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Yi M, Lin Y, Lin Z, Xu Z, Li L, Huang R, Huang W, Wang N, Zuo Y, Li N, Ni D, Zhang Y, Li Y. Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network. Clin Breast Cancer 2024; 24:e319-e332.e2. [PMID: 38494415 DOI: 10.1016/j.clbc.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 02/04/2024] [Accepted: 02/06/2024] [Indexed: 03/19/2024]
Abstract
OBJECTIVES To develop predictive nomograms based on clinical and ultrasound features and to improve the clinical strategy for US BI-RADS 4A lesions. METHODS Patients with US BI-RADS 4A lesions from 3 hospitals between January 2016 and June 2020 were retrospectively included. Clinical and ultrasound features were extracted to establish nomograms CE (based on clinical experience) and DL (based on deep-learning algorithm). The performances of nomograms were evaluated by receiver operator characteristic curves, calibration curves and decision curves. Diagnostic performances with DL of radiologists were analyzed. RESULTS 1616 patients from 2 hospitals were randomly divided into training and internal validation cohorts at a ratio of 7:3. Hundred patients from another hospital made up external validation cohort. DL achieved more optimized AUCs than CE (internal validation: 0.916 vs. 0.863, P < .01; external validation: 0.884 vs. 0.776, P = .05). The sensitivities of DL were higher than CE (internal validation: 81.03% vs. 72.41%, P = .044; external validation: 93.75% vs. 81.25%, P = .4795) without losing specificity (internal validation: 84.91% vs. 86.47%, P = .353; external validation: 69.14% vs. 71.60%, P = .789). Decision curves indicated DL adds more clinical net benefit. With DL's assistance, both radiologists achieved higher AUCs (0.712 vs. 0.801; 0.547 vs. 0.800), improved specificities (70.93% vs. 74.42%, P < .001; 59.3% vs. 81.4%, P = .004), and decreased unnecessary biopsy rates by 6.7% and 24%. CONCLUSION DL was developed to discriminate US BI-RADS 4A lesions with a higher diagnostic power and more clinical net benefit than CE. Using DL may guide clinicians to make precise clinical decisions and avoid overtreatment of benign lesions.
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Affiliation(s)
- Mei Yi
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yue Lin
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zehui Lin
- Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Ziting Xu
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lian Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ruobing Huang
- Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Weijun Huang
- Department of Ultrasound, The First People's Hospital of Foshan, Foshan, China
| | - Nannan Wang
- Department of Ultrasound, The First People's Hospital of Foshan, Foshan, China
| | - Yanling Zuo
- Department of Ultrasound Imaging, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Nuo Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Dong Ni
- Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yanyan Zhang
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Yingjia Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China.
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Yin B, Cai Y, Teng T, Wang X, Liu X, Li X, Wang J, Wu H, He Y, Ren F, Kou T, Zhu ZJ, Zhou X. Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents. Transl Psychiatry 2024; 14:163. [PMID: 38531835 DOI: 10.1038/s41398-024-02886-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024] Open
Abstract
Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.
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Affiliation(s)
- Bangmin Yin
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaolin Wang
- Health Management Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueer Liu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Wang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongyan Wu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuqian He
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fandong Ren
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Tianzhang Kou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, China.
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Wang X, Luo J, Wang J, Cao J, Hong Y, Wen Q, Zeng Y, Shi Z, Ma G, Zhang T, Huang P. Catalytically Active Metal-Organic Frameworks Elicit Robust Immune Response to Combination Chemodynamic and Checkpoint Blockade Immunotherapy. ACS APPLIED MATERIALS & INTERFACES 2023; 15:6442-6455. [PMID: 36700645 DOI: 10.1021/acsami.2c19476] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Chemodynamic therapy (CDT) strategies rely on the generation of reactive oxygen species (ROS) to kill tumor cells, with hydroxyl radicals (•OH) serving as the key mediators of cytotoxicity in this setting. However, the efficacy of CDT approaches is often hampered by the properties of the tumor microenvironment (TME) and associated limitations to the Fenton reaction that constrains ROS generation. As such, there is a pressing need for the design of new nanoplatforms capable of improving CDT outcomes. In this study, an Fc-based metal-organic framework (MOF) vitamin k3 (Vk3)-loaded cascade catalytic nanoplatform (Vk3@Co-Fc) was developed. This platform was capable of undergoing TME-responsive degradation without impacting normal cells. After its release, Vk3 was processed by nicotinamide adenine dinucleotide hydrogen phosphate (NAD(P)H) quinone oxidoreductase-1 (NQO1), which is highly expressed in tumor cells, thereby yielding large quantities of H2O2 that in turn interact with Fe ions via the Fenton reaction to facilitate in situ cytotoxic •OH production. This process leads to immunogenic cell death (ICD) of the tumor, which then promotes dendritic cell maturation and ultimately increases T cell infiltration into the tumor site. When this nanoplatform was combined with programmed death 1 (PD-1) checkpoint blockade approaches, it was sufficient to enhance tumor-associated immune responses in breast cancer as evidenced by increases in the frequencies of CD45+ leukocytes and CD8+ cytotoxic T lymphocytes, thereby inhibiting tumor metastasis to the lungs and improving murine survival outcomes. Together, this Vk3@Co-Fc cascading catalytic nanoplatform enables potent cancer immunotherapy for breast cancer regression and metastasis prevention.
