1
|
Cheng M, Zhang H, Huang W, Li F, Gao J. Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn's Disease and Intestinal Tuberculosis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1516-1528. [PMID: 38424279 PMCID: PMC11300798 DOI: 10.1007/s10278-024-01059-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/17/2024] [Accepted: 02/21/2024] [Indexed: 03/02/2024]
Abstract
This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman's correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.
Collapse
Affiliation(s)
- Ming Cheng
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
| | - Hanyue Zhang
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, 100034, China
| | - Fei Li
- School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, China
| | - Jianbo Gao
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| |
Collapse
|
2
|
Bette S, Canalini L, Feitelson LM, Woźnicki P, Risch F, Huber A, Decker JA, Tehlan K, Becker J, Wollny C, Scheurig-Münkler C, Wendler T, Schwarz F, Kroencke T. Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography. Diagnostics (Basel) 2024; 14:718. [PMID: 38611632 PMCID: PMC11011980 DOI: 10.3390/diagnostics14070718] [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: 02/02/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.
Collapse
Affiliation(s)
- Stefanie Bette
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Luca Canalini
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Laura-Marie Feitelson
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Piotr Woźnicki
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, University of Würzburg, 97080 Würzburg, Germany;
| | - Franka Risch
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Adrian Huber
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Josua A. Decker
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Kartikay Tehlan
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Judith Becker
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Claudia Wollny
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Christian Scheurig-Münkler
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Thomas Wendler
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
- Institute of Digital Health, University Hospital Augsburg, Faculty of Medicine, University of Augsburg, 86356 Neusaess, Germany
- Computer-Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, 85748 Garching bei Muenchen, Germany
| | - Florian Schwarz
- Centre for Diagnostic Imaging and Interventional Therapy, Donau-Isar-Klinikum, 94469 Deggendorf, Germany;
| | - Thomas Kroencke
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
- Centre for Advanced Analytics and Predictive Sciences (CAAPS), University of Augsburg, 86159 Augsburg, Germany
| |
Collapse
|
3
|
Berbís MÁ, Godino FP, Rodríguez-Comas J, Nava E, García-Figueiras R, Baleato-González S, Luna A. Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application. Abdom Radiol (NY) 2024; 49:322-340. [PMID: 37889265 DOI: 10.1007/s00261-023-04071-0] [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: 06/12/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/28/2023]
Abstract
Radiomics allows the extraction of quantitative imaging features from clinical magnetic resonance imaging (MRI) and computerized tomography (CT) studies. The advantages of radiomics have primarily been exploited in oncological applications, including better characterization and staging of oncological lesions and prediction of patient outcomes and treatment response. The potential introduction of radiomics in the clinical setting requires the establishment of a standardized radiomics pipeline and a quality assurance program. Radiomics and texture analysis of the liver have improved the differentiation of hypervascular lesions such as adenomas, focal nodular hyperplasia, and hepatocellular carcinoma (HCC) during the arterial phase, and in the pretreatment determination of HCC prognostic factors (e.g., tumor grade, microvascular invasion, Ki-67 proliferation index). Radiomics of pancreatic CT and MR images has enhanced pancreatic ductal adenocarcinoma detection and its differentiation from pancreatic neuroendocrine tumors, mass-forming chronic pancreatitis, or autoimmune pancreatitis. Radiomics can further help to better characterize incidental pancreatic cystic lesions, accurately discriminating benign from malignant intrapancreatic mucinous neoplasms. Nonetheless, despite their encouraging results and exciting potential, these tools have yet to be implemented in the clinical setting. This non-systematic review will describe the essential steps in the implementation of the radiomics and feature extraction workflow from liver and pancreas CT and MRI studies for their potential clinical application. A succinct overview of reported radiomics applications in the liver and pancreas and the challenges and limitations of their implementation in the clinical setting is also discussed, concluding with a brief exploration of the future perspectives of radiomics in the gastroenterology field.
Collapse
Affiliation(s)
- M Álvaro Berbís
- Department of Radiology, HT Médica, San Juan de Dios Hospital, 14960, Córdoba, Spain.
