnosed with LGG
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Radiomics-based discrimination of coronary chronic total occlusion and subtotal occlusion on coronary computed tomography angiography BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-09 Jun Li, Lichen Ren, Hehe Guo, Haibo Yang, Jingjing Cui, Yonggao Zhang
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 differentiate CTO and SO. A total of 66 patients with SO underwent CCTA before invasive angiography and were matched to 66 patients with CTO. Comprehensive imaging analysis was conducted for all lesioned
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Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-08 Vatsala Anand, Sheifali Gupta, Deepika Koundal, Wael Y. Alghamdi, Bayan M. Alsharbi
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 as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring
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Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-08 S. R. Sannasi Chakravarthy, N. Bharanidharan, V. Vinoth Kumar, T. R. Mahesh, Mohammed S. Alqahtani, Suresh Guluwadi
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 research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper
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Correction: Evaluating the consistency in different methods for measuring left atrium diameters BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-08 Jun-Yan Yue, Kai Ji, Hai-Peng Liu, Qing-Wu Wu, Chang-Hua Liang, Jian-Bo Gao
Correction: Yue et al. BMC Medical Imaging (2024) 24:57. https://doi.org/10.1186/s12880-024-01231-6 In the original article [1], Figure 3 was incorrectly duplicated from Figure 6 during the typesetting process. The wrong Figure 3 is shown below: The correct Figure 3 is shown below: The original article [1] has been corrected. Yue JY, Ji K, Liu HP, et al. Evaluating the consistency in different methods
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Dynamic radiomics based on contrast-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-08 Rui Zhang, Yao Wang, Zhi Li, Yushu Shi, Danping Yu, Qiang Huang, Feng Chen, Wenbo Xiao, Yuan Hong, Zhan Feng
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). We retrospectively included 175 and 75 HCC patients who underwent preoperative DCE-MRI from September 2019 to August 2022 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Static
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A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-06 Blake VanBerlo, Jesse Hoey, Alexander Wong
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 into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic
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Renal interstitial fibrotic assessment using non-Gaussian diffusion kurtosis imaging in a rat model of hyperuricemia BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-03 Ping-Kang Chen, Zhong-Yuan Cheng, Ya-Lin Wang, Bao-Jun Xu, Zong-Chao Yu, Zhao-Xia Li, Shang-Ao Gong, Feng-Tao Zhang, Long Qian, Wei Cui, You-Zhen Feng, Xiang-Ran Cai
To investigate the feasibility of Diffusion Kurtosis Imaging (DKI) in assessing renal interstitial fibrosis induced by hyperuricemia. A hyperuricemia rat model was established, and the rats were randomly split into the hyperuricemia (HUA), allopurinol (AP), and AP + empagliflozin (AP + EM) groups (n = 19 per group). Also, the normal rats were selected as controls (CON, n = 19). DKI was performed before
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Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-02 Xuelian Bian, Qi Sun, Mi Wang, Hanyun Dong, Xiaoxiao Dai, Liyuan Zhang, Guohua Fan, Guangqiang Chen
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) status in colorectal cancer (CRC) patients. A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients
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Dynamic contrast-enhanced MR imaging in identifying active anal fistula after surgery BMC Med. Imaging (IF 2.7) Pub Date : 2024-04-01 Weiping Lu, Xiaoyan Li, Wenwen Liang, Kai Chen, Xinyue Cao, Xiaowen Zhou, Ying Wang, Bingcang Huang
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 between active anal fistula and postoperative healing (granulation) tissue. Thirty-six patients following idiopathic anal fistula underwent DCE-MRI. Subjects were divided into Group I (active
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The use of individual-based FDG-PET volume of interest in predicting conversion from mild cognitive impairment to dementia BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-28 Shu-Hua Huang, Wen-Chiu Hsiao, Hsin-I Chang, Mi-Chia Ma, Shih-Wei Hsu, Chen-Chang Lee, Hong-Jie Chen, Ching-Heng Lin, Chi-Wei Huang, Chiung-Chih Chang
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 conversion in patients with mild cognitive impairment (MCI). We included 44 MCI converters (MCI-C), 38 non-converters (MCI-NC), 42 patients with Alzheimer’s disease with dementia, and 40 cognitively
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The value of a neural network based on multi-scale feature fusion to ultrasound images for the differentiation in thyroid follicular neoplasms BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-27 Weiwei Chen, Xuejun Ni, Cheng Qian, Lei Yang, Zheng Zhang, Mengdan Li, Fanlei Kong, Mengqin Huang, Maosheng He, Yifei Yin
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 preoperative US. This retrospective study involved the collection of ultrasound images from 279 patients at two tertiary level hospitals. To address the issue of false positives caused by small
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Correction: Evaluating renal iron overload in diabetes mellitus by blood oxygen level-dependent magnetic resonance imaging: a longitudinal experimental study BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-26 Weiwei Geng, Liang Pan, Liwen Shen, Yuanyuan Sha, Jun Sun, Shengnan Yu, Jianguo Qiu, Wei Xing
Correction: Geng et al. BMC Medical Imaging (2022) 22:200 https://doi.org/10.1186/s12880-022-00939-7 Following the publication of the original article [1], the authors reported an error with regard to Figure 5. In the original article, the wrong image was used in the hematoxylin and eosin staining image of DI group at week 0, as seen below: The correct Figure is as follows: The original article [1]
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Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-26 Shiying Zhang, Manling Ge, Hao Cheng, Shenghua Chen, Yihui Li, Kaiwei Wang
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, this study combined an index model specific for resting-state functional connectivity (FC) with a supervised machine learning method. Performance scores on conventional cognitive test scores and
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Characteristics of high frame frequency contrast-enhanced ultrasound in renal tumors BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-25 WeiPing Zhang, JingLing Wang, Li Chen
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. A total of 140 patients with renal lesions underwent contrast-enhanced ultrasound (CEUS) examination in the First Affiliated Hospital of Nanchang University from July 2022 to July 2023. Based on the tumor pathology
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Comparison of ASL and DSC perfusion methods in the evaluation of response to treatment in patients with a history of treatment for malignant brain tumor BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-22 Ezgi Suat Bayraktar, Gokhan Duygulu, Yusuf Kenan Çetinoğlu, Mustafa Fazıl Gelal, Melda Apaydın, Hülya Ellidokuz
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 purpose. The use of arterial spin labeling (ASL), a perfusion technique that does not require exogenous contrast material injection, has gained popularity in recent years. The aim of the study
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Predictive value of cyst/tumor volume ratio of pituitary adenoma for tumor cell proliferation BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-21 Jianwu Wu, Fangfang Zhang, Yinxing Huang, Liangfeng Wei, Tao Mei, Shousen Wang, Zihuan Zeng, Wei Wang
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 of PA has not been reported. Herein, we determined the predictive value of the C/T ratio of PA for tumor cell proliferation. The clinical data of 72 patients with PA and cystic change on MRI
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Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-21 Hongyu Zhou, Jianmin Ding, Yan Zhou, Yandong Wang, Lei Zhao, Cho-Chiang Shih, Jingping Xu, Jianan Wang, Ling Tong, Zhouye Chen, Qizhong Lin, Xiang Jing
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 diagnosis of liver lesions with CEUS requires a precise interpretation of CEUS images. However,it is a highly experience dependent task which requires amount of training and practice. To help
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Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI BMC Med. Imaging (IF 2.7) Pub Date : 2024-03-20 Simona Bottani, Elina Thibeau-Sutre, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos
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 gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging
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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