Using breath analysis as a screening tool to detect gastric cancer: A systematic review.
Bibliographic record
Abstract
175 Background: In its early stages, gastric cancer symptoms are frequently lacking, resulting in an often late and incurable diagnosis. A non-invasive, cheap, and reliable screening method for gastric cancer could improve outcomes and increase the number of surgically resectable gastric cancers. Breath analysis has emerged as an experimental method of non-invasive screening of gastric cancer and identification of individuals suitable for confirmatory, diagnostic upper gastrointestinal endoscopy. We aimed to evaluate the accuracy and applicability of breath analysis for gastric cancer detection in adults. Methods: This systematic review searched MEDLINE, EMBASE, BIOSIS, CENTRAL, and Compendex until 11 July 2019 for original studies analyzing exhaled breath to detect gastric cancer in patients. Two authors then independently screened the abstracts, titles, and full texts. Summary sensitivity and specificity analyses were obtained using a hierarchical bivariate method. Positive predictive value and number needed to screen (NNS) of breath analysis methods for gastric cancer detection were calculated for each country using gastric cancer prevalence by country obtained from the Global Cancer Observatory. Non-quantitative results were descriptively summarized. Risk of bias was assessed using the QUADAS-2 tool. This study protocol was pre-registered in PROSPERO (CRD42020139422). Results: Twenty studies were included. Together, the studies included 2,976 subjects. The pooled mean age of the subjects in the gastric cancer groups was 60.5 ± 11 years while the pooled mean age for control groups was 55.4 ± 12 years. Within these twenty studies, breath analysis technologies most commonly used were mass spectrometry (MS)-based methods; other methods included volatile organic compound sensors, thermal desorption tubes, and silicon nanowire field effect transistors. Across all included studies, we found and summarized the characteristics of 131 chemical compounds found in the exhaled breath of study subjects. Eleven studies (total n = 1905) involving all technologies reported quantitative results, with sensitivities ranging from 67-100% and specificities from 71-98%. The summary sensitivity across six studies utilizing MS-based breath analysis methods was 85.3% (95% CI: 82-96%); summary specificity was 81.7%. (95% CI: 78-85%). Based on the MS-based values, we estimated that screening with MS-based breath tests could lower the NNS by more than four-fold in the 15 countries with the highest prevalence of gastric cancer. Conclusions: Breath analysis is a promising method for gastric cancer detection with good diagnostic performance and potential to decrease the NNS for endoscopy-based gastric cancer detection. However, due to the heterogeneity of breath analysis technologies, rigorous studies with standardized, reproducible methods are needed to evaluate the clinical applicability of these technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".