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Yayan J, Rasche K, Franke KJ, Windisch W, Berger M. FDG-PET-CT as an early detection method for tuberculosis: a systematic review and meta-analysis. BMC Public Health 2024; 24:2022. [PMID: 39075378 PMCID: PMC11285570 DOI: 10.1186/s12889-024-19495-6] [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: 07/25/2023] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
Tuberculosis (TB) causes major public health problems worldwide. Fighting TB requires sustained efforts in health prevention, diagnosis and treatment. Previous literature has shown that conventional diagnostic methods like X-ray and sputum microscopy often miss early or extrapulmonary TB due to their limited sensitivity. Blood tests, while useful, lack the anatomical detail needed for precise localization of TB lesions. A possible step forward in the fight against TB could be the use of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and Computed Tomography (CT). This meta-analysis discusses the current literature, including the methods, results and implications of using FDG-PET-CT in the early diagnosis of TB. Analysis of the studies showed that the sensitivity of FDG-PET-CT as a potential method for early detection of TB was 82.6%.
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Affiliation(s)
- Josef Yayan
- Department of Internal Medicine, Division of Pulmonary, Allergy and Sleep Medicine, Witten/Herdecke University, HELIOS Clinic Wuppertal, Heusnerstr. 40, 42283, Wuppertal, Germany.
| | - Kurt Rasche
- Department of Internal Medicine, Division of Pulmonary, Allergy and Sleep Medicine, Witten/Herdecke University, HELIOS Clinic Wuppertal, Heusnerstr. 40, 42283, Wuppertal, Germany
| | - Karl-Josef Franke
- University of Witten/Herdecke Chair of Internal Medicine I Department of Pulmonary Medicine, Clinical Center Siegen, Siegen, Germany
| | - Wolfram Windisch
- Department of Pneumology, Cologne Merheim Hospital, Witten/Herdecke University, Cologne, Germany
| | - Melanie Berger
- Department of Pneumology, Cologne Merheim Hospital, Witten/Herdecke University, Cologne, Germany
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Wang X, Zhang D, Sun Q, You Y, Lan J. Characteristics of atypical Pulmonary Tuberculosis without typical clinical features diagnosed by pathology. Am J Med Sci 2024:S0002-9629(24)01324-7. [PMID: 38936510 DOI: 10.1016/j.amjms.2024.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 06/29/2024]
Abstract
PURPOSE Some patients with pulmonary tuberculosis (PTB) do not display typical clinical features, leading to delays in diagnosis and treatment. METHODS We retrospectively analyzed PTB patients admitted to the Second Affiliated Hospital of Chongqing Medical University between 2017 and 2020. They are divided into pathological group (diagnosed through pathological biopsy) and control group (diagnosed via sputum or lavage fluid). Clinical data of both groups were compared. Based on radiographic features, the pathological group was further divided into the inflammation group, peripheral nodule group, and central occupancy group. We then statistically analyzed the computed tomography (CT) signs, bronchoscopic manifestations and results of pathological biopsy for each subgroup. RESULTS The pathological group consisted of 75 patients, while the control group had 338 patients. Multivariate logistic regression analysis showed that the pathological group had more diabetes (OR = 3.266, 95% CI = 1.609-6.630, P = 0.001), lower ESR (OR = 0.984, 95% CI = 0.971-0.998, P = 0.022), and lower CRP (OR = 0.990, 95% CI = 0.980-0.999, P = 0.036). In the three subgroups, the exudative lesions in the inflammation group were mostly located in atypical areas of PTB. The lobulation sign and spiculation sign were frequently observed in the peripheral nodule group. All presented with significant hilar mediastinal lymphadenopathy in the central occupancy group. In the pathological group, bronchoscopic manifestations typically included mucosal edema and bronchial stenosis. CONCLUSION Diabetes is an independent risk factor for atypical PTB. Expression of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) in atypical PTB is low. Radiologically, it is most easily misdiagnosed when presented as peripheral solid nodules or masses, so a biopsy is recommended.
