Abstract B66: Optical spectroscopy of the breast: Association between breast cancer risk factors and breast tissue composition
Notice bibliographique
Résumé
Abstract B66 Understanding breast tissue development to identify when it is at high risk for tumorigenesis, particularly in younger women, can lead to increased prevention efforts. Current technologies to evaluate breast tissue are not an optimal option for young women due to high costs, invasiveness and risk of ionizing radiation. We are using a novel technique called optical spectroscopy (OS) to examine breast tissue. OS uses light at the red and near-infrared wavelengths shined through the breast and the light scattering and absorption spectra obtained provide information on the breast content of water, lipid, oxyhemoglobin and deoxyhemoglobin. OS measures have been shown to be related to quantitative mammographic measures (i.e. percent density) and association between mammographic density and breast cancer risk factors has been established previously. The objective of this study was to explore the relationship of OS with breast cancer risk factors (anthropometric measures, reproductive factors, hormonal factors, physical activity and family history). Women were recruited in three groups: nulliparous women aged 18-21 (group 1), nulliparous women aged 31-40 (group 2), and parous women aged 31-40 who had given birth prior to age 30 (group 3). All women completed a brief questionnaire on their breast cancer risk factors and underwent OS examination. Measurements were made at four standard positions on each breast for each woman (8 total). Principal components analysis (PCA) was used to derive the four main components from the spectral data and four scores were derived indicating the contribution of each of the four principal components to each individual’s spectra. The scores were averaged over all four positions on both breasts for each woman resulting in four scores, t1-t4, for each woman. We present results from an initial group of 259 women, 137 in group 1, 94 in group 2, and 28 in group 3. Linear regression models were used to look for associations between breast cancer risk factors and the t scores for all groups combined, in groups 2 and 3 combined and in group 1 alone. Multivariate linear regression models were created to adjust for BMI, ethnicity and group and parity differences where applicable. Preliminary results demonstrated that when considering all three groups together, t1 and t2 measures were associated with body mass index (p<0.0001); t2 (p=0.001),t3 (0.0005) and t4 (0.002) showed an association with ethnicity. In addition t3 was associated with physical activity in childhood (β=0.17(SE 0.09), p=0.05). T4 was inversely associated with age at menarche (β=-0.02 (SE 0.01), p=0.02), and positively associated with a first degree family history of breast cancer (β=0.09(SE 0.05), p=0.07) and age (β=0.01(SE 0.01), p=0.05). In group 1, t1 showed an inverse relationship with the luteal phase of the menstrual cycle (β=5.1(SE 2.3), p=0.03). In a combined analysis of groups 2 and 3, t2 was inversely associated with the luteal phase of the menstrual cycle (β=-0.34 (SE 0.14), p=0.02) and duration of hormonal contraceptive use (p=0.05). The association between breast cancer risk factors and OS measures from our analysis indicates that the different components of the breast tissue could be a potential indicator of high risk breast tissue. OS may be a useful non-invasive technique to determine breast tissue characteristics associated with breast cancer risk in young women. Citation Information: Cancer Prev Res 2008;1(7 Suppl):B66.
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 | 0,000 | 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) | 0,001 | 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 ».