- Department of Radiology, HT Médica, San Juan de Dios Hospital, Av. del Brillante, 106, 14012, Córdoba, Spain.
| | | | | | - Enrique Nava
- Department of Communications Engineering, University of Málaga, 29016, Málaga, Spain
| | - Roberto García-Figueiras
- Abdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain
| | - Sandra Baleato-González
- Abdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain
| | - Antonio Luna
- Department of Radiology, HT Médica, Clínica las Nieves, 23007, Jaén, Spain
| |
Collapse
|
4
|
Zhu X, Ye DD, Wang JH, Li J, Liu SW. Diagnostic performance of texture analysis in the differential diagnosis of perianal fistulising Crohn’s disease and glandular anal fistula. World J Gastrointest Surg 2023; 15:882-891. [PMID: 37342861 PMCID: PMC10277959 DOI: 10.4240/wjgs.v15.i5.882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/16/2023] [Accepted: 03/30/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Perianal fistulising Crohn's disease (PFCD) and glandular anal fistula have many similarities on conventional magnetic resonance imaging. However, many patients with PFCD show concomitant active proctitis, but only few patients with glandular anal fistula have active proctitis.
AIM To explore the value of differential diagnosis of PFCD and glandular anal fistula by comparing the textural feature parameters of the rectum and anal canal in fat suppression T2-weighted imaging (FS-T2WI).
METHODS Patients with rectal water sac implantation were screened from the first part of this study (48 patients with PFCD and 22 patients with glandular anal fistula). Open-source software ITK-SNAP (Version 3.6.0, http://www.itksnap.org/) was used to delineate the region of interest (ROI) of the entire rectum and anal canal wall on every axial section, and then the ROIs were input in the Analysis Kit software (version V3.0.0.R, GE Healthcare) to calculate the textural feature parameters. Textural feature parameter differences of the rectum and anal canal wall between the PFCD group vs the glandular anal fistula group were analyzed using Mann-Whitney U test. The redundant textural parameters were screened by bivariate Spearman correlation analysis, and binary logistic regression analysis was used to establish the model of textural feature parameters. Finally, diagnostic accuracy was assessed by receiver operating characteristic-area under the curve (AUC) analysis.
RESULTS In all, 385 textural parameters were obtained, including 37 parameters with statistically significant differences between the PFCD and glandular anal fistula groups. Then, 16 texture feature parameters remained after bivariate Spearman correlation analysis, including one histogram parameter (Histogram energy); four grey level co-occurrence matrix (GLCM) parameters (GLCM energy_all direction_offset1_SD, GLCM entropy_all direction_ offset4_SD, GLCM entropy_all direction_offset7_SD, and Haralick correlation_all direction_ offset7_SD); four texture parameters (Correlation_all direction_offset1_SD, cluster prominence _angle 90_offset4, Inertia_all direction_offset7_SD, and cluster shade_angle 45_offset7); five grey level run-length matrix parameters (grey level nonuniformity_angle 90_offset1, grey level nonuniformity_all direction_offset4_SD, long run high grey level emphasis_all direction_offset1_SD, long run emphasis_all direction_ offset4_ SD, and long run high grey level emphasis_all direction_offset4_SD); and two form factor parameters (surface area and maximum 3D diameter). The AUC, sensitivity, and specificity of the model of textural feature parameters were 0.917, 85.42%, and 86.36%, respectively.
CONCLUSION The model of textural feature parameters showed good diagnostic performance for PFCD. The texture feature parameters of the rectum and anal canal in FS-T2WI are helpful to distinguish PFCD from glandular anal fistula.
