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Fang S, Liu Z, Qiu Q, Tang Z, Yang Y, Kuang Z, Du X, Xiao S, Liu Y, Luo Y, Gu L, Tian L, Liang X, Fan G, Zhang Y, Zhang P, Zhou W, Liu X, Tian J, Wei W. Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study. Gastric Cancer 2024; 27:343-354. [PMID: 38095766 PMCID: PMC10896941 DOI: 10.1007/s10120-023-01451-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/09/2023] [Indexed: 02/28/2024]
Abstract
OBJECTIVE Patients with gastric atrophy and intestinal metaplasia (IM) were at risk for gastric cancer, necessitating an accurate risk assessment. We aimed to establish and validate a diagnostic approach for gastric biopsy specimens using deep learning and OLGA/OLGIM for individual gastric cancer risk classification. METHODS In this study, we prospectively enrolled 545 patients suspected of atrophic gastritis during endoscopy from 13 tertiary hospitals between December 22, 2017, to September 25, 2020, with a total of 2725 whole-slide images (WSIs). Patients were randomly divided into a training set (n = 349), an internal validation set (n = 87), and an external validation set (n = 109). Sixty patients from the external validation set were randomly selected and divided into two groups for an observer study, one with the assistance of algorithm results and the other without. We proposed a semi-supervised deep learning algorithm to diagnose and grade IM and atrophy, and we compared it with the assessments of 10 pathologists. The model's performance was evaluated based on the area under the curve (AUC), sensitivity, specificity, and weighted kappa value. RESULTS The algorithm, named GasMIL, was established and demonstrated encouraging performance in diagnosing IM (AUC 0.884, 95% CI 0.862-0.902) and atrophy (AUC 0.877, 95% CI 0.855-0.897) in the external test set. In the observer study, GasMIL achieved an 80% sensitivity, 85% specificity, a weighted kappa value of 0.61, and an AUC of 0.953, surpassing the performance of all ten pathologists in diagnosing atrophy. Among the 10 pathologists, GasMIL's AUC ranked second in OLGA (0.729, 95% CI 0.625-0.833) and fifth in OLGIM (0.792, 95% CI 0.688-0.896). With the assistance of GasMIL, pathologists demonstrated improved AUC (p = 0.013), sensitivity (p = 0.014), and weighted kappa (p = 0.016) in diagnosing IM, and improved specificity (p = 0.007) in diagnosing atrophy compared to pathologists working alone. CONCLUSION GasMIL shows the best overall performance in diagnosing IM and atrophy when compared to pathologists, significantly enhancing their diagnostic capabilities.
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Affiliation(s)
- Shuangshuang Fang
- Beijing Key Laboratory of Functional Gastrointestinal Disorders Diagnosis and Treatment of Traditional Chinese Medicine; Department of Gastroenterology, Wangjing Hospital, China Academy of Chinese Medical Sciences, No. 6, Zhonghuan South Road, Wangjing, Beijing, 100102, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Science, Beijing, 100190, China
| | - Qi Qiu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Science, Beijing, 100190, China
| | - Zhenchao Tang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China
| | - Yang Yang
- Beijing Key Laboratory of Functional Gastrointestinal Disorders Diagnosis and Treatment of Traditional Chinese Medicine; Department of Gastroenterology, Wangjing Hospital, China Academy of Chinese Medical Sciences, No. 6, Zhonghuan South Road, Wangjing, Beijing, 100102, China
| | - Zhongsheng Kuang
- Department of Pathology, The First Affiliated Hospital of Guangdong University of Traditional Chinese Medicine, Guangzhou, 510405, China
| | - Xiaohua Du
- Department of Pathology, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, 510120, China
| | - Shanshan Xiao
- Department of Pathology, The First Affiliated Hospital of Guangdong University of Traditional Chinese Medicine, Guangzhou, 510405, China
| | - Yanyan Liu
- Department of Pathology, The First Affiliated Hospital of Guangdong University of Traditional Chinese Medicine, Guangzhou, 510405, China
| | - Yuanbin Luo
- Department of Pathology, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, 730050, China
| | - Liping Gu
- Department of Pathology, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, 730050, China
| | - Li Tian
- Department of Pathology, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, 730050, China
| | - Xiaoxia Liang
- Department of Pathology, Shanxi Provincial Hospital of Traditional Chinese Medicine, Taiyuan, 030012, China
| | - Guiling Fan
- Department of Pathology, Shanxi Provincial Hospital of Traditional Chinese Medicine, Taiyuan, 030012, China
| | - Yu Zhang
- Department of Pathology, Shanxi Provincial Hospital of Traditional Chinese Medicine, Taiyuan, 030012, China
| | - Ping Zhang
- Department of Pathology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China
| | - Weixun Zhou
- Department of Pathology, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Xiuli Liu
- Department of Pathology and Immunology, Washington University, St. Louis, MO, 98195, USA
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Science, Beijing, 100190, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China
| | - Wei Wei
- Beijing Key Laboratory of Functional Gastrointestinal Disorders Diagnosis and Treatment of Traditional Chinese Medicine; Department of Gastroenterology, Wangjing Hospital, China Academy of Chinese Medical Sciences, No. 6, Zhonghuan South Road, Wangjing, Beijing, 100102, China.
