Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review

The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes to increased accuracy for diagnosis with higher efficiency, significantly reducing the incidence of advanced gastric cancer. Previous studies have explored the predictive performance of gastric cancer risk prediction models, as well as the predictive factors and algorithms between each model, but have reached controversial conclusions. Thus, the performance of current machine-learning-based gastric cancer risk prediction models alongside the clinical relevance of different predictive factors needs to be evaluated to help build more efficient and feasible models in the future. In this systematic review, we summarize the current research progress related to the gastric cancer risk prediction model; discuss the predictive factors and methods used to construct the model; analyze the role of important predictive factors in gastric cancer, the preference of the selected classification algorithm, and the emphasis of evaluation criteria; and provide suggestions for the subsequent construction and improvement of the gastric cancer risk prediction model. Finally, we propose an improved approach based on the ethical issues of artificial intelligence in medicine to realize the clinical application of the gastric cancer risk prediction model in the future.

[1]  Hongbing Shen,et al.  Development, validation, and evaluation of a risk assessment tool for personalized screening of gastric cancer in Chinese populations , 2023, BMC Medicine.

[2]  A. Olshan,et al.  Discrimination between Precancerous Gastric Lesions and Gastritis Using a Gastric Cancer Risk Stratification Model , 2023, Asian Pacific journal of cancer prevention : APJCP.

[3]  Helda Tutunchi,et al.  Fruit and vegetable intake in relation to gastric cancer risk: A comprehensive and updated systematic review and dose-response meta-analysis of cohort studies , 2023, Frontiers in Nutrition.

[4]  W. Miao,et al.  Development and validation of an artificial neural network model for non-invasive gastric cancer screening and diagnosis , 2022, Scientific reports.

[5]  Hao Yan,et al.  Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview , 2022, World journal of gastroenterology.

[6]  E. Tong,et al.  Evaluation of Cancer Deaths Attributable to Tobacco in California, 2014-2019 , 2022, JAMA network open.

[7]  Fa-Ming Yin,et al.  Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer , 2022, Frontiers in Oncology.

[8]  M. de Kamps,et al.  Machine Learning for Risk Prediction of Oesophago-Gastric Cancer in Primary Care: Comparison with Existing Risk-Assessment Tools , 2022, Cancers.

[9]  Hongbing Shen,et al.  C-reactive protein and cancer risk: a pan-cancer study of prospective cohort and Mendelian randomization analysis , 2022, BMC Medicine.

[10]  Q. Dong,et al.  Prediction of gastric cancer risk by a polygenic risk score of Helicobacter pylori , 2022, World journal of gastrointestinal oncology.

[11]  W. Niu,et al.  Prediction of presurgical metabolic syndrome for gastric cancer‐specific mortality is more evident in smokers: The FIESTA study , 2022, Cancer medicine.

[12]  Peiyuan Yin,et al.  Integration of transcriptomics, proteomics, and metabolomics data to reveal HER2-associated metabolic heterogeneity in gastric cancer with response to immunotherapy and neoadjuvant chemotherapy , 2022, Frontiers in Immunology.

[13]  F. Jiang,et al.  Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway–Related Prognostic Prediction Model in Gastric Cancer , 2022, Frontiers in genetics.

[14]  Cong Lin,et al.  Identification of a 3-Gene Model as Prognostic Biomarker in Patients With Gastric Cancer , 2022, Frontiers in Oncology.

[15]  Fanghai Han,et al.  Immune-Related LncRNAs to Construct a Prognosis Risk-Assessment Model for Gastric Cancer , 2022, Current oncology.

[16]  Yu Guang Wang,et al.  Cell graph neural networks enable the precise prediction of patient survival in gastric cancer , 2022, npj Precision Oncology.

[17]  Tao Wang,et al.  A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer , 2022, Frontiers in Neuroscience.

[18]  Songbo Zhao,et al.  Accuracy evaluation of combining gastroscopy, multi-slice spiral CT, Her-2, and tumor markers in gastric cancer staging diagnosis , 2022, World Journal of Surgical Oncology.