Collapse
Affiliation(s)
- Xin Zhu
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Dan-Dan Ye
- Department of Radiology, Quanzhou Orthopedic-Traumatological Hospital, Quanzhou 362000, Fujian Province, China
| | - Jian-Hua Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Jing Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Shao-Wei Liu
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| |
Collapse
|
5
|
Yao Y, Chen Y, Gou S, Chen S, Zhang X, Tong N. Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
6
|
Shao C, Zhang J, Guo J, Zhang L, Zhang Y, Ma L, Gong C, Tian Y, Chen J, Yu N. A radiomics nomogram model for predicting prognosis of pancreatic ductal adenocarcinoma after high-intensity focused ultrasound surgery. Int J Hyperthermia 2023; 40:2184397. [PMID: 36888994 DOI: 10.1080/02656736.2023.2184397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023] Open
Abstract
OBJECTIVE To develop and validate a radiomics nomogram for predicting the survival of patients with pancreatic ductal adenocarcinoma (PDAC) after receiving high-intensity focused ultrasound (HIFU) treatment. METHODS A total of 52 patients with PDAC were enrolled. To select features, the least absolute shrinkage and selection operator algorithm were applied, and the radiomics score (Rad-Score) was obtained. Radiomics model, clinics model, and radiomics nomogram model were constructed by multivariate regression analysis. The identification, calibration, and clinical application of nomogram were evaluated. Survival analysis was performed using Kaplan-Meier (K-M) method. RESULTS According to conclusions made from the multivariate Cox model, Rad-Score, and tumor size were independent risk factors for OS. Compared with the clinical model and radiomics model, the combination of Rad-Score and clinicopathological factors could better predict the survival of patients. Patients were divided into high-risk and low-risk groups according to Rad-Score. K-M analysis showed that the difference between the two groups was statistically significant (p < 0.05). In addition, the radiomics nomogram model indicated better discrimination, calibration, and clinical practicability in training and validation cohorts. CONCLUSION The radiomics nomogram effectively evaluates the prognosis of patients with advanced pancreatic cancer after HIFU surgery, which could potentially improve treatment strategies and promote individualized treatment of advanced pancreatic cancer.
Collapse
Affiliation(s)
- Changjie Shao
- Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | | | - Jing Guo
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Liang Zhang
- Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yuhan Zhang
- University of Southern California, Los Angeles, CA, USA
| | - Leiyuan Ma
- Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chuanxin Gong
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yaqi Tian
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jingjing Chen
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ning Yu
- Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| |
Collapse
|
7
|
Chen Z, Wang Y, Zhang H, Yin H, Hu C, Huang Z, Tan Q, Song B, Deng L, Xia Q. Deep Learning Models for Severity Prediction of Acute Pancreatitis in the Early Phase From Abdominal Nonenhanced Computed Tomography Images. Pancreas 2023; 52:e45-e53. [PMID: 37378899 DOI: 10.1097/mpa.0000000000002216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
OBJECTIVES To develop and validate deep learning (DL) models for predicting the severity of acute pancreatitis (AP) by using abdominal nonenhanced computed tomography (CT) images. METHODS The study included 978 AP patients admitted within 72 hours after onset and performed abdominal CT on admission. The image DL model was built by the convolutional neural networks. The combined model was developed by integrating CT images and clinical markers. The performance of the models was evaluated by using the area under the receiver operating characteristic curve. RESULTS The clinical, Image DL, and the combined DL models were developed in 783 AP patients and validated in 195 AP patients. The combined models possessed the predictive accuracy of 90.0%, 32.4%, and 74.2% for mild, moderately severe, and severe AP. The combined DL model outperformed clinical and image DL models with 0.820 (95% confidence interval, 0.759-0.871), the sensitivity of 84.76% and the specificity of 66.67% for predicting mild AP and the area under the receiver operating characteristic curve of 0.920 (95% confidence interval, 0.873-0.954), the sensitivity of 90.32%, and the specificity of 82.93% for predicting severe AP. CONCLUSIONS The DL technology allows nonenhanced CT images as a novel tool for predicting the severity of AP.