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Affiliation(s)
- Xi Wang
- Department of Respiratory and Critical Care Medicine, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dandan Zhang
- Department of Respiratory and Critical Care Medicine, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qiuxia Sun
- Department of Respiratory and Critical Care Medicine, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yan You
- Department of Pathology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jian Lan
- Department of Respiratory and Critical Care Medicine, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Xu CJ, Lu PX, Li CH, He YL, Fang WJ, Xie RM, Jin GQ, Lu YB, Zheng QT, Zheng GP, Lv SX, Huang H, Li L, Ren M, Shi YX, Wen XN, Li L, Wei FJ, Hou DL, Lv Y, Shan F, Wu ZC, Hu ZL, Zhang XR, Liu DX, Shi WY, Li HR, Zhang N, Song M, Zhang X, Deng YY, Li J, Liu Q, Li D, Zhao L, Chen BD, Shi YB, Jiang FL, Tang X, Wu LJ, Ma W, Xu XY, Li HJ. Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis. Quant Imaging Med Surg 2024; 14:1039-1060. [PMID: 38223121 PMCID: PMC10784038 DOI: 10.21037/qims-23-1223] [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: 08/27/2023] [Accepted: 09/23/2023] [Indexed: 01/16/2024]
Abstract
Tuberculosis (TB) remains one of the major infectious diseases in the world with a high incidence rate. Drug-resistant tuberculosis (DR-TB) is a key and difficult challenge in the prevention and treatment of TB. Early, rapid, and accurate diagnosis of DR-TB is essential for selecting appropriate and personalized treatment and is an important means of reducing disease transmission and mortality. In recent years, imaging diagnosis of DR-TB has developed rapidly, but there is a lack of consistent understanding. To this end, the Infectious Disease Imaging Group, Infectious Disease Branch, Chinese Research Hospital Association; Infectious Diseases Group of Chinese Medical Association of Radiology; Digital Health Committee of China Association for the Promotion of Science and Technology Industrialization, and other organizations, formed a group of TB experts across China. The conglomerate then considered the Chinese and international diagnosis and treatment status of DR-TB, China's clinical practice, and evidence-based medicine on the methodological requirements of guidelines and standards. After repeated discussion, the expert consensus of imaging diagnosis of DR-PB was proposed. This consensus includes clinical diagnosis and classification of DR-TB, selection of etiology and imaging examination [mainly X-ray and computed tomography (CT)], imaging manifestations, diagnosis, and differential diagnosis. This expert consensus is expected to improve the understanding of the imaging changes of DR-TB, as a starting point for timely detection of suspected DR-TB patients, and can effectively improve the efficiency of clinical diagnosis and achieve the purpose of early diagnosis and treatment of DR-TB.
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Affiliation(s)
- Chuan-Jun Xu
- Department of Radiology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Pu-Xuan Lu
- Department of Medical Imaging, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Chun-Hua Li
- Department of Radiology, Chongqing Public Health Medical Center, Chongqing, China
| | - Yu-Lin He
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wei-Jun Fang
- Department of Radiology, Guangzhou Chest Hospital, Guangzhou, China
| | - Ru-Ming Xie
- Department of Radiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Guan-Qiao Jin
- Department of Radiology, The Affiliated Cancer Hospital of Guangxi Medical University, Nanning, China
| | - Yi-Bo Lu
- Department of Radiology, The Fourth People’s Hospital of Nanning, Nanning, China
| | - Qiu-Ting Zheng
- Department of Medical Imaging, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Guang-Ping Zheng
- Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, China
| | - Sheng-Xiu Lv
- Department of Radiology, Chongqing Public Health Medical Center, Chongqing, China
| | - Hua Huang
- Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, China
| | - Li Li
- Department of Radiology, Beijing You’an Hospital, Capital Medical University, Beijing, China
| | - Meiji Ren
- Department of Radiology, Beijing You’an Hospital, Capital Medical University, Beijing, China
| | - Yu-Xin Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Shanghai, China
| | - Xin-Nian Wen
- Department of Medical Imaging, Chest Hospital of Guangxi Zhuang Autonomous Region, Liuzhou, China
| | - Lin Li