[19]  P. Friedmann,et al.  Serum Pepsinogen as a Biomarker for Gastric Cancer in the United States: A Nested Case-Control Study using the PLCO Cancer Screening Trial Data. , 2022, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[20]  Shuhao Wang,et al.  Assessment of deep learning assistance for the pathological diagnosis of gastric cancer , 2022, Modern Pathology.

[21]  Dechun Liu,et al.  A new risk model based on a 11-m6A-related lncRNA signature for predicting prognosis and monitoring immunotherapy for gastric cancer , 2022, BMC Cancer.

[22]  Jana Schaich Borg,et al.  Computational Ethics , 2022, HMD Praxis der Wirtschaftsinformatik.

[23]  Lei Wu,et al.  A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study , 2022, EClinicalMedicine.

[24]  Y. Liu,et al.  Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients , 2022, Aging.

[25]  Jing Chen,et al.  A novel immune-related lncRNA pair signature for prognostic prediction and immune response evaluation in gastric cancer: a bioinformatics and biological validation study , 2022, Cancer cell international.

[26]  Quan P. Ly,et al.  Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. , 2022, Journal of the National Comprehensive Cancer Network : JNCCN.

[27]  W. Long,et al.  A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma , 2022, Frontiers in Oncology.

[28]  H. Bohnenberger,et al.  A nomogram to predict the recurrence-free survival and analyze the utility of chemotherapy in stage IB non-small cell lung cancer , 2022, Translational lung cancer research.

[29]  Zekuan Xu,et al.  Quantification of Tumor Abnormal Proteins in the Diagnosis and Postoperative Prognostic Evaluation of Gastric Cancer , 2022, Clinical Medicine Insights. Oncology.

[30]  C. Png,et al.  Mucosal microbiome associates with progression to gastric cancer , 2022, Theranostics.

[31]  Lei Gao,et al.  A risk model based on autophagy-related lncRNAs for predicting prognosis and efficacy of immunotherapy and chemotherapy in gastric cancer patients , 2021, Aging.

[32]  Dehua Yang,et al.  Dietary Salt Intake and Gastric Cancer Risk: A Systematic Review and Meta-Analysis , 2021, Frontiers in Nutrition.

[33]  M. Plummer,et al.  The relative and attributable risks of cardia and non-cardia gastric cancer associated with Helicobacter pylori infection in China: a case-cohort study , 2021, The Lancet. Public health.

[34]  Yilun Xu,et al.  Integrative Radiogenomics Approach for Risk Assessment of Postoperative and Adjuvant Chemotherapy Benefits for Gastric Cancer Patients , 2021, Frontiers in Oncology.

[35]  K. Matsuo,et al.  Risk Prediction for Gastric Cancer Using GWAS-Identifie Polymorphisms, Helicobacter pylori Infection and Lifestyle-Related Risk Factors in a Japanese Population , 2021, Cancers.

[36]  L. Dai,et al.  Polygenic Risk Scores for Prediction of Gastric Cancer Based on Bioinformatics Screening and Validation of Functional lncRNA SNPs , 2021, Clinical and translational gastroenterology.

[37]  Yi-Zi Zheng,et al.  Prognostic Factors and a Nomogram Predicting Survival in Patients with Breast Ductal Carcinoma in situ with Microinvasion: A Population-Based Study , 2021, Clinical epidemiology.

[38]  Hang Zheng,et al.  Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblast Signature for Predicting Prognosis and Therapeutic Responses in Gastric Cancer , 2021, Frontiers in Molecular Biosciences.

[39]  Xiuying Wang,et al.  18F-FDG PET/CT Radiomics for Preoperative Prediction of Lymph Node Metastases and Nodal Staging in Gastric Cancer , 2021, Frontiers in Oncology.

[40]  N. Hong,et al.  CT‐detected extramural venous invasion‐related gene signature for the overall survival prediction in patients with gastric cancer , 2021, Cancer medicine.

[41]  Y. Sheng,et al.  Development and Validation of Nomograms to Predict Operative Link for Gastritis Assessment Any-Stage and Stages III–IV in the Chinese High-Risk Gastric Cancer Population , 2021, Frontiers in Medicine.