Collapse
Affiliation(s)
- Zhiyao Chen
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Wang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Huiling Zhang
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Cheng Hu
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qingyuan Tan
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | | | - Lihui Deng
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Xia
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
8
|
A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability. Insights Imaging 2022; 13:139. [PMID: 35986798 PMCID: PMC9391628 DOI: 10.1186/s13244-022-01279-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/26/2022] [Indexed: 12/16/2022] Open
Abstract
Background Multiple tools have been applied to radiomics evaluation, while evidence rating tools for this field are still lacking. This study aims to assess the quality of pancreatitis radiomics research and test the feasibility of the evidence level rating tool. Results Thirty studies were included after a systematic search of pancreatitis radiomics studies until February 28, 2022, via five databases. Twenty-four studies employed radiomics for diagnostic purposes. The mean ± standard deviation of the adherence rate was 38.3 ± 13.3%, 61.3 ± 11.9%, and 37.1 ± 27.2% for the Radiomics Quality Score (RQS), the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guideline for preprocessing steps, respectively. The median (range) of RQS was 7.0 (− 3.0 to 18.0). The risk of bias and application concerns were mainly related to the index test according to the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The meta-analysis on differential diagnosis of autoimmune pancreatitis versus pancreatic cancer by CT and mass-forming pancreatitis versus pancreatic cancer by MRI showed diagnostic odds ratios (95% confidence intervals) of, respectively, 189.63 (79.65–451.48) and 135.70 (36.17–509.13), both rated as weak evidence mainly due to the insufficient sample size. Conclusions More research on prognosis of acute pancreatitis is encouraged. The current pancreatitis radiomics studies have insufficient quality and share common scientific disadvantages. The evidence level rating is feasible and necessary for bringing the field of radiomics from preclinical research area to clinical stage. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01279-4.
Collapse
|
9
|
Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers (Basel) 2022; 14:cancers14143498. [PMID: 35884559 PMCID: PMC9316850 DOI: 10.3390/cancers14143498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed. Abstract Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
Collapse
Affiliation(s)
- Megan Schuurmans
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Natália Alves
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Pierpaolo Vendittelli
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - John Hermans
- Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands;
| |
Collapse
|
10
|
Yan G, Yan G, Li H, Liang H, Peng C, Bhetuwal A, McClure MA, Li Y, Yang G, Li Y, Zhao L, Fan X. Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review. Front Med (Lausanne) 2022; 9:922299. [PMID: 35814756 PMCID: PMC9259974 DOI: 10.3389/fmed.2022.922299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
Abstract
Radiomics involves high-throughput extraction and analysis of quantitative information from medical images. Since it was proposed in 2012, there are some publications on the application of radiomics for (1) predicting recurrent acute pancreatitis (RAP), clinical severity of acute pancreatitis (AP), and extrapancreatic necrosis in AP; (2) differentiating mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC), focal autoimmune pancreatitis (AIP) from PDAC, and functional abdominal pain (functional gastrointestinal diseases) from RAP and chronic pancreatitis (CP); and (3) identifying CP and normal pancreas, and CP risk factors and complications. In this review, we aim to systematically summarize the applications and progress of radiomics in pancreatitis and it associated situations, so as to provide reference for related research.
Collapse
Affiliation(s)
- Gaowu Yan
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Gaowen Yan
- Department of Radiology, The First Hospital of Suining, Suining, China
| | - Hongwei Li
- Department of Radiology, The Third Hospital of Mianyang and Sichuan Mental Health Center, Mianyang, China
| | - Hongwei Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chen Peng
- Department of Gastroenterology, The First Hospital of Suining, Suining, China
| | - Anup Bhetuwal
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Morgan A. McClure
- Department of Radiology and Imaging, Institute of Rehabilitation and Development of Brain Function, The Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Yongmei Li
| | - Guoqing Yang
- Department of Radiology, Suining Central Hospital, Suining, China
- Guoqing Yang
| | - Yong Li
- Department of Radiology, Suining Central Hospital, Suining, China
- Yong Li
| | - Linwei Zhao
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Xiaoping Fan
- Department of Radiology, Suining Central Hospital, Suining, China
| |
Collapse
|
11
|
Tong T, Gu J, Xu D, Song L, Zhao Q, Cheng F, Yuan Z, Tian S, Yang X, Tian J, Wang K, Jiang T. Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis. BMC Med 2022; 20:74. [PMID: 35232446 PMCID: PMC8889703 DOI: 10.1186/s12916-022-02258-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/13/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Accurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary puncture and surgery. This study aimed to develop a deep learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) images to assist radiologists in identifying PDAC and CP. METHODS Patients with PDAC or CP were retrospectively enrolled from three hospitals. Detailed clinicopathological data were collected for each patient. Diagnoses were confirmed pathologically using biopsy or surgery in all patients. We developed an end-to-end DLR model for diagnosing PDAC and CP using CEUS images. To verify the clinical application value of the DLR model, two rounds of reader studies were performed. RESULTS A total of 558 patients with pancreatic lesions were enrolled and were split into the training cohort (n=351), internal validation cohort (n=109), and external validation cohorts 1 (n=50) and 2 (n=48). The DLR model achieved an area under curve (AUC) of 0.986 (95% CI 0.975-0.994), 0.978 (95% CI 0.950-0.996), 0.967 (95% CI 0.917-1.000), and 0.953 (95% CI 0.877-1.000) in the training, internal validation, and external validation cohorts 1 and 2, respectively. The sensitivity and specificity of the DLR model were higher than or comparable to the diagnoses of the five radiologists in the three validation cohorts. With the aid of the DLR model, the diagnostic sensitivity of all radiologists was further improved at the expense of a small or no decrease in specificity in the three validation cohorts. CONCLUSIONS The findings of this study suggest that our DLR model can be used as an effective tool to assist radiologists in the diagnosis of PDAC and CP.
