Notice bibliographique
Résumé
Abstract Potential clinical applications of mammographic density include: 1) the prediction of outcome of breast disease, 2) mammographic screening, 3) research and practice of cancer prevention, and 4) improved prediction of risk in individuals. 1) Breast cancer characteristics and clinical outcomes. Extensive percent mammographic density (PMD) is associated with increased risks for the development of most of the histological non-obligate precursors of breast cancer, including ductal carcinoma in situ (DCIS), atypical hyperplasia, hyperplasia without atypia, and columnar cell lesions (CLLs). PMD is associated with an increased risk of both ER+ and ER− tumors, and with larger tumors of higher histological grade. Risk of contralateral second breast cancer is also increased by extensive PMD. However, among women with breast cancer, risk of death from the disease does not appear to be increased by PMD. 2) Mammographic screening. Women with extensive PMD are both at higher risk of breast cancer and at greater risk that cancer will not be detected by mammography. This heterogeneity is not currently recognized in the design of screening programs. Women with extensive PMD, might benefit from screening more often than once every 2 to 3 years and with modalities such as MR or UST in addition to mammography. Conversely, for women with radiolucent breast tissue and a negative screening mammogram, in whom risk is lower and detection easier, re-screening less frequently than every 2 to 3 years with mammography might be safe. Research into these issues is required. 3) Breast cancer prevention trials. Unlike most other risk factors for breast cancer, PMD can be changed), suggesting that it might be used as a surrogate marker in clinical trials of breast cancer prevention. Clinical trials of breast cancer prevention require large numbers of subjects and long periods of observation and thus are expensive. Smaller, shorter, and less expensive trials of prevention strategies would be possible if there were a surrogate marker that allow the identification of interventions that would reduce breast cancer incidence. To be used as a surrogate for breast cancer, a biomarker such as PMD should meet the following criteria: (a) the marker should be associated with risk of breast cancer, (b) the marker should be changed by the intervention, and (c) the change in the marker should mediate the effect of the intervention on breast cancer risk. The extent to which these criteria can currently be met will be discussed. 4) Improved risk prediction. PMD is inversely associated with age, while breast cancer incidence increases with age. We used data from 3250 healthy women aged 15–80 to estimate cumulative breast density (CBD) in the population, which increases with age, and compared this with age-specific incidence of breast cancer in Canada. Log CBD and log breast cancer incidence had a linear association (r = 0.98; p = 6.0 E−9) that was unchanged by adjustment for log age (r = 0.97; p = 8.0 E−7). Log CBD alone was a better predictor of age-specific log breast cancer incidence (r2 = 0.97) than age alone (r2 = 0.88). The observed strong association between CBD and breast cancer incidence suggest that the factors responsible for CBD may be causally related to the age-specific incidence of breast cancer. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr MS2-2.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| 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 | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».