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Bangolo A, Wadhwani N, Nagesh VK, Dey S, Tran HHV, Aguilar IK, Auda A, Sidiqui A, Menon A, Daoud D, Liu J, Pulipaka SP, George B, Furman F, Khan N, Plumptre A, Sekhon I, Lo A, Weissman S. Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies. Artif Intell Gastrointest Endosc 2024; 5:90704. [DOI: 10.37126/aige.v5.i2.90704] [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: 12/12/2023] [Revised: 01/28/2024] [Accepted: 03/04/2024] [Indexed: 05/11/2024] Open
Abstract
The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.
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Affiliation(s)
- Ayrton Bangolo
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nikita Wadhwani
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Vignesh K Nagesh
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Shraboni Dey
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Hadrian Hoang-Vu Tran
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Izage Kianifar Aguilar
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Auda Auda
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aman Sidiqui
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aiswarya Menon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Deborah Daoud
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - James Liu
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Sai Priyanka Pulipaka
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Blessy George
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Flor Furman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nareeman Khan
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Adewale Plumptre
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Imranjot Sekhon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Abraham Lo
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Simcha Weissman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
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Wang Y, Wang Z, Guo X, Cao Y, Xing H, Wang Y, Xing B, Wang Y, Yao Y, Ma W. Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients. Cancer Med 2024; 13:e7218. [PMID: 38733169 PMCID: PMC11087814 DOI: 10.1002/cam4.7218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 04/03/2024] [Accepted: 04/15/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) are a promising immunotherapy approach, but glioblastoma clinical trials have not yielded satisfactory results. OBJECTIVE To screen glioblastoma patients who may benefit from immunotherapy. METHODS Eighty-one patients receiving anti-PD1/PD-L1 treatment from a large-scale clinical trial and 364 patients without immunotherapy from The Cancer Genome Atlas (TCGA) were included. Patients in the ICI-treated cohort were divided into responders and nonresponders according to overall survival (OS), and the most critical responder-relevant features were screened using random forest (RF). We constructed an artificial neural network (ANN) model and verified its predictive value with immunotherapy response and OS. RESULTS We defined two groups of ICI-treated glioblastoma patients with large differences in survival benefits as nonresponders (OS ≤6 months, n = 18) and responders (OS ≥17 months, n = 8). No differentially mutated genes were observed between responders and nonresponders. We performed RF analysis to select the most critical responder-relevant features and developed an ANN with 20 input variables, five hidden neurons and one output neuron. Receiver operating characteristic analysis and the DeLong test demonstrated that the ANN had the best performance in predicting responders, with an AUC of 0.97. Survival analysis indicated that ANN-predicted responders had significantly better OS rates than nonresponders. CONCLUSION The 20-gene panel developed by the ANN could be a promising biomarker for predicting immunotherapy response and prognostic benefits in ICI-treated GBM patients and may guide oncologists to accurately select potential responders for the preferential use of ICIs.
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Affiliation(s)
- Yaning Wang
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Zihao Wang
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Xiaopeng Guo
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Yaning Cao
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Hao Xing
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Yuekun Wang
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Bing Xing
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Yu Wang
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Yong Yao
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Wenbin Ma
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
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Kuwayama N, Hoshino I, Mori Y, Yokota H, Iwatate Y, Uno T. Applying artificial intelligence using routine clinical data for preoperative diagnosis and prognosis evaluation of gastric cancer. Oncol Lett 2023; 26:499. [PMID: 37854867 PMCID: PMC10579989 DOI: 10.3892/ol.2023.14087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/14/2023] [Indexed: 10/20/2023] Open
Abstract
The present study employed artificial intelligence (AI) machine learning technology to evaluate the prognosis of gastric cancer using blood collection data, commonly used in clinical practice and subsequently performed a stratification distinct from conventional tumor-node-metastasis (TNM) classification. Experiments were conducted using four machine learning methods, namely, logistic regression (LR), random forest (RF), gradient boosting (GB) and deep neural network (DNN), to classify good or poor post-5-year prognosis based on clinicopathological data and post-5-year relapse occurrence. For each machine learning method, the importance was sorted in descending order (from the most to the least); the top features were used for clustering using the k-medoids method. The prediction accuracy and area under the curve (AUC) for 5-year survival were as follows: LR, 76.8% and 0.702; RF, 72.5% and 0.721; GB, 75.3% and 0.73; DNN, 76.9% and 0.682, respectively. The prediction accuracy and AUC for 5-year recurrence-free survival were as follows: LR, 85.5% and 0.692; RF, 79.0% and 0.721; GB, 80.5% and 0.718; DNN, 83.2% and 0.670. Clustering patients into three groups resulted in a stratification distinct from the TNM classification. In conclusion, AI machine learning using routine clinical data can help evaluate the prognosis of gastric cancer, with prognosis differing according to AI-identified clusters.