[42]  P. Lambin,et al.  A review in radiomics: Making personalized medicine a reality via routine imaging , 2021, Medicinal research reviews.

[43]  Yingmu Cai,et al.  Prognostic Model for Predicting Overall and Cancer-Specific Survival Among Patients With Cervical Squamous Cell Carcinoma: A SEER Based Study , 2021, Frontiers in Oncology.

[44]  R. van Hillegersberg,et al.  Risk Prediction Model of 90-Day Mortality After Esophagectomy for Cancer. , 2021, JAMA surgery.

[45]  B. Tang,et al.  Helicobacter pylori-Induced Heparanase Promotes H. pylori Colonization and Gastritis , 2021, Frontiers in Immunology.

[46]  Xia Li,et al.  Identification and Validation of Plasma Metabolomic Signatures in Precancerous Gastric Lesions That Progress to Cancer , 2021, JAMA network open.

[47]  Ce Li,et al.  Immune Landscape of Gastric Carcinoma Tumor Microenvironment Identifies a Peritoneal Relapse Relevant Immune Signature , 2021, Frontiers in Immunology.

[48]  L. Xin,et al.  Establishment of a prognostic model of four genes in gastric cancer based on multiple data sets , 2021, Cancer medicine.

[49]  Wentao Liu,et al.  Prediction Model of Tumor Regression Grade for Advanced Gastric Cancer After Preoperative Chemotherapy , 2021, Frontiers in Oncology.

[50]  Yujie Zhang,et al.  Development and validation of a Surveillance, Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in elderly patients with gastric cancer after surgery. , 2021, Journal of gastrointestinal oncology.

[51]  Chong Sun Hong,et al.  Confusion plot for the confusion matrix , 2021 .

[52]  F. Mégraud,et al.  Gastric Cancer: Advances in Carcinogenesis Research and New Therapeutic Strategies , 2021, International journal of molecular sciences.

[53]  Hongwei Li,et al.  Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning , 2021, Nature Communications.

[54]  A. Xu,et al.  A model established using marital status and other factors from the Surveillance, Epidemiology, and End Results database for early stage gastric cancer , 2021, Journal of Investigative Medicine.

[55]  Yongning Zhou,et al.  A Novel Six-Gene-Based Prognostic Model Predicts Survival and Clinical Risk Score for Gastric Cancer , 2021, Frontiers in Genetics.

[56]  M. Kanda,et al.  Transcriptomic Profiling Identifies a Risk Stratification Signature for Predicting Peritoneal Recurrence and Micrometastasis in Gastric Cancer , 2021, Clinical Cancer Research.

[57]  A. Jemal,et al.  Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries , 2021, CA: a cancer journal for clinicians.

[58]  I. Choi,et al.  Gastric Cancer Risk Prediction Using an Epidemiological Risk Assessment Model and Polygenic Risk Score , 2021, Cancers.

[59]  Haibo Wang,et al.  A predictive model for assessing prognostic risks in gastric cancer patients using gene expression and methylation data , 2021, BMC medical genomics.

[60]  Yan Zhao,et al.  CA724 predicts overall survival in locally advanced gastric cancer patients with neoadjuvant chemotherapy , 2021, BMC Cancer.

[61]  Min Liu,et al.  In silico development and validation of a novel glucose and lipid metabolism-related gene signature in gastric cancer , 2021, Translational cancer research.

[62]  Chuan-feng Ke,et al.  Prediction of distant metastasis and survival prediction of gastric cancer patients with metastasis to the liver, lung, bone, and brain: research based on the SEER database , 2021, Annals of translational medicine.

[63]  Yanxia Sun,et al.  Prognostic Model and Nomogram for Estimating Survival of Small Breast Cancer: A SEER-based Analysis. , 2020, Clinical breast cancer.

[64]  M. Gu,et al.  Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis , 2020, Clinical epigenetics.

[65]  Xiaobing Zhang,et al.  [Application of deep learning in cancer prognosis prediction model]. , 2020, Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi.

[66]  Tao Huang,et al.  A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map , 2020, Comput. Math. Methods Medicine.

