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Zhang J, Zhu H, Shen L, Li J, Zhang X, Bai C, Zhou Z, Yu X, Li Z, Li E, Yuan X, Lou W, Chi Y, Xu N, Yin Y, Bai Y, Zhang T, Xiu D, Chen J, Qin S, Wang X, Yang Y, Shi H, Luo X, Fan S, Su W, Lu M, Xu J. Baseline radiologic features as predictors of efficacy in patients with pancreatic neuroendocrine tumors with liver metastases receiving surufatinib. Chin J Cancer Res 2023; 35:526-535. [PMID: 37969958 PMCID: PMC10643338 DOI: 10.21147/j.issn.1000-9604.2023.05.09] [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: 07/24/2023] [Accepted: 09/15/2023] [Indexed: 11/17/2023] Open
Abstract
Objective Currently, pre-treatment prediction of patients with pancreatic neuroendocrine tumors with liver metastases (PNELM) receiving surufatinib treatment was unsatisfying. Our objective was to examine the association between radiological characteristics and efficacy/prognosis. Methods We enrolled patients with liver metastases in the phase III, SANET-p trial (NCT02589821) and obtained contrast-enhanced computed tomography (CECT) images. Qualitative and quantitative parameters including hepatic tumor margins, lesion volumes, enhancement pattern, localization types, and enhancement ratios were evaluated. The progression-free survival (PFS) and hazard ratio (HR) were calculated using Cox's proportional hazard model. Efficacy was analyzed by logistic-regression models. Results Among 152 patients who had baseline CECT assessments and were included in this analysis, the surufatinib group showed statistically superior efficacy in terms of median PFS compared to placebo across various qualitative and quantitative parameters. In the multivariable analysis of patients receiving surufatinib (N=100), those with higher arterial phase standardized enhancement ratio-peri-lesion (ASER-peri) exhibited longer PFS [HR=0.039; 95% confidence interval (95% CI): 0.003-0.483; P=0.012]. Furthermore, patients with a high enhancement pattern experienced an improvement in the objective response ratio [31.3% vs. 14.7%, odds ratio (OR)=3.488; 95% CI: 1.024-11.875; P=0.046], and well-defined tumor margins were associated with a higher disease control rate (DCR) (89.3% vs. 68.2%, OR=4.535; 95% CI: 1.285-16.011; P=0.019) compared to poorly-defined margins. Conclusions These pre-treatment radiological features, namely high ASER-peri, high enhancement pattern, and well-defined tumor margins, have the potential to serve as predictive markers of efficacy in patients with PNELM receiving surufatinib.
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Affiliation(s)
- Jianwei Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Haibin Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Lin Shen
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jie Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Xiaoyan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Chunmei Bai
- Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Zhiwei Zhou
- Department of Gastric Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510062, China
| | - Xianrui Yu
- Department of Pancreatic and Hepatobiliary Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Zhiping Li
- Department of Abdominal Oncology, West China Hospital, Sichuan University, Chengdu 332001, China
| | - Enxiao Li
- Department of Medical Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Xianglin Yuan
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wenhui Lou
- Department of General Surgery, Zhongshan Hospital of Fudan University, Shanghai 200032, China
| | - Yihebali Chi
- Department of Medical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Nong Xu
- Department of Medical Oncology, the First Affiliated Hospital of Zhejiang University, Hangzhou 310009, China
| | - Yongmei Yin
- Department of Medical Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yuxian Bai
- Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Tao Zhang
- Department of Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Dianrong Xiu
- Department of General Surgery, Peking University Third Hospital, Beijing 100191, China
| | - Jia Chen
- Department of Medical Oncology, Jiangsu Cancer Hospital, Nanjing 214206, China
| | - Shukui Qin
- Cancer Center of Jinling Hospital, Nanjing University of Chinese Medicine, Nanjing 210016, China
| | - Xiuwen Wang
- Department of Medical Oncology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Yujie Yang
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Haoyun Shi
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Xian Luo
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Songhua Fan
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Weiguo Su
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Ming Lu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jianming Xu
- Department of Gastrointestinal Oncology, the Fifth Medical Center, Chinese PLA General Hospital, Beijing 100071, China
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Park YJ, Park YS, Kim ST, Hyun SH. A Machine Learning Approach Using [ 18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor. Mol Imaging Biol 2023; 25:897-910. [PMID: 37395887 DOI: 10.1007/s11307-023-01832-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023]