Collapse
Affiliation(s)
- Tong Tong
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jionghui Gu
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Dong Xu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No.1 East Banshan Road, Gongshu District, Hangzhou, 310022, China
| | - Ling Song
- Department of ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiyu Zhao
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Fang Cheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No.1 East Banshan Road, Gongshu District, Hangzhou, 310022, China
| | - Zhiqiang Yuan
- Department of ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Shuyuan Tian
- Department of Ultrasound, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China
| | - Xin Yang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China.
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Tian'an Jiang
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.
- Zhejiang Provincial Key Laboratory of Pulsed Electric Field Technology for Medical Transformation, Hangzhou, 310003, China.
| |
Collapse
|
12
|
Ren S, Tang HJ, Zhao R, Duan SF, Chen R, Wang ZQ. Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma. Curr Med Sci 2022; 42:217-225. [PMID: 35089491 DOI: 10.1007/s11596-022-2535-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 06/28/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method. RESULTS In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80). CONCLUSION The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.
Collapse
Affiliation(s)
- Shuai Ren
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA.
| | - Hui-Juan Tang
- Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
- Department of Clinical and Molecular Sciences, Marche Polytechnic University, Ancona, 60126, Italy
| | - Rui Zhao
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | | | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA
| | - Zhong-Qiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
| |
Collapse
|
13
|
Ma X, Wang YR, Zhuo LY, Yin XP, Ren JL, Li CY, Xing LH, Zheng TT. Retrospective Analysis of the Value of Enhanced CT Radiomics Analysis in the Differential Diagnosis Between Pancreatic Cancer and Chronic Pancreatitis. Int J Gen Med 2022; 15:233-241. [PMID: 35023961 PMCID: PMC8747707 DOI: 10.2147/ijgm.s337455] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose To investigate the feasibility of enhanced computed tomography (CT) radiomics analysis to differentiate between pancreatic cancer (PC) and chronic pancreatitis. Methods and materials The CT images of 151 PCs and 24 chronic pancreatitis were retrospectively analyzed in the three-dimensional regions of interest on arterial phase (AP) and venous phase (VP) and segmented by MITK software. A multivariable logistic regression model was established based on the selected radiomics features. The radiomics score was calculated, and the nomogram was established. The discrimination of each model was analyzed by the receiver operating characteristic curve (ROC). Decision curve analysis (DCA) was used to evaluate clinical utility. The precision recall curve (PRC) was used to evaluate whether the model is affected by data imbalance. The Delong test was adopted to compare the diagnostic efficiency of each model. Results Significant differences were observed in the distribution of gender (P = 0.034), carbohydrate antigen 19-9 (P < 0.001), and carcinoembryonic antigen (P < 0.001) in patients with PC and chronic pancreatitis. The area under the ROC curve (AUC) value of AP multivariate regression model, VP multivariate regression model, AP combined with VP features model (Radiomics), clinical feature model, and radiomics combined with clinical feature model (COMB) was 0.905, 0.941, 0.941, 0.822, and 0.980, respectively. The sensitivity and specificity of the COMB model were 0.947 and 0.917, respectively. The results of DCA showed that the COMB model exhibited net clinical benefits and PRC shows that COMB model have good precision and recall (sensitivity). Conclusion The COMB model could be a potential tool to distinguish PC from chronic pancreatitis and aid in clinical decisions.