[67]  Wanqing Chen,et al.  Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model , 2020, Chinese journal of cancer research = Chung-kuo yen cheng yen chiu.

[68]  E. Mayo-Wilson,et al.  PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews , 2020, BMJ.

[69]  Yulan Wang,et al.  Identification and prognostic value of metabolism-related genes in gastric cancer , 2020, Aging.

[70]  F. Jiang,et al.  Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer , 2020, Frontiers in Molecular Biosciences.

[71]  F. Bai,et al.  Prediction of gastric cancer risk: association between ZBTB20 genetic variance and gastric cancer risk in Chinese Han population , 2020, Bioscience reports.

[72]  J. Toh,et al.  Pathways of Gastric Carcinogenesis, Helicobacter pylori Virulence and Interactions with Antioxidant Systems, Vitamin C and Phytochemicals , 2020, International journal of molecular sciences.

[73]  Menghong Sun,et al.  Predictive model for risk of gastric cancer using genetic variants from genome‐wide association studies and high‐evidence meta‐analysis , 2020, Cancer medicine.

[74]  Yuming Jiang,et al.  Genomics Score Based on Genome-Wide Network Analysis for Prediction of Survival in Gastric Cancer: A Novel Prognostic Signature , 2020, Frontiers in Genetics.

[75]  Xiao Fu,et al.  Identification of a Tumor Microenvironment-relevant Gene set-based Prognostic Signature and Related Therapy Targets in Gastric Cancer , 2020, Theranostics.

[76]  Yang Li,et al.  Artificial intelligence in gastric cancer: a systematic review , 2020, Journal of Cancer Research and Clinical Oncology.

[77]  D. Dong,et al.  A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study. , 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[78]  Mengyu Sun,et al.  Prognostic Implications of Novel Gene Signatures in Gastric Cancer Microenvironment , 2020, Medical science monitor : international medical journal of experimental and clinical research.

[79]  J. Machlowska,et al.  Gastric Cancer: Epidemiology, Risk Factors, Classification, Genomic Characteristics and Treatment Strategies , 2020, International journal of molecular sciences.

[80]  Jianhua Wu,et al.  Development and validation of a hypoxia-immune-based microenvironment gene signature for risk stratification in gastric cancer , 2020, Journal of Translational Medicine.

[81]  R. Safaralizadeh,et al.  Helicobacter pylori‐related risk predictors of gastric cancer: The latest models, challenges, and future prospects , 2020, Cancer medicine.

[82]  Zhenzhen Liu,et al.  Pepsinogen Serology and Gastritis OLGA Staging in Mucosal Atrophy Assessment: A Cross-Sectional Study Involving East China Endoscopy Population , 2020, Gastroenterology research and practice.

[83]  Edward L. Giovannucci,et al.  Global Burden of 5 Major Types Of Gastrointestinal Cancer. , 2020, Gastroenterology.

[84]  Xin-Zu Chen,et al.  Prevalence of atrophic gastritis in southwest China and predictive strength of serum gastrin-17: A cross-sectional study (SIGES) , 2020, Scientific Reports.

[85]  R. Sun,et al.  Expression Status And Prognostic Value Of M6A-associated Genes in Gastric Cancer , 2020, Journal of Cancer.

[86]  Haitao Zhao,et al.  Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Differentially Expressed Genes , 2020, International journal of biological sciences.

[87]  L. Moradi,et al.  Risk factors for stomach cancer: a systematic review and meta-analysis , 2020, Epidemiology and health.

[88]  Yanfeng Hu,et al.  A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine , 2019, BMC Genomics.

[89]  Hao Wang,et al.  Stromal-Immune Score-Based Gene Signature: A Prognosis Stratification Tool in Gastric Cancer , 2019, Front. Oncol..

[90]  Yujie Zhang,et al.  Development and validation of prognostic nomogram for young patients with gastric cancer. , 2019, Annals of translational medicine.

[91]  Yoon Young Choi,et al.  Individual Patient Data Meta-Analysis of the Value of Microsatellite Instability As a Biomarker in Gastric Cancer. , 2019, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[92]  Zhongxue Chen,et al.  Clinicopathological risk factors for gastric cancer: a retrospective cohort study in China , 2019, BMJ Open.