- Department of Radiology, Linyi People’s Hospital, Linyi, China
| | - Fang-Jun Wei
- Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, China
| | - Dai-Lun Hou
- Department of Medical Imaging, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Yan Lv
- Department of Medical Imaging, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Shanghai, China
| | - Zheng-Can Wu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhi-Liang Hu
- Department of Infectious Disease, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Xiang-Rong Zhang
- Department of Pulmonary Tuberculosis, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Du-Xian Liu
- Department of Pathology, The Second Hospital of Nanjing, Nanjing, China
| | - Wei-Ya Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Shanghai, China
| | - Hui-Ru Li
- Department of Radiology, Guangzhou Chest Hospital, Guangzhou, China
| | - Na Zhang
- Department of Radiology, Public Health and Clinical Center of Chengdu, Chengdu, China
| | - Min Song
- Department of Radiology, Guangzhou Chest Hospital, Guangzhou, China
| | - Xin Zhang
- Department of Medical Imaging, The Fourth People’s Hospital of Huai’an, Huai’an, China
| | - Ying-Ying Deng
- Department of Radiology, Shenzhen Yantian District People’s Hospital, Shenzhen, China
| | - Jinlong Li
- Department of Laboratory Medicine, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Qiang Liu
- Department of Radiology, Shandong Provincial Hospital, Shandong First Medical University, Jinan, China
| | - Dechun Li
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Lingling Zhao
- Department of Radiology, The Sixth Peoples Hospital of Zhengzhou, Zhengzhou, China
| | - Bu-Dong Chen
- Medical Imaging Quality Research Committee, China Quality Association for Pharmaceuticals, Beijing, China
| | - Yan-Bin Shi
- Department of Radiology, The Sixth Peoples Hospital of Zhengzhou, Zhengzhou, China
| | - Feng-Li Jiang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Xin Tang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Li-Ji Wu
- Department of Imaging, Fourth Hospital of Inner Mongolia Autonomous, Hohhot, China
| | - Wei Ma
- Department of Radiology, The Third People’s Hospital of Longgang, Shenzhen, China
| | - Xin-Yue Xu
- The School of Radiation Medicine and Protection (SRMP) of Soochow University, Suzhou, China
| | - Hong-Jun Li
- Department of Radiology, Beijing You’an Hospital, Capital Medical University, Beijing, China
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Li CH, Fan X, Lv SX, Liu XY, Wang JN, Li YM, Li Q. Clinical and Computed Tomography Features Associated with Multidrug-Resistant Pulmonary Tuberculosis: A Retrospective Study in China. Infect Drug Resist 2023; 16:651-659. [PMID: 36743337 PMCID: PMC9897068 DOI: 10.2147/idr.s394071] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 01/06/2023] [Indexed: 02/03/2023] Open
Abstract
Purpose To explore the value of integrating clinical and computed tomography (CT) features to predict multidrug-resistant pulmonary tuberculosis (MDR-PTB). Patients and Methods The study included 212 patients with MDR-PTB and 180 patients with drug-sensitive pulmonary tuberculosis (DS-PTB) who referred to our institute in China between January 2016 and March 2021. The clinical and CT characteristics were analyzed and compared between both groups. Multivariable logistic regression analysis was performed to identify independent factors that can be used to predict MDR-PTB. Furthermore, 115 patients admitted to another center from January 2019 to January 2022 were included as external validation cohort. Results For clinical characteristics, five parameters were significantly different between the two groups (all P < 0.05). With regard to CT features, nine parameters were significantly different between the two groups (all P < 0.05). Multivariable logistic regression analysis using the aforementioned differential features showed that male sex, retreated history, longer duration of previous anti-TB treatment, lower CD4+ T lymphocyte count, thick-walled cavity, centrilobular micronodules and tree-in-bud sign, bronchial stenosis, pleural and pericardial thickening were the most effective variations associated with MDR-PTB with an area under the curve (AUC) of 0.849 and accuracy of 78.6%. Furthermore, the external validation cohort that contains 115 patients obtained an AUC of 0.933 and accuracy of 81.7%. Conclusion MDR-PTB and DS-PTB have different clinical and imaging characteristics. A combined model incorporating these differential features can promptly diagnose MDR-PTB and develop subsequent therapeutic strategies.