Collapse
Affiliation(s)
- Xi Ma
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, Hebei Province, 071000, People's Republic of China
| | - Yu-Rui Wang
- Department of Computed Tomography, Tangshan Gongren Hospital, Tangshan, Hebei Province, 063000, People's Republic of China
| | - Li-Yong Zhuo
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, Hebei Province, 071000, People's Republic of China
| | - Xiao-Ping Yin
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, Hebei Province, 071000, People's Republic of China
| | - Jia-Liang Ren
- GE Healthcare[Shanghai] Co Ltd, Shanghai, 210000, People's Republic of China
| | - Cai-Ying Li
- Department of Radiology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050000, People's Republic of China
| | - Li-Hong Xing
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, Hebei Province, 071000, People's Republic of China
| | - Tong-Tong Zheng
- Department of Radiology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050000, People's Republic of China
| |
Collapse
|
14
|
Casà C, Piras A, D’Aviero A, Preziosi F, Mariani S, Cusumano D, Romano A, Boskoski I, Lenkowicz J, Dinapoli N, Cellini F, Gambacorta MA, Valentini V, Mattiucci GC, Boldrini L. The impact of radiomics in diagnosis and staging of pancreatic cancer. Ther Adv Gastrointest Endosc 2022; 15:26317745221081596. [PMID: 35342883 PMCID: PMC8943316 DOI: 10.1177/26317745221081596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 02/02/2022] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was 'radiomics [All Fields] AND ("pancreas" [MeSH Terms] OR "pancreas" [All Fields] OR "pancreatic" [All Fields])' and only original articles referred to PC in humans in the English language were considered. RESULTS A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction. DISCUSSION Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.
Collapse
Affiliation(s)
- Calogero Casà
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | | | - Francesco Preziosi
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Mariani
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Davide Cusumano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Angela Romano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy
| | - Jacopo Lenkowicz
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Nicola Dinapoli
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Francesco Cellini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gian Carlo Mattiucci
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| |
Collapse
|
15
|
Gao J, Han F, Wang X, Duan S, Zhang J. Multi-Phase CT-Based Radiomics Nomogram for Discrimination Between Pancreatic Serous Cystic Neoplasm From Mucinous Cystic Neoplasm. Front Oncol 2021; 11:699812. [PMID: 34926238 PMCID: PMC8672034 DOI: 10.3389/fonc.2021.699812] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 11/15/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose This study aimed to develop and verify a multi-phase (MP) computed tomography (CT)-based radiomics nomogram to differentiate pancreatic serous cystic neoplasms (SCNs) from mucinous cystic neoplasms (MCNs), and to compare the diagnostic efficacy of radiomics models for different phases of CT scans. Materials and Methods A total of 170 patients who underwent surgical resection between January 2011 and December 2018, with pathologically confirmed pancreatic cystic neoplasms (SCN=115, MCN=55) were included in this single-center retrospective study. Radiomics features were extracted from plain scan (PS), arterial phase (AP), and venous phase (VP) CT scans. Algorithms were performed to identify the optimal features to build a radiomics signature (Radscore) for each phase. All features from these three phases were analyzed to develop the MP-Radscore. A combined model comprised the MP-Radscore and imaging features from which a nomogram was developed. The accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration tests, and decision curve analysis. Results For each scan phase, 1218 features were extracted, and the optimal ones were selected to construct the PS-Radscore (11 features), AP-Radscore (11 features), and VP-Radscore (12 features). The MP-Radscore (14 features) achieved better performance based on ROC curve analysis than any single phase did [area under the curve (AUC), training cohort: MP-Radscore 0.89, PS-Radscore 0.78, AP-Radscore 0.83, VP-Radscore 0.85; validation cohort: MP-Radscore 0.88, PS-Radscore 0.77, AP-Radscore 0.83, VP-Radscore 0.84]. The combination nomogram performance was excellent, surpassing those of all other nomograms in both the training cohort (AUC, 0.91) and validation cohort (AUC, 0.90). The nomogram also performed well in the calibration and decision curve analyses. Conclusions Radiomics for arterial and venous single-phase models outperformed the plain scan model. The combination nomogram that incorporated the MP-Radscore, tumor location, and cystic number had the best discriminatory performance and showed excellent accuracy for differentiating SCN from MCN.