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Lazăr DC, Avram MF, Faur AC, Romoşan I, Goldiş A. The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions. Gastroenterol Rep (Oxf) 2021; 9:185-204. [PMID: 34316369 PMCID: PMC8309682 DOI: 10.1093/gastro/goab008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 12/05/2020] [Accepted: 12/20/2020] [Indexed: 12/24/2022] Open
Abstract
This article analyses the literature regarding the value of computer-assisted systems in esogastroduodenoscopy-quality monitoring and the assessment of gastric lesions. Current data show promising results in upper-endoscopy quality control and a satisfactory detection accuracy of gastric premalignant and malignant lesions, similar or even exceeding that of experienced endoscopists. Moreover, artificial systems enable the decision for the best treatment strategies in gastric-cancer patient care, namely endoscopic vs surgical resection according to tumor depth. In so doing, unnecessary surgical interventions would be avoided whilst providing a better quality of life and prognosis for these patients. All these performance data have been revealed by numerous studies using different artificial intelligence (AI) algorithms in addition to white-light endoscopy or novel endoscopic techniques that are available in expert endoscopy centers. It is expected that ongoing clinical trials involving AI and the embedding of computer-assisted diagnosis systems into endoscopic devices will enable real-life implementation of AI endoscopic systems in the near future and at the same time will help to overcome the current limits of the computer-assisted systems leading to an improvement in performance. These benefits should lead to better diagnostic and treatment strategies for gastric-cancer patients. Furthermore, the incorporation of AI algorithms in endoscopic tools along with the development of large electronic databases containing endoscopic images might help in upper-endoscopy assistance and could be used for telemedicine purposes and second opinion for difficult cases.
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Affiliation(s)
- Daniela Cornelia Lazăr
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania,Timișoara, Romania
| | - Mihaela Flavia Avram
- Department of Surgery X, 1st Surgery Discipline, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Timișoara, Romania
| | - Alexandra Corina Faur
- Department I, Discipline of Anatomy and Embriology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Timișoara, Romania
| | - Ioan Romoşan
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania,Timișoara, Romania
| | - Adrian Goldiş
- Department VII of Internal Medicine II, Discipline of Gastroenterology and Hepatology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Timișoara, Romania
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Zhang Y, Li F, Yuan F, Zhang K, Huo L, Dong Z, Lang Y, Zhang Y, Wang M, Gao Z, Qin Z, Shen L. Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence. Dig Liver Dis 2020; 52:566-572. [PMID: 32061504 DOI: 10.1016/j.dld.2019.12.146] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 12/28/2019] [Accepted: 12/31/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND The sensitivity of endoscopy in diagnosing chronic atrophic gastritis is only 42%, and multipoint biopsy, despite being more accurate, is not always available. AIMS This study aimed to construct a convolutional neural network to improve the diagnostic rate of chronic atrophic gastritis. METHODS We collected 5470 images of the gastric antrums of 1699 patients and labeled them with their pathological findings. Of these, 3042 images depicted atrophic gastritis and 2428 did not. We designed and trained a convolutional neural network-chronic atrophic gastritis model to diagnose atrophic gastritis accurately, verified by five-fold cross-validation. Moreover, the diagnoses of the deep learning model were compared with those of three experts. RESULTS The diagnostic accuracy, sensitivity, and specificity of the convolutional neural network-chronic atrophic gastritis model in diagnosing atrophic gastritis were 0.942, 0.945, and 0.940, respectively, which were higher than those of the experts. The detection rates of mild, moderate, and severe atrophic gastritis were 93%, 95%, and 99%, respectively. CONCLUSION Chronic atrophic gastritis could be diagnosed by gastroscopic images using the convolutional neural network-chronic atrophic gastritis model. This may greatly reduce the burden on endoscopy physicians, simplify diagnostic routines, and reduce costs for doctors and patients.