Abstract
PURPOSE We sought to develop and validate machine learning (ML) models for predicting tumor grade and prognosis using 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET)-based radiomics and clinical features in patients with pancreatic neuroendocrine tumors (PNETs). PROCEDURES A total of 58 patients with PNETs who underwent pretherapeutic [18F]FDG PET/computed tomography (CT) were retrospectively enrolled. PET-based radiomics extracted from segmented tumor and clinical features were selected to develop prediction models by the least absolute shrinkage and selection operator feature selection method. The predictive performances of ML models using neural network (NN) and random forest algorithms were compared by the areas under the receiver operating characteristic curves (AUROCs) and validated by stratified five-fold cross validation. RESULTS We developed two separate ML models for predicting high-grade tumors (Grade 3) and tumors with poor prognosis (disease progression within two years). The integrated models consisting of clinical and radiomic features with NN algorithm showed the best performances than the other models (stand-alone clinical or radiomics models). The performance metrics of the integrated model by NN algorithm were AUROC of 0.864 in the tumor grade prediction model and AUROC of 0.830 in the prognosis prediction model. In addition, AUROC of the integrated clinico-radiomics model with NN was significantly higher than that of tumor maximum standardized uptake model in predicting prognosis (P < 0.001). CONCLUSIONS Integration of clinical features and [18F]FDG PET-based radiomics using ML algorithms improved the prediction of high-grade PNET and poor prognosis in a non-invasive manner.
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Affiliation(s)
- Yong-Jin Park
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Nuclear Medicine, Ajou University Medical Center, Ajou University School of Medicine, 164, Worldcup-ro, Yeongtong-gu, Suwon, 16499, South Korea
| | - Young Suk Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Tae Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Hyup Hyun
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
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Staal FCR, Aalbersberg EA, van der Velden D, Wilthagen EA, Tesselaar MET, Beets-Tan RGH, Maas M. GEP-NET radiomics: a systematic review and radiomics quality score assessment. Eur Radiol 2022; 32:7278-7294. [PMID: 35882634 DOI: 10.1007/s00330-022-08996-w] [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: 02/21/2022] [Revised: 05/25/2022] [Accepted: 06/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence. METHODS PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data. RESULTS In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%. CONCLUSION Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce. KEY POINTS • The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.
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Affiliation(s)
- Femke C R Staal
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands
| | - Else A Aalbersberg
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Nuclear Medicine, The Netherlands Cancer Institute Amsterdam, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Daphne van der Velden
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Erica A Wilthagen
- Scientific Information Service, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Margot E T Tesselaar
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Medical Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,Faculty of Health Sciences, University of Southern Denmark, J. B. Winsløws Vej 19, 3, 5000, Odense, Denmark
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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Radiofrequency ablation of hepatocellular carcinoma: CT texture analysis of the ablated area to predict local recurrence. Eur J Radiol 2022; 150:110250. [DOI: 10.1016/j.ejrad.2022.110250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 02/25/2022] [Accepted: 03/07/2022] [Indexed: 11/22/2022]
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Fehrenbach U, Xin S, Hartenstein A, Auer TA, Dräger F, Froböse K, Jann H, Mogl M, Amthauer H, Geisel D, Denecke T, Wiedenmann B, Penzkofer T. Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI-A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making. Cancers (Basel) 2021; 13:2726. [PMID: 34072865 PMCID: PMC8199286 DOI: 10.3390/cancers13112726] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/22/2021] [Accepted: 05/25/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC). METHODS Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure). RESULTS Internal validation of the model's accuracy showed a high overlap for NELM and livers (Matthew's correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001). CONCLUSION The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model's measurements correlated well with MCC's evaluation of therapeutic response.