[93]  T. Naito,et al.  Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study , 2019, Scientific Reports.

[94]  Chaohui Yu,et al.  Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging , 2019, Gastric Cancer.

[95]  Jie Tian,et al.  The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges , 2019, Theranostics.

[96]  A. Zwinderman,et al.  SOURCE: A Registry-Based Prediction Model for Overall Survival in Patients with Metastatic Oesophageal or Gastric Cancer , 2019, Cancers.

[97]  Prashanth Rawla,et al.  Epidemiology of gastric cancer: global trends, risk factors and prevention , 2018, Przeglad gastroenterologiczny.

[98]  Q. Gao,et al.  Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients , 2018, BMC Cancer.

[99]  Mohammed Ramdani,et al.  Deep Learning: An Overview , 2018, SITA.

[100]  Yonghong Zhang,et al.  LASSO-based Cox-PH model identifies an 11-lncRNA signature for prognosis prediction in gastric cancer , 2018, Molecular medicine reports.

[101]  Wenbing Lv,et al.  Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer , 2018, EBioMedicine.

[102]  G. Rosman,et al.  Artificial Intelligence in Surgery: Promises and Perils , 2018, Annals of surgery.

[103]  B. Rapkin,et al.  Can a gastric cancer risk survey identify high-risk patients for endoscopic screening? A pilot study. , 2018, The Journal of surgical research.

[104]  Christa Boer,et al.  Correlation Coefficients: Appropriate Use and Interpretation , 2018, Anesthesia and analgesia.

[105]  T. Ninomiya,et al.  Development and validation of a risk assessment tool for gastric cancer in a general Japanese population , 2018, Gastric Cancer.

[106]  John Yuen Shyi Peng,et al.  Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns. , 2018, JCO clinical cancer informatics.

[107]  Jun-chi Yang,et al.  Pathway- and clinical-factor-based risk model predicts the prognosis of patients with gastric cancer , 2018, Molecular medicine reports.

[108]  Jun Xiao,et al.  Prognostic significance of pretreatment serum carcinoembryonic antigen levels in gastric cancer with pathological lymph node-negative: A large sample single-center retrospective study , 2017, World journal of gastroenterology.

[109]  Hongwei Zhang,et al.  Diagnostic and prognostic value of CEA, CA19–9, AFP and CA125 for early gastric cancer , 2017, BMC Cancer.

[110]  K. Koike,et al.  Metaplasia in the Stomach—Precursor of Gastric Cancer? , 2017, International journal of molecular sciences.

[111]  S. Ng,et al.  Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. , 2017, Gastroenterology.

[112]  J. Joo,et al.  Alpha-fetoprotein is a significant prognostic factor for gastric cancer: Results from a propensity score matching analysis after curative resection. , 2017, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[113]  J. Ajani,et al.  Clinical Significance of Four Molecular Subtypes of Gastric Cancer Identified by The Cancer Genome Atlas Project , 2017, Clinical Cancer Research.

[114]  Yulian Wu,et al.  The potential value of serum pepsinogen for the diagnosis of atrophic gastritis among the health check-up populations in China: a diagnostic clinical research , 2017, BMC Gastroenterology.

[115]  M. Camilleri,et al.  Gastrointestinal Complications of Obesity. , 2017, Gastroenterology.

[116]  M. Büchler,et al.  Outcomes after extended pancreatectomy in patients with borderline resectable and locally advanced pancreatic cancer , 2016, The British journal of surgery.

[117]  R. Malekzadeh,et al.  Cigarette smoking and gastric cancer in the Stomach Cancer Pooling (StoP) Project , 2016, European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation.

[118]  J. Lagergren,et al.  A model for predicting individuals’ absolute risk of esophageal adenocarcinoma: Moving toward tailored screening and prevention , 2016, International journal of cancer.

[119]  Ming-ming Nie,et al.  Clinicopathologic characteristics and prognostic of gastric cancer in young patients , 2016, Scandinavian journal of gastroenterology.

[120]  S. Nunobe,et al.  Clinicopathological features of gastric cancer in young patients , 2016, Gastric Cancer.

