Using breath analysis as a screening tool to detect gastric cancer: A systematic review.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».