Trends and outcomes in patients receiving neoadjuvant chemotherapy for breast cancer in Ontario: A population-based study.
Notice bibliographique
Résumé
e12664 Background: Modern neoadjuvant chemotherapy (NAC) regimens in breast cancer offer higher rates of pathologic complete response, an ability to guide further adjuvant treatment, and may de-escalate surgery. However, the utilization of NAC has been heterogeneous in Ontario. This study aimed to describe trends in NAC use in Ontario, report survival outcomes, and explore factors associated with overall survival in these patients. Methods: This was a population-based cohort study using linked health administrative data in Ontario, Canada. From the Ontario Cancer Registry, we identified women ≥18 years who underwent neoadjuvant chemotherapy followed by surgery for operable (cT1N1, cT2-3N0, or cT2-3N1) breast cancer between 2012 and 2020. Patients were divided by receptor subtype (triple negative breast cancer [TNBC], ER+/HER2-, ER+/HER2+, ER-/HER2+) and characteristics were compared using standardized mean differences (SMDs). Five-year overall survival (OS) and breast cancer-specific survival (BCSS) were determined and reported for each subtype. Associations with OS and BCSS were calculated for patient and disease characteristics using univariate and multivariable Cox proportional hazards models and Fine and Gray models, respectively. Results: 3,804 women underwent NAC for cT1N1, cT2-3N0, or cT2-3N1 breast cancer in Ontario between 2012 and 2020. The largest contributing subtype was ER+/HER2- patients (39.3%) followed by those with HER2+ disease (ER-/HER2+ 13.8%, ER+/HER2+ 23.4%), and TNBC (23.5%). The median age was 50 years (IQR 42-59 years). Most patients were clinically node-positive (2,736; 71.9%), and underwent mastectomy (2,397; 63.0%). There was a significant increase in the number of patients receiving NAC in Ontario, from 217 in 2012 to 667 in 2019 ( p<0.001), with a greater increase among patients with TNBC or HER2+ disease. Compared to those with ER+/HER2- disease, TNBC patients were treated for smaller tumours (28.6% T3 vs. 39.8%; SMD = 0.24), and were more likely to be node-negative (39.1% N0 vs. 22.1%; SMD = 0.38). Similar trends for nodal status were found for patients with ER-/HER2+ (26.7% N0) and ER+/HER2+ cancer (27.9% N0). 5-year OS for the entire cohort was 88.1% (95% CI 87.1 – 89.2%). Survival was highest for ER+/HER2+ patients (94.2%, 95% CI 92.6 – 95.8%) and lowest for TNBC patients (80.1%, 95% CI 77.5 – 82.8%). Similar patterns were seen for BCSS. Factors associated with OS in a multivariable model included older age (increase in 5 years HR 1.1, 95% CI 1.07 – 1.14), N1 status (HR 1.99, 95% CI 1.61 – 2.47), larger tumour size (T3 HR 1.83, 95% CI 1.34 – 2.5), mastectomy (HR 1.45, 95% CI 1.2 – 1.75), and TNBC (versus ER+/HER2- HR 1.9, 95% CI 1.56 – 2.32). Conclusions: The use of NAC has increased in Ontario, particularly among TNBC and HER2+ patients. Women with TNBC have worse outcomes compared to those with ER+/HER2- disease, despite being treated for less advanced disease.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».