Abstract P3-12-05: Fractionation patterns in adjuvant breast radiotherapy in Ontario, Canada from 2009 to 2015
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".