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Clinical and Translational... Feb 2023Some 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...
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.
Topics: Humans; Stomach Neoplasms; Helicobacter Infections; Early Detection of Cancer; Helicobacter pylori; Prognosis
PubMed: 36413795
DOI: 10.14309/ctg.0000000000000546 -
Advances in Therapy Feb 2021Detecting upper gastrointestinal (GI) cancers in primary care is challenging, as cancer symptoms are common, often non-specific, and most patients presenting with these... (Review)
Review
INTRODUCTION
Detecting upper gastrointestinal (GI) cancers in primary care is challenging, as cancer symptoms are common, often non-specific, and most patients presenting with these symptoms will not have cancer. Substantial investment has been made to develop biomarkers for cancer detection, but few have reached routine clinical practice. We aimed to identify novel biomarkers for upper GI cancers which have been sufficiently validated to be ready for evaluation in low-prevalence populations.
METHODS
We systematically searched MEDLINE, Embase, Emcare, and Web of Science for studies published in English from January 2000 to October 2019 (PROSPERO registration CRD42020165005). Reference lists of included studies were assessed. Studies had to report on second measures of diagnostic performance (beyond discovery phase) for biomarkers (single or in panels) used to detect pancreatic, oesophageal, gastric, and biliary tract cancers. We included all designs and excluded studies with less than 50 cases/controls. Data were extracted on types of biomarkers, populations and outcomes. Heterogeneity prevented pooling of outcomes.
RESULTS
We identified 149 eligible studies, involving 22,264 cancer cases and 49,474 controls. A total of 431 biomarkers were identified (183 microRNAs and other RNAs, 79 autoantibodies and other immunological markers, 119 other proteins, 36 metabolic markers, 6 circulating tumour DNA and 8 other). Over half (nā=ā231) were reported in pancreatic cancer studies. Only 35 biomarkers had been investigated in at least two studies, with reported outcomes for that individual marker for the same tumour type. Apolipoproteins (apoAII-AT and apoAII-ATQ), and pepsinogens (PGI and PGII) were the most promising biomarkers for pancreatic and gastric cancer, respectively.
CONCLUSION
Most novel biomarkers for the early detection of upper GI cancers are still at an early stage of matureness. Further evidence is needed on biomarker performance in low-prevalence populations, in addition to implementation and health economic studies, before extensive adoption into clinical practice can be recommended.
Topics: Biomarkers; Early Detection of Cancer; Gastrointestinal Neoplasms; Humans; Pancreatic Neoplasms; Prevalence
PubMed: 33306189
DOI: 10.1007/s12325-020-01571-z -
Cancer Prevention Research... May 2022Risk prediction models for gastric cancer could identify high-risk individuals in the general population. The objective of this study was to systematically review the...
UNLABELLED
Risk prediction models for gastric cancer 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 gastric cancer predictive models. We searched PubMed, Embase, and Cochrane Library databases for articles that developed or validated gastric cancer 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 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 (AUC) 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, body mass index, 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 methodologic limitations and are not externally validated efficiently. Future prediction models should adherence to well-established standards and guidelines to benefit gastric cancer screening.
PREVENTION RELEVANCE
Through systematical reviewing available evidence about the construction and verification of gastric cancer predictive models, we found that most models have a high ROB due to methodologic limitations and are not externally validated efficiently. Future prediction models are supposed to adherence to well-established standards and guidelines to benefit gastric cancer screening.
Topics: Bias; Early Detection of Cancer; Helicobacter pylori; Humans; Risk Assessment; Stomach Neoplasms
PubMed: 35017181
DOI: 10.1158/1940-6207.CAPR-21-0426 -
Acta Bio-medica : Atenei Parmensis Dec 2018Upper-GI diseases are one of the most relevant issue in primary care. Nowadays they are still responsible for about 100 million ambulatory care visits only in the US....
Upper-GI diseases are one of the most relevant issue in primary care. Nowadays they are still responsible for about 100 million ambulatory care visits only in the US. The diagnosis of almost every upper-GI condition is still deputed to invasive tests such as upper gastrointestinal endoscopy, gastroesophageal manometry or radiography. The possibility of analysing serum markers like Pepsinogens I and II, produced by gastric mucosa, in order to assess the functional characteristics of the upper GI tract has spread itself since the 80's especially in the diagnosis of peptic ulcer. The discovery of Helicobacter pylori by Marshall and Warren in 1983 and the scientific consecration of its role in the pathogenesis of gastric cancer and peptic ulcer (crystallized in Peleo Correa's Cascade, 1992), led to an increase importance of non-invasive tests, raising the attention towards the assessment of both immunoglobulins anti-H.p. and Gastrin hormone produced by antral G cells, as an implementation of the panel of gastric markers. This narrative review aims to analyze the huge landscape of non-invasive tests for diagnosis of GI diseases, studying the literature of the recent years.
Topics: Antibodies, Bacterial; Biomarkers; Diagnostic Techniques, Digestive System; Dyspepsia; Endoscopy, Gastrointestinal; Esophageal Diseases; Gastrins; Helicobacter Infections; Helicobacter pylori; Humans; Pepsinogens; Stomach Diseases
PubMed: 30561417
DOI: 10.23750/abm.v89i8-S.7917