Risk Factors for Breast Malignancy among Women Presenting with Breast Masses in a Teaching Hospital in Lagos, Nigeria
Notice bibliographique
Résumé
Background: Breast cancer is the commonest female malignancy globally with increasing prevalence in Nigeria. This study aimed at identifying factors associated with malignant breast masses among women with breast masses receiving care at the Lagos University Teaching Hospital (LUTH).
 Materials: This cross-sectional, descriptive study investigated 100 women with breast masses by history-taking, clinical breast examination and biopsy for histopathology assessment. Sociodemographic characteristics, family, social, gynecologic, and obstetric history were obtained, and features of breast mass on examination were documented and histology specimens were taken for assessment. The proportion of women with malignant breast masses by biopsy was calculated. Using binary logistic regression analysis, factors associated with malignant breast masses were identified. Significance level was set at p < 0.05.
 Results: The mean age of participants was 39.4±13.4 years. Most were married (63.0%), overweight (53.0%) and had tertiary level of education (58.0%). The right breast was affected in 56% of women, while most breast masses were oval (49.0%). History of breast pain, exclusive breast feeding and nipple retraction was reported in 76.0%, 35.0% and 17.0% respectively. Malignant breast masses were identified in 46.0% of women and associated factors included increasing age (OR=10.95 – 442.00; p<0.05), married women (OR=6.96; p: 0.001), obesity (OR= 16.20; p: 0.003). Clinical symptoms associated with malignant breast mass included history of previous breast mass (OR = 3.93; p: 0.028), breast pain (OR = 3.07; p: 0.023), nipple retraction (OR=7.44;0.003) and nipple discharge (OR = 10.24; 0.003).
 Conclusion: The study found that a high percentage of breast masses assessed by biopsy were malignant among the studied populace. Factors significantly associated with increased risk of malignant breast mass included increasing age, obesity, and marital status. We recommend lifestyle modification as well as adoption of primordial and primary prevention measures in tackling problems related with breast cancer among Nigerian women. This should also include the improvement of access to such services.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 ».