[121]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[122]  M. Inoue,et al.  Prediction of the 10‐year probability of gastric cancer occurrence in the Japanese population: the JPHC study cohort II , 2016, International journal of cancer.

[123]  David Smith,et al.  Mass Spectrometric Analysis of Exhaled Breath for the Identification of Volatile Organic Compound Biomarkers in Esophageal and Gastric Adenocarcinoma , 2015, Annals of surgery.

[124]  E. Oki,et al.  Carbohydrate antigen 19-9 is a useful prognostic marker in esophagogastric junction adenocarcinoma , 2015, Cancer medicine.

[125]  H. Sugihara,et al.  Two distinct etiologies of gastric cardia adenocarcinoma: interactions among pH, Helicobacter pylori, and bile acids , 2015, Front. Microbiol..

[126]  Jaw-Town Lin,et al.  A tool to predict risk for gastric cancer in patients with peptic ulcer disease on the basis of a nationwide cohort. , 2015, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[127]  R. Wong,et al.  Ethnic Disparities in Gastric Cancer Incidence and Survival in the USA: An Updated Analysis of 1992–2009 SEER Data , 2014, Digestive Diseases and Sciences.

[128]  Sharmila Anandasabapathy,et al.  Gastric Cancer: Descriptive Epidemiology, Risk Factors, Screening, and Prevention , 2014, Cancer Epidemiology, Biomarkers & Prevention.

[129]  H. Liang,et al.  Characteristics and prognosis of gastric cancer in patients aged ≥ 70 years. , 2013, World journal of gastroenterology.

[130]  Lu Leng,et al.  PalmHash Code vs. PalmPhasor Code , 2013, Neurocomputing.

[131]  H. Lang,et al.  Endoscopic and surgical resection of T1a/T1b esophageal neoplasms: a systematic review. , 2013, World Journal of Gastroenterology.

[132]  F. Cappuccio,et al.  Habitual salt intake and risk of gastric cancer: a meta-analysis of prospective studies. , 2012, Clinical nutrition.

[133]  J. Kitayama,et al.  Clinical significance of CA125 and CA72-4 in gastric cancer with peritoneal dissemination , 2012, Gastric Cancer.

[134]  C. la Vecchia,et al.  A meta-analysis on alcohol drinking and esophageal and gastric cardia adenocarcinoma risk. , 2012, Annals of oncology : official journal of the European Society for Medical Oncology.

[135]  A. Vickers Prediction models: revolutionary in principle, but do they do more good than harm? , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[136]  K. Miki Gastric cancer screening by combined assay for serum anti-Helicobacter pylori IgG antibody and serum pepsinogen levels — “ABC method” , 2011, Proceedings of the Japan Academy. Series B, Physical and biological sciences.

[137]  A. Kızıltunç,et al.  Usefulness of serum pepsinogen levels as a screening test for atrophic gastritis and gastric cancer. , 2010, The Eurasian journal of medicine.

[138]  C. Abnet,et al.  The Gastric Cardia Is Not a Target for Human Papillomavirus–Induced Carcinogenesis , 2010, Cancer Epidemiology, Biomarkers & Prevention.

[139]  S. Narod,et al.  Family history and the risk of gastric cancer , 2009, British Journal of Cancer.

[140]  C. Earle,et al.  Treatment and outcomes of gastric cancer among United States‐born and foreign‐born Asians and Pacific Islanders , 2009, Cancer.

[141]  David J Lee,et al.  Cancer Incidence in First Generation U.S. Hispanics: Cubans, Mexicans, Puerto Ricans, and New Latinos , 2009, Cancer Epidemiology, Biomarkers & Prevention.

[142]  M. Kubo,et al.  Population-based prospective study of the combined influence of cigarette smoking and Helicobacter pylori infection on gastric cancer incidence: the Hisayama Study. , 2008, American journal of epidemiology.

[143]  Chengwei Tang,et al.  A rising trend of gastric cardia cancer in Gansu Province of China. , 2008, Cancer letters.

[144]  C. Abnet,et al.  A prospective study of tobacco, alcohol, and the risk of esophageal and gastric cancer subtypes. , 2007, American journal of epidemiology.

