1
|
Mertiri L, Freiling JT, Desai NK, Kralik SF, Huisman TAGM. Pediatric and adult meningeal, parenchymal, and spinal tuberculosis: A neuroimaging review. J Neuroimaging 2024; 34:179-194. [PMID: 38073450 DOI: 10.1111/jon.13177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/24/2023] [Accepted: 11/28/2023] [Indexed: 03/12/2024] Open
Abstract
Neurotuberculosis is defined as a tuberculous infection of the meninges, brain parenchyma, vessels, cranial and spinal nerves, spinal cord, skull, and spine that can occur either in a localized or in a diffuse form. It is a heterogeneous disease characterized by many imaging appearances and it has been defined as "the great mimicker" due to similarities with many other conditions. The diagnosis of central nervous system (CNS) tuberculosis (TB) is based on clinical presentation, neuroimaging findings, laboratory and microbiological findings, and comprehensive evaluation of the response to anti-TB drug treatment. However, the absence of specific symptoms, the wide spectrum of neurological manifestations, the myriad of imaging findings, possible inconclusive laboratory results, and the paradoxical reaction to treatment make the diagnosis often challenging and difficult, potentially delaying adequate treatment with possible devastating short-term and long-term neurologic sequelae. Familiarity with the imaging characteristics helps in accurate diagnosis and may prevent or limit significantly morbidity and mortality. The goal of this review is to provide a comprehensive up-to-date overview of the conventional and advanced imaging features of CNS TB for radiologists, neuroradiologists, and pediatric radiologists. We discuss the most typical neurotuberculosis imaging findings and their differential diagnosis in children and adults with the goal to provide a global overview of this entity.
Collapse
Affiliation(s)
- Livja Mertiri
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - John T Freiling
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Nilesh K Desai
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Stephen F Kralik
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Thierry A G M Huisman
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| |
Collapse
|
2
|
Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Mousavi Baigi SF, Rezaei Sarsari M, Dahmardeh Kemmak F, Mazaheri Habibi MR. The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review. Health Sci Rep 2024; 7:e1893. [PMID: 38357491 PMCID: PMC10865276 DOI: 10.1002/hsr2.1893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Background and Aims This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. Methods On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. Results After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. Conclusion The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.
Collapse
Affiliation(s)
- Kosar Ghaddaripouri
- Department of Health Information Management, School of Health Management and Information SciencesShiraz University of Medical SciencesShirazIran
| | - Maryam Ghaddaripouri
- Department of Laboratory Sciences, School of Paramedical and Rehabilitation SciencesMashhad University of Medical SciencesMashhadIran
| | | | - Seyyedeh Fatemeh Mousavi Baigi
- Mashhad University of Medical SciencesMashhadIran
- Student Research CommitteeMashhad University of Medical SciencesMashhadIran
| | | | - Fatemeh Dahmardeh Kemmak
- Mashhad University of Medical SciencesMashhadIran
- Student Research CommitteeMashhad University of Medical SciencesMashhadIran
| | | |
Collapse
|
3
|
Chen X, Chen F, Liang C, He G, Chen H, Wu Y, Chen Y, Shuai J, Yang Y, Dai C, Cao L, Wang X, Cai E, Wang J, Wu M, Zeng L, Zhu J, Hai D, Pan W, Pan S, Zhang C, Quan S, Su F. MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations. Front Microbiol 2023; 14:1308149. [PMID: 38149270 PMCID: PMC10750405 DOI: 10.3389/fmicb.2023.1308149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 11/14/2023] [Indexed: 12/28/2023] Open
Abstract
Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting grave clinical challenges due to its nonspecific symptoms and the urgent need for timely intervention. The severity and the rapid progression of TBM underscore the necessity of early and accurate diagnosis to prevent irreversible neurological deficits and reduce mortality rates. Traditional diagnostic methods, reliant primarily on clinical findings and cerebrospinal fluid analysis, often falter in delivering timely and conclusive results. Moreover, such methods struggle to distinguish TBM from other forms of neuroinfections, making it critical to seek advanced diagnostic solutions. Against this backdrop, magnetic resonance imaging (MRI) has emerged as an indispensable modality in diagnostics, owing to its unique advantages. This review provides an overview of the advancements in MRI technology, specifically emphasizing its crucial applications in the early detection and identification of complex pathological changes in TBM. The integration of artificial intelligence (AI) has further enhanced the transformative impact of MRI on TBM diagnostic imaging. When these cutting-edge technologies synergize with deep learning algorithms, they substantially improve diagnostic precision and efficiency. Currently, the field of TBM imaging diagnosis is undergoing a phase of technological amalgamation. The melding of MRI and AI technologies unquestionably signals new opportunities in this specialized area.
