Abstract PO3-07-08: Breast density estimation with a microwave-frequency imaging system
Notice bibliographique
Résumé
Abstract Background: Microwave imaging has been proposed as an alternative method of breast imaging that is low-cost and comfortable for women as it avoids excessive compression. Microwave properties of tissues relate to water content and behavior (Gabriel et al, 1996); specifically, fatty tissues have lower properties and glandular tissues have greater properties (Lazebnik et al, 2007). These differences in microwave signatures of fatty and glandular tissues provide the opportunity to map the composition of the breast and create a density score without a mammogram. This density score may find utility in risk stratification or monitoring interventions aimed at decreasing breast density (Salazar et a, 2020). Purpose: We examine the feasibility of developing a density score based on microwave images that correlates to mammographic breast density (VOLPARA score) in a pilot study with healthy volunteers. Imaging System: We have developed a microwave imaging system that facilitates scanning of large groups of patients, as well as comparison to x-ray mammography (Mojabi et al, 2023). The system consists of two plates which are placed in contact with the breast. Microwave transmitters and receivers are embedded in the plates; signals transmitted through the breast are used to estimate microwave frequency properties of tissues, and maps of these estimates form a 2D image. Methods: 50 patients provided informed consent (study approved by Health Ethics Research Board of Alberta CC-21-0082). Both breasts of each volunteer were scanned. Previously performed mammograms were available for 21 of the volunteers. The number of volunteers with VOLPARA scores A, B, C, and D is 2, 8, 6, and 5, respectively. The percent density reported with the VOLPARA score is also available for these volunteers. Microwave images were formed for each scan and analyzed to predict density with three approaches: (1) average permittivity, (2) segmented regions, and (3) pixel-based intensities. The results demonstrate that the average permittivity of the breast typically increases from VOLPARA A to D, with some overlap in average values observed between the density categories. A correlation between average permittivity and percent breast density was observed. Regions representing glandular tissues are segmented from microwave images; the average values of these regions clearly differentiate between VOLPARA A and D, however do not show consistent ranges for VOLPARA scores B and C. Finally, the microwave breast density estimated using pixel-based intensities shows good correlation with the percentage density calculations from mammograms. Conclusions: Microwave images contain features related to the glandular tissues embedded in fat. By analyzing the composition of the breast in these images, density scores are created. While average permittivity appears to correlate to percent density calculated from mammograms, area or pixel-based approaches appear to have greater potential for categorizing into density classes. Expanding the number of participants, identifying biomarkers, and exploring deep learning techniques for density prediction are considered for future work Citation Format: Elise Fear, Jeremie Bourqui, Pedram Mojabi, Bobbie-Jo Docktor, Anita Garland, Danielle Deutscher, Zahra Lasemiimeni, Kathleen McMahon, Brendon Besler, Roger Tsang. Breast density estimation with a microwave-frequency imaging system [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-08.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 ».