Collapse
Affiliation(s)
- Jiahao Gao
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China.,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Fang Han
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China.,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoshuang Wang
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China
| | - Shaofeng Duan
- Department of Life Sciences, GE Healthcare, Shanghai, China
| | - Jiawen Zhang
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China.,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
16
|
Preoperative differentiation of serous cystic neoplasms from mucin-producing pancreatic cystic neoplasms using a CT-based radiomics nomogram. Abdom Radiol (NY) 2021; 46:2637-2646. [PMID: 33558952 DOI: 10.1007/s00261-021-02954-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/05/2021] [Accepted: 01/13/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To develop and validate a CT-based radiomics nomogram in preoperative differential diagnosis of SCNs from mucin-producing PCNs. MATERIAL AND METHODS A total of 89 patients consisting of 31 SCNs, 30 IPMNs, and 28 MCNs who underwent preoperative CT were analyzed. A total of 710 radiomics features were extracted from each case. Patients were divided into training (n = 63) and validation cohorts (n = 26) with a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) method and logistic regression analysis were used for feature selection and model construction. A nomogram was created from a comprehensive model consisting of clinical features and the fusion radiomics signature. A decision curve analysis was used for clinical decisions. RESULTS The radiomics features extracted from CT could assist with the differentiation of SCNs from mucin-producing PCNs in both the training and validation cohorts. The signature of the combination of the plain, late arterial, and venous phases had the largest areas under the curve (AUCs) of 0.960 (95% CI 0.910-1) in the training cohort and 0.817 (95% CI 0.651-0.983) in the validation cohort with good calibration. The value and efficacy of the nomogram was verified using decision curve analysis. CONCLUSION A comprehensive nomogram incorporating clinical features and fusion radiomics signature can differentiate SCNs from mucin-producing PCNs.
Collapse
|
17
|
Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021; 13:cancers13112522. [PMID: 34063937 PMCID: PMC8196591 DOI: 10.3390/cancers13112522] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Part I is an overview aimed to investigate some technical principles and the main fields of radiomic application in gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy in gastrointestinal cancers, describing mostly the results for each pre-eminent tumor. In particular, this paper provides a general description of the main radiomic drawbacks and future challenges, which limit radiomic application in clinical setting as routine. Further investigations need to standardize and validate the Radiomics as a helpful tool in management of oncologic patients. In that context, Radiomics has been playing a relevant role and could be considered as a future imaging landscape. Abstract Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.
Collapse
Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome-Umberto I University Hospital, Viale del Policlinico, 155, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-063-377-5285
| |
Collapse
|
18
|
Huang WP, Liu SY, Han YJ, Li LM, Liang P, Gao JB. Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm. Front Oncol 2021; 11:677814. [PMID: 34079766 PMCID: PMC8166224 DOI: 10.3389/fonc.2021.677814] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 04/27/2021] [Indexed: 12/11/2022] Open
Abstract
Purpose It is challenging for traditional CT signs to predict invasiveness of pancreatic solid pseudopapillary neoplasm (pSPN). We aim to develop and evaluate CT-based radiomics signature to preoperatively predict invasive behavior in pSPN. Methods Eighty-five patients who had pathologically confirmed pSPN and preoperative contrasted-enhanced CT imaging in our hospital were retrospectively analyzed (invasive: 24; non-invasive: 61). 1316 radiomics features were separately extracted from delineated 2D or 3D ROIs in arterial and venous phases. 200% (SMOTE) was used to generate balanced dataset (invasive: 72, non-invasive: 96) for each phase, which was for feature selection and modeling. The model was internally validated in the original dataset. Inter-observer consistency analysis, spearman correlation, univariate analysis, LASSO regression and backward stepwise logical regression were mainly applied to screen the features, and 6 logistic regression models were established based on multi-phase features from 2D or 3D segmentations. The ROC analysis and Delong's test were mainly used for model assessment and AUC comparison. Results It retained 11, 8, 7 and 7 features to construct 3D-arterial, 3D-venous, 2D-arterial and 2D-venous model. Based on 3D ROIs, the arterial model (AUC: 0.914) performed better than venous (AUC: 0.815) and the arterial-venous combined model was slightly improved (AUC: 0.918). Based on 2D ROIs, the arterial model (AUC: 0.814) performed better than venous (AUC:0.768), while the arterial-venous combined model (AUC:0.893) performed better than any single-phase model. In addition, the 3D arterial model performed better than the best combined 2D model. The Delong's test showed that the significant difference of model AUC existed in arterial models in original dataset (p = 0.019) while not in arterial-venous combined model (p=0.49) as comparing 2D and 3D ROIs. Conclusion The arterial radiomics model constructed by 3D-ROI feature is potential to predict the invasiveness of pSPN preoperatively.