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Affiliation(s)
- Yaqiong Zhang
- Department of Gastroenterology, Shanxi Provincial People's Hospital of Shanxi Medical University, Taiyuan, China
| | - Fengxia Li
- Department of Gastroenterology, Shanxi Provincial People's Hospital, Taiyuan, China.
| | - Fuqiang Yuan
- Baidu Online Network Technology (Beijing) Corporation, Beijing, China
| | - Kai Zhang
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Lijuan Huo
- Department of Gastroenterology, The First Hospital of Shanxi Medical University, Taiyuan, China
| | - Zichen Dong
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yiming Lang
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yapeng Zhang
- Fenyang College of Shanxi Medical University, Fenyang, China
| | - Meihong Wang
- Department of Gastroenterology, Shanxi Provincial People's Hospital of Shanxi Medical University, Taiyuan, China
| | - Zenghui Gao
- Department of Gastroenterology, Shanxi Provincial People's Hospital of Shanxi Medical University, Taiyuan, China
| | - Zhenzhen Qin
- Department of Gastroenterology, Shanxi Provincial People's Hospital of Shanxi Medical University, Taiyuan, China
| | - Leixue Shen
- School of Computer Science and Technology, Xidian University, Xi'an, China
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Awaysheh A, Wilcke J, Elvinger F, Rees L, Fan W, Zimmerman KL. Review of Medical Decision Support and Machine-Learning Methods. Vet Pathol 2019; 56:512-525. [DOI: 10.1177/0300985819829524] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Machine-learning methods can assist with the medical decision-making processes at the both the clinical and diagnostic levels. In this article, we first review historical milestones and specific applications of computer-based medical decision support tools in both veterinary and human medicine. Next, we take a mechanistic look at 3 archetypal learning algorithms—naive Bayes, decision trees, and neural network—commonly used to power these medical decision support tools. Last, we focus our discussion on the data sets used to train these algorithms and examine methods for validation, data representation, transformation, and feature selection. From this review, the reader should gain some appreciation for how these decision support tools have and can be used in medicine along with insight on their inner workings.
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Affiliation(s)
- Abdullah Awaysheh
- Department of Biomedical Sciences and Pathobiology, VA-MD College of Veterinary Medicine, Blacksburg, VA, USA
| | - Jeffrey Wilcke
- Department of Biomedical Sciences and Pathobiology, VA-MD College of Veterinary Medicine, Blacksburg, VA, USA
| | - François Elvinger
- Virginia Tech, Blacksburg, VA, USA
- Animal Health Diagnostic Center, Cornell University, Ithaca, NY, USA
| | - Loren Rees
- Department of Business Information Technology, Pamplin College of Business, Blacksburg, VA, USA
| | - Weiguo Fan
- Department of Business Information Technology, Pamplin College of Business, Blacksburg, VA, USA
| | - Kurt L. Zimmerman
- Department of Biomedical Sciences and Pathobiology, VA-MD College of Veterinary Medicine, Blacksburg, VA, USA
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Awaysheh A, Wilcke J, Elvinger F, Rees L, Fan W, Zimmerman K. Identifying free-text features to improve automated classification of structured histopathology reports for feline small intestinal disease. J Vet Diagn Invest 2017; 30:211-217. [PMID: 29188759 DOI: 10.1177/1040638717744002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The histologic evaluation of gastrointestinal (GI) biopsies is the standard for diagnosis of a variety of GI diseases (e.g., inflammatory bowel disease [IBD] and alimentary lymphoma [ALA]). The World Small Animal Veterinary Association (WSAVA) Gastrointestinal International Standardization Group proposed a reporting standard for GI biopsies consisting of a defined set of microscopic features. We compared the machine classification accuracy of free-text microscopic findings with those represented in the WSAVA format with a diagnosis of IBD and ALA. Unstructured free-text duodenal biopsy pathology reports from cats ( n = 60) with a diagnosis of IBD ( n = 20), ALA ( n = 20), or normal ( n = 20) were identified. Biopsy samples from these cases were then scored following the WSAVA guidelines to create a set of structured reports. Three supervised machine-learning algorithms were trained using the structured and then the unstructured reports. Diagnosis classification accuracy for the 3 algorithms was compared using the structured and unstructured reports. Using naive Bayes and neural networks, unstructured information-based models achieved higher diagnostic accuracy (0.90 and 0.88, respectively) compared to the structured information-based models (0.74 and 0.72, respectively). Results suggest that discriminating diagnostic information was lost using current WSAVA microscopic guideline features. Addition of free-text features (number of plasma cells) increased WSAVA auto-classification performance. The methodologies reported in our study represent a way of identifying candidate microscopic features for use in structured histopathology reports.