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Affiliation(s)
- Xue Wang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Jiali Luo
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Jing Wang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Jing Cao
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Yurong Hong
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Qing Wen
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Yiqing Zeng
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Zhan Shi
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Guangrong Ma
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Tao Zhang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
| | - Pintong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou310009, P. R. China
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou310009, P. R. China
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Lin ZM, Wang TT, Zhu JY, Xu YY, Chen F, Huang PT. A nomogram based on combining clinical features and contrast enhanced ultrasound is not able to identify Her-2 over-expressing cancer from other breast cancers. Front Oncol 2023; 13:1035645. [PMID: 36776315 PMCID: PMC9909531 DOI: 10.3389/fonc.2023.1035645] [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: 09/03/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023] Open
Abstract
Objective The aim of this study was to evaluate whether a predictive model based on a contrast enhanced ultrasound (CEUS)-based nomogram and clinical features (Clin) could differentiate Her-2-overexpressing breast cancers from other breast cancers. Methods A total of 152 pathology-proven breast cancers including 55 Her-2-overexpressing cancers and 97 other cancers from two units that underwent preoperative CEUS examination, were included and divided into training (n = 102) and validation cohorts (n = 50). Multivariate regression analysis was utilized to identify independent indicators for developing predictive nomogram models. The area under the receiver operating characteristic (AUC) curve was also calculated to establish the diagnostic performance of different predictive models. The corresponding sensitivities and specificities of different models at the cutoff nomogram value were compared. Results In the training cohort, 7 clinical features (menstruation, larger tumor size, higher CA153 level, BMI, diastolic pressure, heart rate and outer upper quarter (OUQ)) + enlargement in CEUS with P < 0.2 according to the univariate analysis were submitted to the multivariate analysis. By incorporating clinical information and enlargement on the CEUS pattern, independently significant indicators for Her-2-overexpression were used for further predictive modeling as follows: Model I, nomogram model based on clinical features (Clin); Model II, nomogram model combining enlargement (Clin + Enlargement); Model III, nomogram model based on typical clinical features combining enlargement (MC + BMI + diastolic pressure (DP) + outer upper quarter (OUQ) + Enlargement). Model II achieved an AUC value of 0.776 at nomogram cutoff score value of 190, which was higher than that of the other models in the training cohort without significant differences (all P>0.05). In the test cohort, the diagnostic efficiency of predictive model was poor (all AUC<0.6). In addition, the sensitivity and specificity were not significantly different between Models I and II (all P>0.05), in either the training or the test cohort. In addition, Clin exhibited an AUC similar to that of model III (P=0.12). Moreover, model III exhibited a higher sensitivity (70.0%) than the other models with similar AUC and specificity, only in the test cohort. Conclusion The main finding of the study was that the predictive model based on a CEUS-based nomogram and clinical features could not differentiate Her-2-overexpressing breast cancers from other breast cancers.