[145]  Xiaocheng Wu,et al.  Incidence of esophageal and gastric cancers among Hispanics, non-Hispanic whites and non-Hispanic blacks in the United States: subsite and histology differences , 2007, Cancer Causes & Control.

[146]  S. Kikuchi,et al.  Highly salted food and mountain herbs elevate the risk for stomach cancer death in a rural area of Japan , 2006, Journal of gastroenterology and hepatology.

[147]  M. Blaser,et al.  Opposing risks of gastric cardia and noncardia gastric adenocarcinomas associated with Helicobacter pylori seropositivity. , 2006, Journal of the National Cancer Institute.

[148]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[149]  A. Neugut,et al.  Epidemiology of gastric cancer. , 2006, World journal of gastroenterology.

[150]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[151]  S. Horvath,et al.  Statistical Applications in Genetics and Molecular Biology , 2011 .

[152]  Shoichiro Tsugane,et al.  Salt, salted food intake, and risk of gastric cancer: Epidemiologic evidence , 2005, Cancer science.

[153]  L. Bernstein,et al.  Hiatal hernia, reflux symptoms, body size, and risk of esophageal and gastric adenocarcinoma , 2003, Cancer.

[154]  M. Daly,et al.  PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes , 2003, Nature Genetics.

[155]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[156]  Jason H. Moore,et al.  Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions , 2003, Bioinform..

[157]  W. Chow,et al.  Risk of adenocarcinomas of the esophagus and gastric cardia in patients with gastroesophageal reflux diseases and after antireflux surgery. , 2001, Gastroenterology.

[158]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[159]  J. H. Moore,et al.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. , 2001, American journal of human genetics.

[160]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[161]  L. Hansson,et al.  Tobacco, alcohol and the risk of gastric cancer by sub‐site and histologic type , 1999, International journal of cancer.

[162]  S. Schwartz,et al.  The incidence of gastric carcinoma in Asian migrants to the United States and their descendants , 1999, Cancer Causes & Control.

[163]  B. Wong,et al.  Epidemiology of gastric cancer in relation to diet and Helicobacter pylori infection , 1998, Journal of gastroenterology and hepatology.

[164]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[165]  H. Adami,et al.  Tobacco, alcohol and the risk of gastric cancer. A population‐based case‐control study in Sweden , 1994, International journal of cancer.

[166]  P. Correa,et al.  Phenotypic and genotypic events in gastric carcinogenesis. , 1994, Cancer research.

[167]  E. Wynder,et al.  Tobacco, alcohol intake, and diet in relation to adenocarcinoma of the esophagus and gastric cardia , 1993, Cancer Causes & Control.

[168]  P. Correa,et al.  Human gastric carcinogenesis: a multistep and multifactorial process--First American Cancer Society Award Lecture on Cancer Epidemiology and Prevention. , 1992, Cancer research.

[169]  Y. Yang,et al.  Construction and Evaluation of Gastric Cancer Risk Prediction Model , 2021, Indian Journal of Pharmaceutical Sciences.

[170]  Kazuyoshi Yamamoto,et al.  Validation of an assessment tool: Estimation of Postoperative Overall Survival for Gastric Cancer. , 2018, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[171]  Ming Li,et al.  Dual-source discrimination power analysis for multi-instance contactless palmprint recognition , 2015, Multimedia Tools and Applications.

[172]  D. Chung,et al.  Familial Gastric Cancers: A Review With Focus on Hereditary Diffuse Gastric Cancer Syndrome , 2014 .

[173]  H. Shimada,et al.  Clinical significance of serum tumor markers for gastric cancer: a systematic review of literature by the Task Force of the Japanese Gastric Cancer Association , 2013, Gastric Cancer.

[174]  Shou-En Lu,et al.  Cancer incidence among Korean-American immigrants in the United States and native Koreans in South Korea. , 2007, Cancer control : journal of the Moffitt Cancer Center.

[175]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[176]  G. Garrido Cantarero,et al.  [The area under the ROC curve]. , 1996, Medicina clinica.

[177]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .