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

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.

Development and external validation of a quantitative diagnostic model for malignant gastric lesions in clinical opportunistic screening: A multicenter real-world study

BACKGROUND

Clinical opportunistic screening is a cost-effective cancer screening modality. This study aimed to establish an easy-to-use diagnostic model serving as a risk stratification tool for identification of individuals with malignant gastric lesions for opportunistic screening.

METHODS

We developed a questionnaire-based diagnostic model using a joint dataset including two clinical cohorts from northern and southern China. The cohorts consisted of 17,360 outpatients who had undergone upper gastrointestinal endoscopic examination in endoscopic clinics. The final model was derived based on unconditional logistic regression, and predictors were selected according to the Akaike information criterion. External validation was carried out with 32,614 participants from a community-based randomized controlled trial.

RESULTS

This questionnaire-based diagnostic model for malignant gastric lesions had eight predictors, including advanced age, male gender, family history of gastric cancer, low body mass index, unexplained weight loss, consumption of leftover food, consumption of preserved food, and epigastric pain. This model showed high discriminative power in the development set with an area under the receiver operating characteristic curve (AUC) of 0.791 (95% confidence interval [CI]: 0.750-0.831). External validation of the model in the general population generated an AUC of 0.696 (95% CI: 0.570-0.822). This model showed an ideal ability for enriching prevalent malignant gastric lesions when applied to various scenarios.

CONCLUSION

This easy-to-use questionnaire-based model for diagnosis of prevalent malignant gastric lesions may serve as an effective prescreening tool in clinical opportunistic screening for gastric cancer.

Evolving Concepts in Helicobacter pylori Management

Helicobacter pylori is the most common chronic bacterial infection worldwide and the most significant risk factor for gastric cancer, which remains a leading cause of cancer-related death globally. H pylori and gastric cancer continue to disproportionately impact racial and ethnic minority and immigrant groups in the United States. The approach to H pylori case-finding thus far has relied on opportunistic testing based on symptoms or high-risk indicators, such as racial or ethnic background and family history. However, this approach misses a substantial proportion of individuals infected with H pylori who remain at risk for gastric cancer because most infections remain clinically silent. Moreover, individuals with chronic H pylori infection are at risk for gastric preneoplastic lesions, which are also asymptomatic and only reliably diagnosed using endoscopy and biopsy. Thus, to make a significant impact in gastric cancer prevention, a systematic approach is needed to better identify individuals at highest risk of both H pylori infection and its complications, including gastric preneoplasia and cancer. The approach to H pylori eradication must also be optimized given sharply decreasing rates of successful eradication with commonly used therapies and increasing antimicrobial resistance. With growing acceptance that H pylori should be managed as an infectious disease and the increasing availability of susceptibility testing, we now have the momentum to abandon empirical therapies demonstrated to have inadequate eradication rates. Molecular-based susceptibility profiling facilitates selection of a personalized eradication regimen without necessitating an invasive procedure. An improved approach to H pylori eradication coupled with population-level programs for screening and treatment could be an effective and efficient strategy to prevent gastric cancer, especially in minority and potentially marginalized populations that bear the heaviest burden of H pylori infection and its complications.

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.

Development of a modified ABC method among Helicobacter pylori infected but serum pepsinogen test-negative individuals

BACKGROUND

Although the ABC method for gastric cancer (GC) screening has been widely adopted in Japan, it may not be suitable for other countries due to population heterogeneity and different tumor histology. We aim to develop a modified ABC method to improve GC screening performance, especially among Helicobacter pylori (Hp) infected but serum pepsinogen (sPG) test-negative individuals.

METHODS

A total of 4745 participants were recruited from Tianjin, China, and were classified into four groups by combined assay for Hp infection and sPG concentrations: Group A (Hp [-], PG [-]), Group B (Hp [+], PG [-]), Group C (Hp [+], PG [+]), and Group D (Hp [-], PG [+]). We used receiver-operating characteristic (ROC) curves analysis and minimum p value method to determine the optimal cutoff point for PG II in Group B. We performed logistic regressions to examine the risk of GC across different subgroups. In addition to the derivation set, the performance of the modified ABC method was also evaluated in an external set involving 16,292 participants from Liaoning, China.

RESULTS

In the modified ABC method, we further classified Group B as low-risk (Group B1) and high-risk subgroups (Group B2) using optimal sPG II cutoff point (20.0 ng/mL) by ROC curves analysis and minimum p value method. Compared with Group B1, Group B2 had a significantly higher risk of GC (adjusted OR = 2.54, 95% CI = 1.94-3.33). The modified ABC method showed good discrimination for GC (AUC = 0.61, 95% CI = 0.59-0.63) and improved risk reclassification (NRI = 0.11, p < .01). Similar results were observed in the validation dataset.

CONCLUSIONS

The modified ABC method can effectively identify high-risk population for GC among Hp-infected but sPG test-negative participants in China.

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).