Breast cancer detection using a realtime breath analyzer: A pilot study.
Notice bibliographique
Résumé
e13040 Background: Despite advances in mammography, limitations persist related to accuracy among challenging cases (e.g. dense breast tissue) and screening adherence. Volatile organic compounds (VOCs) associated with breast cancer have been identified in the exhaled breath, however, analytical tools such as gas chromatography-mass spectrometry for VOC analysis, are neither generalizable nor scalable. A breathomics device, called DiagNoze, that leverages a novel digital olfaction platform to fingerprint complex mixtures of VOCs, may offer a portable and non-invasive option for breast cancer detection. Methods: Patients with suspicious findings on a breast image or examination, presenting to the McGill University Hospital Centre (MUHC) Breast Center for diagnostic testing, were recruited into the study. Patients with a history of asthma, COPD, diabetes mellitus, cigarette smoking, or those with concurrent cancer or who were actively receiving chemotherapy, were excluded from the study. Participants were excluded if they consumed alcohol or recreational drugs within 8 hours of recruitment or food or liquids, other than water, within one hour. The DiagNoze device captured up to 5 alveolar breath samples per study participant. Digitized breath fingerprints represented by a 32 dimensional (D) time series were collected for each breath sample. Samples were labelled as either positive or negative for breast cancer based on biopsy results. A t-distributed stochastic neighbor embedding (T-SNE) dimensional reduction method was applied to convert the 32D datasets into 2D latent space plots, and a fitted model was applied to determine data clustering accuracy. The fitted model performance was evaluated for all patients, and a patient subgroup with high breast density. Results: A total of 182 patients were recruited, with 156 meeting study inclusion criteria, with biopsy results and with at least one breath sample meeting data curation criteria (56 positive, 100 negative, average number of breath samples of 3.4). Of those, 125 (41 positive, 84 negative) had highly dense breast parenchyma (ACR C or D). The positive cases had a cancer stage distribution, from 0 to 3 of: 7, 25, 20, and 1, with 3 not reported. The data shows clear separation between positive cases and controls using a T-SNE clustering method, with clustering performance shown. Conclusions: This study demonstrates that classification of breast cancer status from alveolar breath samples is possible using the DiagNoze device, independent of breast density. DiagNoze has the potential to diagnose the presence of breast cancer and could be used for diagnosis and for followup of patients post-treatment. Model performance. Population Sensitivity Specificity PPV NPV All Patients 84% 89% 81% 91% Patients with dense breast parenchyma 78% 90% 79% 89%
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».