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Affiliation(s)
- Chun-Hua Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China,Department of Radiology, Chongqing Public Health Medical Center, Chongqing, People’s Republic of China
| | - Xiao Fan
- Department of Radiology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, People’s Republic of China
| | - Sheng-Xiu Lv
- Department of Radiology, Chongqing Public Health Medical Center, Chongqing, People’s Republic of China
| | - Xue-Yan Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China,Department of Radiology, Chongqing Public Health Medical Center, Chongqing, People’s Republic of China
| | - Jia-Nan Wang
- Department of Radiology, Chongqing Public Health Medical Center, Chongqing, People’s Republic of China
| | - Yong-Mei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Qi Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China,Correspondence: Qi Li; Yong-Mei Li, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, People’s Republic of China, Tel +0086 15823408652, Fax +0086 23 68811487, Email ;
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Zhang ZX, Mu XY, Yu J, Guan CS, Chen BD, Xie RM. Establishment and evaluation of a CT-based radiomic model for AIDS-associated pulmonary cryptococcosis. BMC Med Imaging 2022; 22:185. [PMID: 36309647 PMCID: PMC9617378 DOI: 10.1186/s12880-022-00910-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/29/2022] [Indexed: 11/10/2022] Open
Abstract
Background Establish a CT-based diagnostic radiomic model for AIDS complicated with pulmonary cryptococcosis and evaluate the diagnostic efficacy of this model.
Methods This retrospective study enrolled 98 AIDS patients with pulmonary cryptococcosis and 103 AIDS patients with other infections or neoplastic lesions, comprising a total of 699 lesions. Patients were randomly divided into a training group and test group at a ratio of 2.75:1. Features from all lesions, cavity lesions and solid nodule lesions were extracted, and two kinds of radiomic models (6 types) were established. ROC curves were drawn, and the sensitivity and specificity were calculated to compare the SVM model and LR model, radiologists’ empirical diagnoses and the combination of these empirical diagnoses with the radiomic model. Results The AUCs of senior radiologist for all lesions and cavity lesions were lower than those of the SVM and LR models. The diagnostic efficacy of primary radiologist was lower than that of both of the other model types. The diagnostic efficacy of the LR model was relatively stable, with the highest diagnostic efficiency of the 3 model/radiologist groups. The AUCs of intermediate radiologist in combination with the LR radiomic model for all lesions, nodular lesions and cavity lesions were 0.88, 0.84, and 0.9, respectively, which were the highest among all models and radiologists. Conclusions The CT-based radiomic LR model of AIDS-associated pulmonary cryptococcosis exhibits good diagnostic performance, which was similar to that of senior radiologists and higher than that of the primary radiologist. With the help of a radiomic model, radiologists can achieve improved diagnostic accuracy compared to that when only an empirical diagnosis is used. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00910-6.
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Ren Y, Ren H, Tian Q, Li X, Liu Y. The relationship between computed tomography appearance of pulmonary tuberculosis and blood glucose levels in 763 diabetes mellitus patients with pulmonary tuberculosis: a comparative study. Endocrine 2022; 76:584-592. [PMID: 35332512 PMCID: PMC9156482 DOI: 10.1007/s12020-022-03033-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/02/2022] [Indexed: 11/07/2022]
Abstract
INTRODUCTION Glucose tolerance is often impaired in pulmonary tuberculosis (TB) patients. We aimed to explore the relationship between computed tomography (CT) findings of TB and blood glucose levels in diabetes mellitus (DM) patients. METHODS 763 diabetes mellitus patients with pulmonary tuberculosis (DMTB) from March 2015 to March 2018 were selected and their clinical data were retrospectively evaluated. CT appearance of DMTB was reviewed and compared according to blood glucose levels and CT scores. TB scores were calculated according to the combination of typical and atypical CT appearance. The relationship between blood glucose levels and CT scores was analyzed via Pearson correlation coefficient. RESULTS TB lesions mainly occurred in the left lung and the lower lobes in the DMTB patients. Nodules and cavities are the main typical complications in these DMTB patients, and especially the number and size of cavities predominantly contribute to CT scan scores. The size of cavities (diameters (cm), median (95% CI of median)) was 0.72 (0.66-0.77), 1.20 (1.09-1.28), and 3.45 (2.92-3.94) from the low-, middle- and high-CT-score groups, respectively. The patients with high CT scores had a higher level of fasting plasma glucose (mean 13.48 mM, 95% CI of median 12.56-14.44 mM) than the patients in the low-CT-score (mean 8.73 mM, 95% CI of median 8.49-9.36 mM) and middle-CT-score groups (mean 10.16 mM, 95% CI of median 9.89-10.49 mM) (P < 0.0001). CT scores have a consistent relationship with the levels of blood glucose (rho = 0.60, P < 0.0001). CONCLUSIONS CT appearance stands for the severity of tuberculosis and is closely associated with blood glucose levels in diabetic TB patients.
