Abstract P3-12-05: Fractionation patterns in adjuvant breast radiotherapy in Ontario, Canada from 2009 to 2015
Notice bibliographique
Résumé
Abstract Purpose: To report the patterns of use of hypofractionated radiotherapy (HFRT) (≤16 fractions)in breast cancer and ductal carcinoma in situ (DCIS) patients in Ontario, Canada from 2009 to 2015 and identify factors related to HFRT use. Methods: A retrospective cohort study of Ontario women diagnosed with breast cancer or DCIS followed by adjuvant breast or chest wall radiation (RT) from 2009 to 2015 was conducted using data from the Institute for Clinical Evaluative Sciences (ICES). Logistic regression models were used to identify factors associated with HFRT use. Physician was included as a random effect. To calculate the potential amount of time that could be saved if all patients were to receive 16 fractions of HFRT, the number of extra RT treatments after the 16th visit was multiplied by the median amount of time spent to treat one patient at our cancer centre (8.76 minutes). This time was found with Sunnybrook Health Sciences Centre data collected from October 2017 to March 2018 (n=523) and represents the amount of time from when a patient enters the RT unit for treatment setup, until the RT beam is turned off. Results: A total of 42,072 patients were included. Most patients were aged between 50 and 69 years old (56.7%) with stage I or II breast cancer (74.6%) and had breast conserving surgery (BCS) (76.9%). Half previously received chemotherapy and 7.9% were DCIS patients. Use of sequential boost, simultaneous boost, and regional nodal RT was 17.2%, 3.1%, and 31.7% respectively. Institutional variation in HFRT use ranged from 27.5% to 71.6%. HFRT use has increased in all patient populations over the study period. HFRT use was more common in breast cancer and DCIS patients with BCS than in chest wall and nodal RT. Simultaneous boost (OR=0.09), nodal RT (OR=0.08), previous chemotherapy (OR=0.7), stage 0, II, and III breast cancer (OR=0.06 relative to stage I), were correlated with less HFRT use. Older age, later year of diagnosis, sequential boost (OR=0.09), BCS and no surgery (OR=1.2, 1.5 relative to mastectomy) were correlated with higher HFRT use. Institution was significantly correlated to HFRT use. The variance estimate for the physician random effect was 0.33 (p<0.0001). For breast cancer patients with BCS and breast RT specifically, 62,396 extra visits occurred from 2009 to 2015, corresponding to ˜9100 hours of treatment if all patients received HFRT. For the entire patient population, a total of 190,726 extra visits occurred or ˜27,900 hours. Trends in the use of HFRT (%) from 2009 to 2015 for DCIS and breast cancer patients in Ontario.YearStage I-IV + BCS + Breast RTStage I-IV + BCS + Breast + Nodal RTStage I-IV + Mastectomy + Breast RTStage I-IV + Mastectomy + Breast + Nodal RTDCIS + BCS200962.610.329.09.545.2201062.06.323.76.951.2201162.24.121.34.752.7201263.79.621.711.452.0201365.112.629.315.057.3201469.37.835.415.164.5201573.711.839.217.573.4BCS: breast conserving surgery, RT: radiation treatment Conclusions: HFRT use in Ontario has increased over time for all patient populations, and reflects the current evidence supporting HFRT in different patient populations, with lower HFRT use seen in chest wall and nodal RT and higher HFRT use in early-stage breast cancer patients with BCS. Citation Format: Chow E, Chan S, Yao Z, Sutradhar R, Lee J, Wan BA, Paszat L. Fractionation patterns in adjuvant breast radiotherapy in Ontario, Canada from 2009 to 2015 [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-12-05.
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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 | 0,002 |
| 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 | 0,002 | 0,001 |
| 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 ».