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Affiliation(s)
- Uli Fehrenbach
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
| | - Siyi Xin
- Division of Gastroenterology, Medical Department, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (S.X.); (H.J.); (B.W.)
| | - Alexander Hartenstein
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
- Bayer AG, 13353 Berlin, Germany
| | - Timo Alexander Auer
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
- Berlin Institute of Health, 10178 Berlin, Germany
| | - Franziska Dräger
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
| | - Konrad Froböse
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
| | - Henning Jann
- Division of Gastroenterology, Medical Department, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (S.X.); (H.J.); (B.W.)
| | - Martina Mogl
- Department of Surgery Campus Charité Mitte/Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany;
| | - Holger Amthauer
- Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany;
| | - Dominik Geisel
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
| | - Timm Denecke
- Department of Diagnostic and Interventional Radiology, University Hospital Leipzig, 04103 Leipzig, Germany;
| | - Bertram Wiedenmann
- Division of Gastroenterology, Medical Department, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (S.X.); (H.J.); (B.W.)
| | - Tobias Penzkofer
- Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany; (A.H.); (T.A.A.); (F.D.); (K.F.); (D.G.); (T.P.)
- Berlin Institute of Health, 10178 Berlin, Germany
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Yang X, Lei P, Song Y, Fei Z, Ai Y, Shang H, Bai T, Ye J, Li X. Quantitative CT assessment by histogram and volume ratio in pyrrolizidines alkaloids-induced hepatic sinusoidal obstruction syndrome. Eur J Radiol 2021; 138:109632. [PMID: 33711570 DOI: 10.1016/j.ejrad.2021.109632] [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: 12/12/2020] [Revised: 02/22/2021] [Accepted: 03/02/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To quantitatively assess hypoattenuation volume ratio and hepatic parenchymal hypoattenuation on contrast enhanced computed tomography (CECT) in patients with pyrrolizidines alkaloids (PAs)-induced hepatic sinusoidal obstruction syndrome (HSOS), and evaluate the correlations of the CT-based quantitative values with clinical factors. METHODS Thirty-five patients with PAs-induced HSOS who underwent CECT were retrospectively enrolled. The ratio of hypoattenuation volume to total liver volume, and changes in damaged area-to-normal liver density ratio (ΔDR) derived from histogram on portal venous phase were quantitatively measured. Heterogeneous hypoattenuation (CT score) scored by hypoattenuation volume ratio and ΔDR were calculated. The correlation between imaging findings and clinical factors was analyzed using Pearson correlation test. RESULTS Liver function tests were abnormal in most patients, the mean Hounsfield unit (HU) of damaged area (58.68 ± 17.3) was significantly lower (P < 0.001) than the corresponding normal liver (82.27 ± 23.97). Heterogeneous hypoattenuation were mild in 13 patients (37 %), moderate in 16 patients (46 %), and severe in 6 patients (17 %). ΔDR derived from histogram was positively correlated (weakly to moderately) with total bilirubin (r = 0.341, P = 0.045), direct bilirubin (r = 0.385, P = 0.022), and alkaline phosphatase (r = 0.491, P = 0.003), while such correlation was not observed in hypoattenuation volume ratio. The severity of heterogeneous hypoattenuation scored by hypoattenuation volume ratio and ΔDR was positively correlated (weakly) with prothrombin time (r = 0.357, P = 0.035), international normalized ratio (r = 0.363, P = 0.032), alkaline phosphatase (r = 0.359, P = 0.034), and model for end-stage liver disease (MELD) score (r = 0.347, P = 0.041). CONCLUSION Heterogeneous hypoattenuation scored by volume ratio and ΔDR on CECT provides a non-invasive approach in evaluating the severity of PAs-induced HSOS.
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Affiliation(s)
- Xiaoqian Yang
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Ping Lei
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yuhu Song
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Zhenyu Fei
- Siemens Shanghai Medical Equipment Ltd., Shanghai, 201318, China
| | - Yan Ai
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Haitao Shang
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Tao Bai
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Jin Ye
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Xin Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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