Utilizing Cultural and Ethnic Variables in Screening Models to Identify Individuals at High Risk for Gastric Cancer: A Pilot Study

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

BACKGROUND

The lack of specific symptoms of gastric cancer (GC) causes great challenges in its early diagnosis. Thus it is essential to identify the risk factors for early diagnosis and treatment of GC and to improve the survival rates.

AIM

To assist physicians in identifying changes in the output of publications and research hotspots related to risk factors for GC, constructing a list of key risk factors, and providing a reference for early identification of patients at high risk for GC.

METHODS

Research articles on risk factors for GC were searched in the Web of Science core collection, and relevant information was extracted after screening. The literature was analyzed using Microsoft Excel 2019, CiteSpace V, and VOSviewer 1.6.18.

RESULTS

A total of 2514 papers from 72 countries and 2507 research institutions were retrieved. China (n = 1061), National Cancer Center (n = 138), and Shoichiro Tsugane (n = 36) were the most productive country, institution, or author, respectively. The research hotspots in the study of risk factors for GC are summarized in four areas, namely: Helicobacter pylori (H. pylori) infection, single nucleotide polymorphism, bio-diagnostic markers, and GC risk prediction models.

CONCLUSION

In this study, we found that H. pylori infection is the most significant risk factor for GC; single-nucleotide polymorphism (SNP) is the most dominant genetic factor for GC; bio-diagnostic markers are the most promising diagnostic modality for GC. GC risk prediction models are the latest current research hotspot. We conclude that the most important risk factors for the development of GC are H. pylori infection, SNP, smoking, diet, and alcohol.

RE.GA.IN.: the Real-world Gastritis Initiative-updating the updates

At the end of the last century, a far-sighted 'working party' held in Sydney, Australia addressed the clinicopathological issues related to gastric inflammatory diseases. A few years later, an international conference held in Houston, Texas, USA critically updated the seminal Sydney classification. In line with these initiatives, Kyoto Global Consensus Report, flanked by the Maastricht-Florence conferences, added new clinical evidence to the gastritis clinicopathological puzzle.The most relevant topics related to the gastric inflammatory diseases have been addressed by the Real-world Gastritis Initiative (RE.GA.IN.), from disease definitions to the clinical diagnosis and prognosis. This paper reports the conclusions of the RE.GA.IN. consensus process, which culminated in Venice in November 2022 after more than 8 months of intense global scientific deliberations. A forum of gastritis scholars from five continents participated in the multidisciplinary RE.GA.IN. consensus. After lively debates on the most controversial aspects of the gastritis spectrum, the RE.GA.IN. Faculty amalgamated complementary knowledge to distil patient-centred, evidence-based statements to assist health professionals in their real-world clinical practice. The sections of this report focus on: the epidemiology of gastritis; Helicobacter pylori as dominant aetiology of environmental gastritis and as the most important determinant of the gastric oncogenetic field; the evolving knowledge on gastric autoimmunity; the clinicopathological relevance of gastric microbiota; the new diagnostic horizons of endoscopy; and the clinical priority of histologically reporting gastritis in terms of staging. The ultimate goal of RE.GA.IN. was and remains the promotion of further improvement in the clinical management of patients with gastritis.

Prediction algorithm for gastric cancer in a general population: A validation study

BACKGROUND

Worldwide, gastric cancer is a leading cause of cancer incidence and mortality. This study aims to devise and validate a scoring system based on readily available clinical data to predict the risk of gastric cancer in a large Chinese population.

METHODS

We included a total of 6,209,697 subjects aged between 18 and 70 years who have received upper digestive endoscopy in Hong Kong from 1997 to 2018. A binary logistic regression model was constructed to examine the predictors of gastric cancer in a derivation cohort (n = 4,347,224), followed by model evaluation in a validation cohort (n = 1,862,473). The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic (ROC) curve.

RESULTS

Age, male gender, history of Helicobacter pylori infection, use of proton pump inhibitors, non-use of aspirin, non-steroidal anti-inflammatory drugs (NSAIDs), and statins were significantly associated with gastric cancer. A scoring of ≤8 was designated as "average risk (AR)". Scores at 9 or above were assigned as "high risk (HR)". The prevalence of gastric cancer was 1.81% and 0.096%, respectively, for the HR and LR groups. The AUC for the risk score in the validation cohort was 0.834, implying an excellent fit of the model.

CONCLUSIONS

This study has validated a simple, accurate, and easy-to-use scoring algorithm which has a high discriminatory capability to predict gastric cancer. The score could be adopted to risk stratify subjects suspected as having gastric cancer, thus allowing prioritized upper digestive tract investigation.

Real-World Practice of Gastric Cancer Prevention and Screening Calls for Practical Prediction Models

INTRODUCTION

Some gastric cancer prediction models have been published. Still, the value of these models for application in real-world practice remains unclear. We aim to summarize and appraise modeling studies for gastric cancer risk prediction and identify potential barriers to real-world use.

METHODS

This systematic review included studies that developed or validated gastric cancer prediction models in the general population.

RESULTS

A total of 4,223 studies were screened. We included 18 development studies for diagnostic models, 10 for prognostic models, and 1 external validation study. Diagnostic models commonly included biomarkers, such as Helicobacter pylori infection indicator, pepsinogen, hormone, and microRNA. Age, sex, smoking, body mass index, and family history of gastric cancer were frequently used in prognostic models. Most of the models were not validated. Only 25% of models evaluated the calibration. All studies had a high risk of bias, but over half had acceptable applicability. Besides, most studies failed to clearly report the application scenarios of prediction models.

DISCUSSION

Most gastric cancer prediction models showed common shortcomings in methods, validation, and reports. Model developers should further minimize the risk of bias, improve models' applicability, and report targeting application scenarios to promote real-world use.

Prediction models for gastric cancer risk in the general population: a systematic review

Risk prediction models for gastric cancer (GC) could identify high-risk individuals in the general population. The objective of this study was to systematically review the available evidence about the construction and verification of GC predictive models. We searched PubMed, Embase, and Cochrane Library databases for articles that developed or validated GC risk prediction models up to November 2021. Data extracted included study characteristics, predictor selection, missing data, and evaluation metrics. Risk of bias (ROB) was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We identified a total of 12 original risk prediction models that fulfilled the criteria for analysis. The area under the receiver operating characteristic curve ranged from 0.73 to 0.93 in derivation sets (n=6), 0.68 to 0.90 in internal validation sets (n=5), 0.71 to 0.92 in external validation sets (n=7). The higher-performing models usually include age, salt preference, Helicobacter pylori, smoking, BMI, family history, pepsinogen and sex. According to PROBAST, at least one domain with a high ROB was present in all studies mainly due to methodologic limitations in the analysis domain. In conclusion, although some risk prediction models including similar predictors have displayed sufficient discriminative abilities, many have a high ROB due to methodological limitations and are not externally validated efficiently. Future prediction models should adherence to well-established standards and guidelines to benefit GC screening.