Impact of concurrent (CON) and sequential (SEQ) radiotherapy (RT) with adjuvant aromatase inhibitors (AI) in early-stage breast cancer (EBC): NCIC CTG MA.27.
Notice bibliographique
Résumé
500 Notice of Retraction: "Impact of concurrent (CON) and sequential (SEQ) radiotherapy (RT) with adjuvant aromatase inhibitors (AI) in early-stage breast cancer (EBC): NCIC CTG MA.27." Abstract 500, published in the 2012 Annual Meeting Proceedings Part I, a supplement to the Journal of Clinical Oncology, has been retracted by Wendy Parulekar, MD, and Timothy J. Whelan, BM, BCh, MSc, on behalf of all authors of the abstract. The abstract concluded by suggesting that concurrent administration of an AI during the period of radiation may improve event-free survival as compared to commencing AI after completing radiation therapy. After submitting the abstract for the 2012 ASCO Annual Meeting, the authors identified issues associated with the statistical analysis of this research, which led them to reanalyze the data and in so doing, they reached different conclusions from those described in the abstract. As opposed to the abstract, which reports a hazard ratio (HR) of AI administration that is concurrent with RT vs. sequential to RT of 0.78 (p=0.001), they have determined that using a more appropriate analysis, the hazard ratio is 0.84 (p=0.13). Multiple sensitivity analyses have been performed and yield hazard ratios of 0.81-0.84 and p values of 0.07 to 0.13. In view of these findings, the conclusions reported in the abstract cannot be supported. Background: Optimal timing of administration of adjuvant (adj) RT and AI in EBC is unknown. Methods: MA.27 was a phase III RCT of exemestane to anastrozole in postmenopausal women with hormone receptor positive EBC (Goss et al. Cancer Res. 70(24, Suppl):75s, 2010). The final trial database was used for this retrospective analysis. Median follow-up was 4.1 years. MA.27 patients received CON-AI [any overlap with AI; 4233 (57%) patients], SEQ-AI [RT preceded AI, no overlap with AI; 1010 (14%) patients] and No RT [AI only; 2128 (29%) patients]. Outcome measures for this analysis were: event free survival (EFS; time to locoregional or distant disease recurrence, new primary BC, or death from any cause), locoregional recurrence (LRFS), distant recurrence (DDFS) and overall survival (OS). RT groups were compared univariately (uni) with stratified log-rank tests, and multivariately (multi) with step-wise stratified Cox regression adjusted by stratification factors: nodal status, adj chemotherapy (chemo), celecoxib, aspirin, and trastuzumab. Results: 7371 eligible women received AI; were included in the analysis; and 71% (5243) received adj RT. CON-AI and SEQ-AI groups were comparable by median age (63 v 63), proportion T1 tumours (75% v 75 %), and mastectomy rate (10% v 11%). The frequency of axillary dissection for CON-AI and SEQ-AI was 48% v 44%, proportion N0 was 73% v 69%, and proportion receiving adj chemo 29% v 41%. CON-AI had similar uni results to SEQ-AI: EFS, HR=0.86, p=0.20; LRFS, HR=0.82, p=0.51; DDFS, HR=0.92, p=0.59; and OS, HR=1.04, p=0.80. In multi analyses, CON-AI had better EFS than SEQ-AI patients [stratified HR of CON-AI to SEQ-AI 0.78 (0.66 – 0.91), p=0.001]; as well, age≥70 (p<0.0001), ECOG PS≥1 (p<0.0001), L-sided tumours (p=0.02), T2-T4 (p<0.0001), N2/N3 (p<0.0001), and no adj chemo (p=0.01) had significantly shorter EFS. There was no multi difference between CON-AI and SEQ-AI for LRFS (p=0.50), DDFS (p=0.72), or OS (p=0.85). Conclusions: Patients receiving CON-AI had significantly better EFS than SEQ-AI suggesting timing of administration of AI and RT may affect patient outcomes. Further research is necessary to confirm these findings.
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 | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».