Collapse
Affiliation(s)
- Xingyu Chen
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Fanxuan Chen
- School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Chenglong Liang
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Guoqiang He
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Hao Chen
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yanchan Wu
- School of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Yinda Chen
- School of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Jincen Shuai
- Baskin Engineering, University of California, Santa Cruz, CA, United States
| | - Yilei Yang
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | | | - Luhuan Cao
- Wenzhou Medical University, Wenzhou, China
| | - Xian Wang
- Wenzhou Medical University, Wenzhou, China
| | - Enna Cai
- Wenzhou Medical University, Wenzhou, China
| | | | | | - Li Zeng
- Wenzhou Medical University, Wenzhou, China
| | | | - Darong Hai
- Wenzhou Medical University, Wenzhou, China
| | - Wangzheng Pan
- Renji College of Wenzhou Medical University, Wenzhou, China
| | - Shuo Pan
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Chengxi Zhang
- School of Materials Science and Engineering, Shandong Jianzhu University, Jinan, China
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou, China
- Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou, China
| | - Feifei Su
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
- Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China
| |
Collapse
|
4
|
Shi Y, Zhang C, Pan S, Chen Y, Miao X, He G, Wu Y, Ye H, Weng C, Zhang H, Zhou W, Yang X, Liang C, Chen D, Hong L, Su F. The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms. Front Microbiol 2023; 14:1290746. [PMID: 37942080 PMCID: PMC10628659 DOI: 10.3389/fmicb.2023.1290746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
Tuberculous meningitis (TBM) poses a diagnostic challenge, particularly impacting vulnerable populations such as infants and those with untreated HIV. Given the diagnostic intricacies of TBM, there's a pressing need for rapid and reliable diagnostic tools. This review scrutinizes the efficacy of up-and-coming technologies like machine learning in transforming TBM diagnostics and management. Advanced diagnostic technologies like targeted gene sequencing, real-time polymerase chain reaction (RT-PCR), miRNA assays, and metagenomic next-generation sequencing (mNGS) offer promising avenues for early TBM detection. The capabilities of these technologies are further augmented when paired with mass spectrometry, metabolomics, and proteomics, enriching the pool of disease-specific biomarkers. Machine learning algorithms, adept at sifting through voluminous datasets like medical imaging, genomic profiles, and patient histories, are increasingly revealing nuanced disease pathways, thereby elevating diagnostic accuracy and guiding treatment strategies. While these burgeoning technologies offer hope for more precise TBM diagnosis, hurdles remain in terms of their clinical implementation. Future endeavors should zero in on the validation of these tools through prospective studies, critically evaluating their limitations, and outlining protocols for seamless incorporation into established healthcare frameworks. Through this review, we aim to present an exhaustive snapshot of emerging diagnostic modalities in TBM, the current standing of machine learning in meningitis diagnostics, and the challenges and future prospects of converging these domains.
Collapse
Affiliation(s)
- Yi Shi
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Chengxi Zhang
- School of Materials Science and Engineering, Shandong Jianzhu University, Jinan, China
| | - Shuo Pan
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yi Chen
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Xingguo Miao
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
| | - Guoqiang He
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Yanchan Wu
- School of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Hui Ye
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
| | - Chujun Weng
- The Fourth Affiliated Hospital Zhejiang University School of Medicine, Yiwu, China
| | - Huanhuan Zhang
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China
| | - Wenya Zhou
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China
| | - Xiaojie Yang
- Wenzhou Medical University Renji College, Wenzhou, China
| | - Chenglong Liang
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Dong Chen
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
- Wenzhou Central Blood Station, Wenzhou, China
| | - Liang Hong
- Department of Infectious Diseases, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feifei Su
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
| |
Collapse
|
5
|
Zhao MR, Ma WJ, Song XC, Li ZJ, Shao ZZ, Lu H, Zhao R, Guo YJ, Ye ZX, Liu PF. Feasibility analysis of magnetic resonance imaging-based radiomics features for preoperative prediction of nuclear grading of ductal carcinoma in situ. Gland Surg 2023; 12:1209-1223. [PMID: 37842532 PMCID: PMC10570967 DOI: 10.21037/gs-23-132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/27/2023] [Indexed: 10/17/2023]
Abstract
Background The nuclear grading of ductal carcinoma in situ (DCIS) affects its clinical risk. The aim of this study was to investigate the possibility of predicting the nuclear grading of DCIS, by magnetic resonance imaging (MRI)-based radiomics features. And to develop a nomogram combining radiomics features and MRI semantic features to explore the potential role of MRI radiomic features in the assessment of DCIS nuclear grading. Methods A total of 156 patients (159 lesions) with DCIS and DCIS with microinvasive (DCIS-MI) were enrolled in this retrospective study, with 112 lesions included in the training cohort and 47 lesions included in the validation cohort. Radiomics features were extracted from Dynamic contrast-enhanced MRI (DCE-MRI) phases 1st and 5th. After feature selection, radiomics signature was constructed and radiomics score (Rad-score) was calculated. Multivariate analysis was used to identify MRI semantic features that were significantly associated with DCIS nuclear grading and combined with Rad-score to construct a Nomogram. Receiver operating characteristic curves were used to evaluate the predictive performance of Rad-score and Nomogram, and decision curve analysis (DCA) was used to evaluate the clinical utility. Results In multivariate analyses of MRI semantic features, larger tumor size and heterogeneous enhancement pattern were significantly associated with high-nuclear grade DCIS (HNG DCIS). In the training cohort, Nomogram had an area under curve (AUC) of 0.879 and Rad-score had an AUC of 0.828. Similarly, in the independent validation cohort, Nomogram had an AUC value of 0.828 and Rad-score had an AUC of 0.772. In both the training and validation cohorts, Nomogram had a significantly higher AUC value than Rad-score (P<0.05). DCA confirmed that Nomogram had a higher net clinical benefit. Conclusions MRI-based radiomic features can be used as potential biomarkers for assessing nuclear grading of DCIS. The nomogram constructed by radiomic features combined with semantic features is feasible in discriminating non-HNG and HNG DCIS.
Collapse
Affiliation(s)
- Meng-Ran Zhao
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wen-Juan Ma
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xiang-Chao Song
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhi-Jun Li
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhen-Zhen Shao
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Hong Lu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Rui Zhao
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Yi-Jun Guo
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhao-Xiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Pei-Fang Liu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| |
Collapse
|
6
|
Li X, Zhang C, Li T, Lin X, Wu D, Yang G, Cao D. Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies. Sci Rep 2023; 13:15586. [PMID: 37730961 PMCID: PMC10511693 DOI: 10.1038/s41598-023-42916-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/16/2023] [Indexed: 09/22/2023] Open
Abstract
Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.
Collapse
Affiliation(s)
- Xiumei Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Tingting Li
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, 361021, Fujian, China
| | - Xiuqiang Lin
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China.
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China.
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Shanghai, 200062, China.
| |
Collapse
|