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Affiliation(s)
- Yan Ren
- Tuberculosis Ward 9, Shenyang Tenth People's Hospital, Shenyang, 110044, China.
| | - Hongmei Ren
- Tuberculosis Ward 9, Shenyang Tenth People's Hospital, Shenyang, 110044, China
| | - Qian Tian
- Tuberculosis Ward 9, Shenyang Tenth People's Hospital, Shenyang, 110044, China
| | - Xue Li
- Tuberculosis Ward 9, Shenyang Tenth People's Hospital, Shenyang, 110044, China
| | - Yuxi Liu
- Tuberculosis Ward 9, Shenyang Tenth People's Hospital, Shenyang, 110044, China
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Zhang K, Qi S, Cai J, Zhao D, Yu T, Yue Y, Yao Y, Qian W. Content-based image retrieval with a Convolutional Siamese Neural Network: Distinguishing lung cancer and tuberculosis in CT images. Comput Biol Med 2022; 140:105096. [PMID: 34872010 DOI: 10.1016/j.compbiomed.2021.105096] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/17/2021] [Accepted: 11/27/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND CT findings of lung cancer and tuberculosis are sometimes similar, potentially leading to misdiagnosis. This study aims to combine deep learning and content-based image retrieval (CBIR) to distinguish lung cancer (LC) from nodular/mass atypical tuberculosis (NMTB) in CT images. METHODS This study proposes CBIR with a convolutional Siamese neural network (CBIR-CSNN). First, the lesion patches are cropped out to compose LC and NMTB datasets and the pairs of two arbitrary patches form a patch-pair dataset. Second, this patch-pair dataset is utilized to train a CSNN. Third, a test patch is treated as a query. The distance between this query and 20 patches in both datasets is calculated using the trained CSNN. The patches closest to the query are used to give the final prediction by majority voting. One dataset of 719 patients is used to train and test the CBIR-CSNN. Another external dataset with 30 patients is employed to verify CBIR-CSNN. RESULTS The CBIR-CSNN achieves excellent performance at the patch level with an mAP (Mean Average Precision) of 0.953, an accuracy of 0.947, and an area under the curve (AUC) of 0.970. At the patient level, the CBIR-CSNN correctly predicted all labels. In the external dataset, the CBIR-CSNN has an accuracy of 0.802 and AUC of 0.858 at the patch level, and 0.833 and 0.902 at the patient level. CONCLUSIONS This CBIR-CSNN can accurately and automatically distinguish LC from NMTB using CT images. CBIR-CSNN has excellent representation capability, compatibility with few-shot learning, and visual explainability.
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Affiliation(s)
- Kai Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110169, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110169, China.
| | - Jiumei Cai
- Department of Health Medicine, General Hospital of Northern Theater Command, Shenyang, 110003, China; Department of Medical Imaging, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, 110042, China.
| | - Dan Zhao
- Department of Medical Imaging, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, 110042, China.
| | - Tao Yu
- Department of Medical Imaging, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, 110042, China.
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110004, China.
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.
| | - Wei Qian
- Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX, 79968, USA.
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