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Affiliation(s)
- Abdullah Awaysheh
- Department of Biomedical Sciences and Pathobiology, VA-MD College of Veterinary Medicine (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA.,Department of Business Information Technology, Pamplin College of Business (Fan, Rees), Virginia Tech, Blacksburg, VA.,Animal Health Diagnostic Center, Cornell University, Ithaca, NY (Elvinger)
| | - Jeffrey Wilcke
- Department of Biomedical Sciences and Pathobiology, VA-MD College of Veterinary Medicine (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA.,Department of Business Information Technology, Pamplin College of Business (Fan, Rees), Virginia Tech, Blacksburg, VA.,Animal Health Diagnostic Center, Cornell University, Ithaca, NY (Elvinger)
| | - François Elvinger
- Department of Biomedical Sciences and Pathobiology, VA-MD College of Veterinary Medicine (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA.,Department of Business Information Technology, Pamplin College of Business (Fan, Rees), Virginia Tech, Blacksburg, VA.,Animal Health Diagnostic Center, Cornell University, Ithaca, NY (Elvinger)
| | - Loren Rees
- Department of Biomedical Sciences and Pathobiology, VA-MD College of Veterinary Medicine (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA.,Department of Business Information Technology, Pamplin College of Business (Fan, Rees), Virginia Tech, Blacksburg, VA.,Animal Health Diagnostic Center, Cornell University, Ithaca, NY (Elvinger)
| | - Weiguo Fan
- Department of Biomedical Sciences and Pathobiology, VA-MD College of Veterinary Medicine (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA.,Department of Business Information Technology, Pamplin College of Business (Fan, Rees), Virginia Tech, Blacksburg, VA.,Animal Health Diagnostic Center, Cornell University, Ithaca, NY (Elvinger)
| | - Kurt Zimmerman
- Department of Biomedical Sciences and Pathobiology, VA-MD College of Veterinary Medicine (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA.,Department of Business Information Technology, Pamplin College of Business (Fan, Rees), Virginia Tech, Blacksburg, VA.,Animal Health Diagnostic Center, Cornell University, Ithaca, NY (Elvinger)
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Awaysheh A, Wilcke J, Elvinger F, Rees L, Fan W, Zimmerman KL. Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats. J Vet Diagn Invest 2016; 28:679-687. [DOI: 10.1177/1040638716657377] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p < 0.0001). The areas under the receiver-operating characteristic curve for classifying cases into the 3 categories was 83% by naive Bayes, 79% by decision tree, and 82% by artificial neural networks. Prediction models using machine learning provided a method for distinguishing between ALA–IBD, ALA–normal, and IBD–normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats.
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Affiliation(s)
- Abdullah Awaysheh
- Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA
- Population Health Sciences (Elvinger), Virginia Tech, Blacksburg, VA
- Business Information Technology (Rees), Virginia Tech, Blacksburg, VA
- Accounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA
| | - Jeffrey Wilcke
- Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA
- Population Health Sciences (Elvinger), Virginia Tech, Blacksburg, VA
- Business Information Technology (Rees), Virginia Tech, Blacksburg, VA
- Accounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA
| | - François Elvinger
- Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA
- Population Health Sciences (Elvinger), Virginia Tech, Blacksburg, VA
- Business Information Technology (Rees), Virginia Tech, Blacksburg, VA
- Accounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA
| | - Loren Rees
- Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA
- Population Health Sciences (Elvinger), Virginia Tech, Blacksburg, VA
- Business Information Technology (Rees), Virginia Tech, Blacksburg, VA
- Accounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA
| | - Weiguo Fan
- Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA
- Population Health Sciences (Elvinger), Virginia Tech, Blacksburg, VA
- Business Information Technology (Rees), Virginia Tech, Blacksburg, VA
- Accounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA
| | - Kurt L. Zimmerman
- Departments of Biomedical Sciences and Pathobiology (Awaysheh, Wilcke, Zimmerman), Virginia Tech, Blacksburg, VA
- Population Health Sciences (Elvinger), Virginia Tech, Blacksburg, VA
- Business Information Technology (Rees), Virginia Tech, Blacksburg, VA
- Accounting and Information Systems (Fan), Virginia Tech, Blacksburg, VA
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