Hotspots and trends of risk factors in gastric cancer: A visualization and bibliometric analysis

Table 8 Research characteristics of gastric cancer risk prediction models

Research characteristics				Model characteristics			Findings	Ref.
Country	Research design	Number of participants	Data types	Model types	AUC	C-index
China	Prospective cohort study	435673	①②③④	c	-	0.736	The GCRS can be an effective risk assessment tool for tailored endoscopic screening of GC in China. RESCUE, an online tool was developed to aid the use of GCRS	Zhu et al[82]
China	Retrospective study	6005	①④⑤⑥⑦	-	0.708	-	Li’s prediction model performs the best for risk stratification in the screening, detection, and diagnosis of GC and precancerous lesions, whereas the overall performance of the other three models is similar	Hu et al[83]
South Korea	Retrospective cohort study	1157	①④⑤⑦	a	0.894	-	The 4-point discriminative model may help identify patients with a normal serological test who are nonetheless at risk of developing GC	Cho et al[84]
China	Cohort study	89568	①②④⑨	b	0.97	-	This model could enable a potentially more cost- effective endoscopic surveillance program, as well as to exclude very low-risk patients from unnecessary surveillance	Leung et al[85]
China	Retrospective cohort study	2287	①②④⑩	a	0.684	-	The present study established a predictive model to assess the risk of GC using high-evidence genetic variants and detected the potential gene-environment interaction, which may be helpful in prevention of the cancer	Qiu et al[86]
China	Case-control study	383	①②③④⑤	a	0.883	-	This model is simple, convenient, and economical, has good patient compliance, is easy to implement clinically, is easy to concentrate medical resources, and is expected to identify high-risk groups at an early stage, then to increase the detection rate of GC	Tao et al[87]
America	Case-control study	140	①②③④⑦	a	0.9495	-	The addition of ethnic and cultural variables, particularly the immigration/generation, to conventional risk factor variables improved the ability of models to identify individuals at high risk for GC	In et al[88]
Japan	Case-control study	1431	①④⑦⑧	b	0.899	-	XGBoost outperformed logistic regression and showed the highest AUC value	Taninaga et al[89]
China	Cross-sectional study	14929	①②③④⑤⑥⑦	a	0.76	-	The prediction rule had good performance and showed significantly better discrimination ability to identify a patient with GC than three other alternative prediction methods	Cai et al[90]
Japan	Cohort study	5648	①②⑦⑧	c	0.790	-	We developed a risk assessment tool for gastric cancer that provides a useful guide for stratifying an individual’s risk of future gastric cancer	Iida et al[91]
China	Cross-sectional study	12112	①②③④⑤⑥⑦	c	0.811	-	A serological biopsy composed of the five stomach-specific circulating biomarkers could be used to identify high-risk individuals for further diagnostic gastroscopy, and to stratify individuals’ risk of developing GC and thus to guide targeted screening and precision prevention	Tu et al[92]
Japan	Prospective cohort study	19028	①②③④⑤⑦	c	-	0.777	In this study, the authors developed a model and a simple scoring system to estimate an individual's risk of developing GC, based on factors such as H. pylori antibodies, serum pepsinogen levels, and lifestyle habits	Charvat et al[93]
South Korea	Case-control study	217	①②③④	a	0.888	-	This study provides the first predictive model for assessing the risk factors for GC in Korea, where the incidence rate of GC is high. This study has also identified new risk factors for GC, such as drinking tap water	Lee et al[94]

^aLogistic regression model.

^bMachine learning algorithm

^cCox proportional hazards model.

① demographic data; ② personal history; ③ family history; ④ gastroscopy; ⑤ pepsinogen; ⑥ gastrin-17; ⑦ Helicobacter pylori infection status; ⑧ HbA1c; ⑨ medication history; ⑩ single nucleotide polymorphisms.

XGBoost: Extreme gradient boosting; GC: Gastric cancer; AUC: Area under the curve; RESCUE: Risk Evaluation for Stomach Cancer by Yourself; GCRS: GC risk score; H. pylori: Helicobacter pylori.