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Affiliation(s)
- Naoki Kuwayama
- Division of Gastroenterological Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Isamu Hoshino
- Division of Gastroenterological Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Yasukuni Mori
- Graduate School of Engineering, Faculty of Engineering, Chiba University, Chiba 263-8522, Japan
| | - Hajime Yokota
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yosuke Iwatate
- Division of Hepato-Biliary-Pancreatic Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Takashi Uno
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
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Ma X, Pierce E, Anand H, Aviles N, Kunk P, Alemazkoor N. Early prediction of response to palliative chemotherapy in patients with stage-IV gastric and esophageal cancer. BMC Cancer 2023; 23:910. [PMID: 37759332 PMCID: PMC10536729 DOI: 10.1186/s12885-023-11422-z] [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: 04/17/2023] [Accepted: 09/20/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The goal of therapy for many patients with advanced stage malignancies, including those with metastatic gastric and esophageal cancers, is to extend overall survival while also maintaining quality of life. After weighing the risks and benefits of treatment with palliative chemotherapy (PC) with non-curative intent, many patients decide to pursue treatment. It is known that a subset of patients who are treated with PC experience significant side effects without clinically significant survival benefits from PC. METHODS We use data from 150 patients with stage-IV gastric and esophageal cancers to train machine learning models that predict whether a patient with stage-IV gastric or esophageal cancers would benefit from PC, in terms of increased survival duration, at very early stages of the treatment. RESULTS Our findings show that machine learning can predict with high accuracy whether a patient will benefit from PC at the time of diagnosis. More accurate predictions can be obtained after only two cycles of PC (i.e., about 4 weeks after diagnosis). The results from this study are promising with regard to potential improvements in quality of life for patients near the end of life and a potential overall survival benefit by optimizing systemic therapy earlier in the treatment course of patients.
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Affiliation(s)
- Xiaoyuan Ma
- Department of Statistics, University of Virginia, Charlottesville, USA
| | - Eric Pierce
- School of Medicine, University of Virginia, Charlottesville, USA
| | - Harsh Anand
- System and Information Engineering, University of Virginia, Charlottesville, USA
| | - Natalie Aviles
- Department of Sociology, University of Virginia, Charlottesville, USA
| | - Paul Kunk
- School of Medicine, University of Virginia, Charlottesville, USA
| | - Negin Alemazkoor
- System and Information Engineering, University of Virginia, Charlottesville, USA.
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Preoperative Immunocite-Derived Ratios Predict Surgical Complications Better when Artificial Neural Networks Are Used for Analysis-A Pilot Comparative Study. J Pers Med 2023; 13:jpm13010101. [PMID: 36675762 PMCID: PMC9861480 DOI: 10.3390/jpm13010101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 01/04/2023] Open
Abstract
We aimed to comparatively assess the prognostic preoperative value of the main peripheral blood components and their ratios-the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)-to the use of artificial-neural-network analysis in determining undesired postoperative outcomes in colorectal cancer patients. Our retrospective study included 281 patients undergoing elective radical surgery for colorectal cancer in the last seven years. The preoperative values of SII, NLR, LMR, and PLR were analyzed in relation to postoperative complications, with a special emphasis on their ability to accurately predict the occurrence of anastomotic leak. A feed-forward fully connected multilayer perceptron network (MLP) was trained and tested alongside conventional statistical tools to assess the predictive value of the abovementioned blood markers in terms of sensitivity and specificity. Statistically significant differences and moderate correlation levels were observed for SII and NLR in predicting the anastomotic leak rate and degree of postoperative complications. No correlations were found between the LMR and PLR or the abovementioned outcomes. The MLP network analysis showed superior prediction value in terms of both sensitivity (0.78 ± 0.07; 0.74 ± 0.04; 0.71 ± 0.13) and specificity (0.81 ± 0.11; 0.69 ± 0.03; 0.9 ± 0.04) for all the given tasks. Preoperative SII and NLR appear to be modest prognostic factors for anastomotic leakage and overall morbidity. Using an artificial neural network offers superior prognostic results in the preoperative risk assessment for overall morbidity and anastomotic leak rate.
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Hu B, Yin G, Sun X. Identification of specific role of SNX family in gastric cancer prognosis evaluation. Sci Rep 2022; 12:10231. [PMID: 35715463 PMCID: PMC9205943 DOI: 10.1038/s41598-022-14266-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 06/03/2022] [Indexed: 11/10/2022] Open
Abstract
We here perform a systematic bioinformatic analysis to uncover the role of sorting nexin (SNX) family in clinical outcome of gastric cancer (GC). Comprehensive bioinformatic analysis were realized with online tools such as TCGA, GEO, String, Timer, cBioportal and Kaplan-Meier Plotter. Statistical analysis was conducted with R language or Perl, and artificial neural network (ANN) model was established using Python. Our analysis demonstrated that SNX4/5/6/7/8/10/13/14/15/16/20/22/25/27/30 were higher expressed in GC, whereas SNX1/17/21/24/33 were in the opposite expression profiles. GSE66229 was employed as verification of the differential expression analysis based on TCGA. Clustering results gave the relative transcriptional levels of 30 SNXs in tumor, and it was totally consistent to the inner relevance of SNXs at mRNA level. Protein-Protein Interaction map showed closely and complex connection among 33 SNXs. Tumor immune infiltration analysis asserted that SNX1/3/9/18/19/21/29/33, SNX1/17/18/20/21/29/31/33, SNX1/2/3/6/10/18/29/33, and SNX1/2/6/10/17/18/20/29 were strongly correlated with four kinds of survival related tumor-infiltrating immune cells, including cancer associated fibroblast, endothelial cells, macrophages and Tregs. Kaplan-Meier survival analysis based on GEO presented more satisfactory results than that based on TCGA-STAD did, and all the 29 SNXs were statistically significant, SNX23/26/28 excluded. SNXs alteration contributed to microsatellite instability (MSI) or higher level of MSI-H (hyper-mutated MSI or high level of MSI), and other malignancy encompassing mutation of TP53 and ARID1A, as well as methylation of MLH1.The multivariate cox model, visualized as a nomogram, performed excellently in patients risk classification, for those with higher risk-score suffered from shorter overall survival (OS). Compared to previous researches, our ANN models showed a predictive power at a middle-upper level, with AUC of 0.87/0.72, 0.84/0.72, 0.90/0.71 (GSE84437), 0.98/0.66, 0.86/0.70, 0.98/0.71 (GSE66229), 0.94/0.66, 0.83/0.71, 0.88/0.72 (GSE26253) corresponding to one-, three- and five-year OS and recurrence free survival (RFS) estimation, especially ANN model built with GSE66229 including exclusively SNXs as input data. The SNX family shows great value in postoperative survival evaluation of GC, and ANN models constructed using SNXs transcriptional data manifesting excellent predictive power in both OS and RFS prediction works as convincing verification to that.
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Affiliation(s)
- Beibei Hu
- Department of Gastroenterology, First Affiliated Hospital of China Medical University, North Nanjing Street 155, Shenyang, 110001, People's Republic of China
| | - Guohui Yin
- School of Civil Engineering and Transportation, Hebei University of Technology, Tianjin, 300401, People's Republic of China
| | - Xuren Sun
- Department of Gastroenterology, First Affiliated Hospital of China Medical University, North Nanjing Street 155, Shenyang, 110001, People's Republic of China.
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Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021; 13:cancers13215494. [PMID: 34771658 PMCID: PMC8582733 DOI: 10.3390/cancers13215494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Gastrointestinal cancers cause over 2.8 million deaths annually worldwide. Currently, the diagnosis of various gastrointestinal cancer mainly relies on manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. Artificial intelligence (AI) may be useful in screening, diagnosing, and treating various cancers by accurately analyzing diagnostic clinical images, identifying therapeutic targets, and processing large datasets. The use of AI in endoscopic procedures is a significant breakthrough in modern medicine. Although the diagnostic accuracy of AI systems has markedly increased, it still needs collaboration with physicians. In the near future, AI-assisted systems will become a vital tool for the management of these cancer patients. Abstract Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Lu J, Xue Z, Xu BB, Wu D, Zheng HL, Xie JW, Wang JB, Lin JX, Chen QY, Li P, Huang CM, Zheng CH. Application of an artificial neural network for predicting the potential chemotherapy benefit of patients with gastric cancer after radical surgery. Surgery 2021; 171:955-965. [PMID: 34756492 DOI: 10.1016/j.surg.2021.08.055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 08/19/2021] [Accepted: 08/31/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Artificial neural network models have a strong self-learning ability and can deal with complex biological information, but there is no artificial neural network model for predicting the benefits of adjuvant chemotherapy in patients with gastric cancer. METHODS The clinicopathological data of patients who underwent radical resection of gastric cancer from January 2010 to September 2014 were analyzed retrospectively. Patients who underwent surgery combined with adjuvant chemotherapy were randomly divided into a training cohort (70%) and a validation cohort (30%). An artificial neural network model (potential-CT-benefit-ANN) was established, and its ability to predict the potential benefit of chemotherapy was evaluated by the C-index. The prognostic prediction and stratification ability of potential-CT-benefit-ANN and the eighth American Joint Committee on Cancer staging system were compared by receiver operating characteristic curves and Kaplan-Meier curves. RESULTS In both the training and validation cohort, potential-CT-benefit-ANN shows good prediction accuracy for potential adjuvant chemotherapy benefit. The receiver operating characteristic curve showed that the prediction accuracy of potential-CT-benefit-ANN was better than that of the eighth American Joint Committee on Cancer staging system in all groups. The calibration plots showed that the predicted prognosis of potential-CT-benefit-ANN was highly consistent with the actual value. The survival curves showed that potential-CT-benefit-ANN could stratify prognosis well for all groups and performed significantly better than the eighth AJCC staging system. CONCLUSION The potential-CT-benefit-ANN model developed in this study can accurately predict the potential benefits of adjuvant chemotherapy in patients with stage II/III gastric cancer. The benefit score based on potential-CT-benefit-ANN can predict the long-term prognosis of patients with adjuvant chemotherapy and has good prognostic stratification ability.
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Affiliation(s)
- Jun Lu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Zhen Xue
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Bin-Bin Xu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Dong Wu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Hua-Long Zheng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Jian-Wei Xie
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Jia-Bin Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Jian-Xian Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Qi-Yue Chen
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Ping Li
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Chang-Ming Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China.
| | - Chao-Hui Zheng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China; Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China.
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11
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Sakamoto T, Goto T, Fujiogi M, Kawarai Lefor A. Machine learning in gastrointestinal surgery. Surg Today 2021; 52:995-1007. [PMID: 34559310 DOI: 10.1007/s00595-021-02380-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 07/03/2021] [Indexed: 12/11/2022]
Abstract
Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.
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Affiliation(s)
- Takashi Sakamoto
- Department of Gastroenterological Surgery, Gastroenterological Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto, Tokyo, 135-8550, Japan. .,Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Tadahiro Goto
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.,TXP Medical Co. Ltd, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 114-8485, Japan
| | - Michimasa Fujiogi
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.,Department of Pediatric Surgery, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Alan Kawarai Lefor
- Department of Surgery, Jichi Medical University, Shimotsuke, Tochigi, 3290498, Japan
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Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27:2681-2709. [PMID: 34135549 PMCID: PMC8173384 DOI: 10.3748/wjg.v27.i21.2681] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/29/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.
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Affiliation(s)
- Bo Cao
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ke-Cheng Zhang
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Bo Wei
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Lin Chen
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
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13
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Yu C, Helwig EJ. Artificial intelligence in gastric cancer: a translational narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:269. [PMID: 33708896 PMCID: PMC7940908 DOI: 10.21037/atm-20-6337] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Increasing clinical contributions and novel techniques have been made by artificial intelligence (AI) during the last decade. The role of AI is increasingly recognized in cancer research and clinical application. Cancers like gastric cancer, or stomach cancer, are ideal testing grounds to see if early undertakings of applying AI to medicine can yield valuable results. There are numerous concepts derived from AI, including machine learning (ML) and deep learning (DL). ML is defined as the ability to learn data features without being explicitly programmed. It arises at the intersection of data science and computer science and aims at the efficiency of computing algorithms. In cancer research, ML has been increasingly used in predictive prognostic models. DL is defined as a subset of ML targeting multilayer computation processes. DL is less dependent on the understanding of data features than ML. Therefore, the algorithms of DL are much more difficult to interpret than ML, even potentially impossible. This review discussed the role of AI in the diagnostic, therapeutic and prognostic advances of gastric cancer. Models like convolutional neural networks (CNNs) or artificial neural networks (ANNs) achieved significant praise in their application. There is much more to be fully covered across the clinical administration of gastric cancer. Despite growing efforts, adapting AI to improving diagnoses for gastric cancer is a worthwhile venture. The information yield can revolutionize how we approach gastric cancer problems. Though integration might be slow and labored, it can be given the ability to enhance diagnosing through visual modalities and augment treatment strategies. It can grow to become an invaluable tool for physicians. AI not only benefits diagnostic and therapeutic outcomes, but also reshapes perspectives over future medical trajectory.
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Affiliation(s)
- Chaoran Yu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Fudan University Shanghai Cancer Center, Shanghai, China
| | - Ernest Johann Helwig
- Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
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14
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Kudou M, Kosuga T, Otsuji E. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives. Artif Intell Gastroenterol 2020; 1:71-85. [DOI: 10.35712/aig.v1.i4.71] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 10/28/2020] [Accepted: 11/12/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) using machine or deep learning algorithms is attracting increasing attention because of its more accurate image recognition ability and prediction performance than human-aid analyses. The application of AI models to gastrointestinal (GI) clinical oncology has been investigated for the past decade. AI has the capacity to automatically detect and diagnose GI tumors with similar diagnostic accuracy to expert clinicians. AI may also predict malignant potential, such as tumor histology, metastasis, patient survival, resistance to cancer treatments and the molecular biology of tumors, through image analyses of radiological or pathological imaging data using complex deep learning models beyond human cognition. The introduction of AI-assisted diagnostic systems into clinical settings is expected in the near future. However, limitations associated with the evaluation of GI tumors by AI models have yet to be resolved. Recent studies on AI-assisted diagnostic models of gastric and colorectal cancers in the endoscopic, pathological, and radiological fields were herein reviewed. The limitations and future perspectives for the application of AI systems in clinical settings have also been discussed. With the establishment of a multidisciplinary team containing AI experts in each medical institution and prospective studies, AI-assisted medical systems will become a promising tool for GI cancer.
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Affiliation(s)
- Michihiro Kudou
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Kyoto Okamoto Memorial Hospital, Kyoto 613-0034, Japan
| | - Toshiyuki Kosuga
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Saiseikai Shiga Hospital, Ritto 520-3046, Japan
| | - Eigo Otsuji
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
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15
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Jin P, Ji X, Kang W, Li Y, Liu H, Ma F, Ma S, Hu H, Li W, Tian Y. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol 2020; 146:2339-2350. [PMID: 32613386 DOI: 10.1007/s00432-020-03304-9] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 06/26/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVE This study aims to systematically review the application of artificial intelligence (AI) techniques in gastric cancer and to discuss the potential limitations and future directions of AI in gastric cancer. METHODS A systematic review was performed that follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pubmed, EMBASE, the Web of Science, and the Cochrane Library were used to search for gastric cancer publications with an emphasis on AI that were published up to June 2020. The terms "artificial intelligence" and "gastric cancer" were used to search for the publications. RESULTS A total of 64 articles were included in this review. In gastric cancer, AI is mainly used for molecular bio-information analysis, endoscopic detection for Helicobacter pylori infection, chronic atrophic gastritis, early gastric cancer, invasion depth, and pathology recognition. AI may also be used to establish predictive models for evaluating lymph node metastasis, response to drug treatments, and prognosis. In addition, AI can be used for surgical training, skill assessment, and surgery guidance. CONCLUSIONS In the foreseeable future, AI applications can play an important role in gastric cancer management in the era of precision medicine.
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Affiliation(s)
- Peng Jin
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Xiaoyan Ji
- Department of Emergency Ward, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Wenzhe Kang
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yang Li
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Hao Liu
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Fuhai Ma
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shuai Ma
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Haitao Hu
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Weikun Li
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yantao Tian
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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