Inconsistencies in predictive models based on exhaled volatile organic compounds for distinguishing between benign pulmonary nodules and lung cancer: a systematic review
Notice bibliographique
Résumé
BACKGROUND: There is a general rise in incidentally found pulmonary nodules (PNs) requiring follow-up due to increased CT use. Biopsy and repeated CT scan are the most useful methods for distinguishing between benign PNs and lung cancer, while they are either invasive or involves radiation exposure. Therefore, there has been increasing interest in the analysis of exhaled volatile organic compounds (VOCs) to distinguish between benign PNs and lung cancer because it's cheap, noninvasive, efficient, and easy-to-use. However, the exact value of breath analysis in this regard remains unclear. METHODS: A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-oriented systematic search was performed to include studies that established exhaled VOC-based predictive models to distinguish between benign PNs and lung cancer and reported the exact VOCs used. Data regarding study characteristics, performance of the models, which predictors were incorporated, and methodologies for breath collection and analysis were independently extracted by two researchers. The exhaled VOCs incorporated into the predictive models were narratively synthesized, and those compounds that were reported in > 2 studies and reportedly exhibited consistent associations with lung cancer were considered key breath biomarkers. A quality assessment was independently performed by two researchers using both the Newcastle-Ottawa Scale (NOS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: A total of 11 articles reporting on 46 VOC-based predictive models were included. The majority relied solely on exhaled VOCs (n = 44), while two incorporated VOCs, demographical factors, and radiological signs. The variation in the sensitivity, specificity, and AUC indicators of the models that incorporated multiple factors was lower compared with those of the models that relied solely on exhaled VOCs. A total of 84 VOCs were incorporated. Of these, 2-butanone, 3-hydroxy-2-butanone, and 2-hydroxyacetaldehyde were identified as key predictors that had significantly higher concentrations in the exhaled breath samples of patients with lung cancer. Substantial heterogeneity was observed in terms of the modeling and validation methods used, as well as the approaches to breath collection and analysis. Many of the reports were missing certain key pieces of clinical and methodological information. CONCLUSIONS: Although exhaled VOC-based models for predicting cancer risk might be a conceivable role as monitoring tools for PNs risk, there has been little overall change in the accuracy of these tests over time, and their role in routine clinical practice has not yet been established. CLINICAL TRIAL NUMBER: PROSPERO registration number CRD42023381458.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».