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Affiliation(s)
- Zi-mei Lin
- Department of Ultrasound in Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Ting-ting Wang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Jun-Yan Zhu
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Yong-yuan Xu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Fen Chen
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Pin-tong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China,*Correspondence: Pin-tong Huang,
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Chen Y, Lu J, Li J, Liao J, Huang X, Zhang B. Evaluation of diagnostic efficacy of multimode ultrasound in BI-RADS 4 breast neoplasms and establishment of a predictive model. Front Oncol 2022; 12:1053280. [PMID: 36505867 PMCID: PMC9730703 DOI: 10.3389/fonc.2022.1053280] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
Objectives To explore the diagnostic efficacy of ultrasound (US), two-dimensional and three-dimensional shear-wave elastography (2D-SWE and 3D-SWE), and contrast-enhanced ultrasound (CEUS) in breast neoplasms in category 4 based on the Breast Imaging Reporting and Data System (BI-RADS) from the American College of Radiology (ACR) and to develop a risk-prediction nomogram based on the optimal combination to provide a reference for the clinical management of BI-RADS 4 breast neoplasms. Methods From September 2021 to April 2022, a total of 104 breast neoplasms categorized as BI-RADS 4 by US were included in this prospective study. There were 78 breast neoplasms randomly assigned to the training cohort; the area under the receiver-operating characteristic curve (AUC), 95% confidence interval (95% CI), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 2D-SWE, 3D-SWE, CEUS, and their combination were analyzed and compared. The optimal combination was selected to develop a risk-prediction nomogram. The performance of the nomogram was assessed by a validation cohort of 26 neoplasms. Results Of the 78 neoplasms in the training cohort, 16 were malignant and 62 were benign. Among the 26 neoplasms in the validation cohort, 6 were malignant and 20 were benign. The AUC values of 2D-SWE, 3D-SWE, and CEUS were not significantly different. After a comparison of the different combinations, 2D-SWE+CEUS showed the optimal performance. Least absolute shrinkage and selection operator (LASSO) regression was used to filter the variables in this combination, and the variables included Emax, Eratio, enhancement mode, perfusion defect, and area ratio. Then, a risk-prediction nomogram with BI-RADS was built. The performance of the nomogram was better than that of the radiologists in the training cohort (AUC: 0.974 vs. 0.863). In the validation cohort, there was no significant difference in diagnostic accuracy between the nomogram and the experienced radiologists (AUC: 0.946 vs. 0.842). Conclusions US, 2D-SWE, 3D-SWE, CEUS, and their combination could improve the diagnostic efficiency of BI-RADS 4 breast neoplasms. The diagnostic efficacy of US+3D-SWE was not better than US+2D-SWE. US+2D-SWE+CEUS showed the optimal diagnostic performance. The nomogram based on US+2D-SWE+CEUS performs well.
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Chen J, Ma J, Li C, Shao S, Su Y, Wu R, Yao M. Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis. Front Oncol 2022; 12:1027784. [PMID: 36465370 PMCID: PMC9714455 DOI: 10.3389/fonc.2022.1027784] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/01/2022] [Indexed: 12/31/2023] Open
Abstract
OBJECTIVES To develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS). MATERIALS AND METHODS This retrospective study included 674 patients with 674 breast lesions. The data, a main and an independent datasets, were divided into three cohorts. Cohort 1 (80% of the main dataset; n = 448) was analyzed by logistic regression analysis to identify risk factors and establish the predictive model. The area under the receiver operating characteristic curve (AUC) was analyzed in Cohort 2 (20% of the main dataset; n = 119) to validate and in Cohort 3 (the independent dataset; n = 107) to evaluate the predictive model. RESULTS Multivariable regression analysis revealed nine independent breast cancer risk factors, including age > 40 years; ill-defined margin, heterogeneity, rich blood flow, and abnormal axillary lymph nodes on US; enhanced area enlargement, contrast agent retention, and irregular shape on CEUS; mean SWE higher than the cutoff value (P < 0.05 for all). The diagnostic performance of the model was good, with AUC values of 0.847, 0.857, and 0.774 for Cohorts 1, 2, and 3, respectively. The model increased the diagnostic specificity (from 31% to 81.3% and 7.3% to 73.1% in cohorts 2 and 3, respectively) without a significant loss in sensitivity (from 100.0% to 90.1% and 100.0% to 81.8% in cohorts 2 and 3, respectively). CONCLUSION The multi-parameter US-based model showed good performance in breast cancer diagnosis, improving specificity without a significant loss in sensitivity. Using the model could reduce unnecessary biopsies and guide clinical diagnosis and treatment.
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Affiliation(s)
| | | | | | | | | | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minghua Yao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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