Collapse
Affiliation(s)
- Wen-Peng Huang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Si-Yun Liu
- Pharmaceutical Diagnostics, General Electric Company (GE) Healthcare, Beijing, China
| | - Yi-Jing Han
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Li-Ming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| |
Collapse
|
19
|
Chen PT, Chang D, Wu T, Wu MS, Wang W, Liao WC. Applications of artificial intelligence in pancreatic and biliary diseases. J Gastroenterol Hepatol 2021; 36:286-294. [PMID: 33624891 DOI: 10.1111/jgh.15380] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 12/09/2020] [Accepted: 12/12/2020] [Indexed: 12/11/2022]
Abstract
The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.
Collapse
Affiliation(s)
- Po-Ting Chen
- Department of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Dawei Chang
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
| | - Tinghui Wu
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
| | - Ming-Shiang Wu
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.,Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Weichung Wang
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
| | - Wei-Chih Liao
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.,Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
| |
Collapse
|
20
|
Ren S, Zhao R, Cui W, Qiu W, Guo K, Cao Y, Duan S, Wang Z, Chen R. Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma From Pancreatic Ductal Adenocarcinoma. Front Oncol 2020; 10:1618. [PMID: 32984030 PMCID: PMC7477956 DOI: 10.3389/fonc.2020.01618] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 07/27/2020] [Indexed: 12/15/2022] Open
Abstract
Purpose The purpose was to assess the predictive ability of computed tomography (CT)-based radiomics signature in differential diagnosis between pancreatic adenosquamous carcinoma (PASC) and pancreatic ductal adenocarcinoma (PDAC). Materials and Methods Eighty-one patients (63.6 ± 8.8 years old) with PDAC and 31 patients (64.7 ± 11.1 years old) with PASC who underwent preoperative CE-CT were included. A total of 792 radiomics features were extracted from the late arterial phase (n = 396) and portal venous phase (n = 396) for each case. Significantly different features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy and maximum relevance method. A radiomics signature was constructed using random forest method, the robustness and the reliability of which was validated using 10-times leave group out cross-validation (LGOCV) method. Results Seven radiomics features from late arterial phase images and three from portal venous phase images were finally selected. The radiomics signature performed well in differential diagnosis between PASC and PDAC, with 94.5% accuracy, 98.3% sensitivity, 90.1% specificity, 91.9% positive predictive value (PPV), and 97.8% negative predictive value (NPV). Moreover, the radiomics signature was proved to be robust and reliable using the LGOCV method, with 76.4% accuracy, 91.1% sensitivity, 70.8% specificity, 56.7% PPV, and 96.2% NPV. Conclusion CT-based radiomics signature may serve as a promising non-invasive method in differential diagnosis between PASC and PDAC.
Collapse
Affiliation(s)
- Shuai Ren
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.,Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.,The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China.,Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Rui Zhao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wenjing Cui
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wenli Qiu
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.,Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Kai Guo
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.,Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yingying Cao
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.,Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | | | - Zhongqiu Wang
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.,Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| |
Collapse
|