Abstract OT1-01-01: A randomized, pragmatic trial investigating the timing of radiotherapy and endocrine in patients with early stage breast cancer (REaCT-RETT trial)
Bibliographic record
Abstract
Abstract The optimal timing of commencing adjuvant endocrine therapy (ET) relative to adjuvant radiotherapy (RT) (i.e. concurrent with or sequential to radiotherapy) remains unknown. A systematic review performed by our team was unable to answer this question due to a lack of high quality, randomized data on concurrent versus sequential ET and RT. Surveys of physicians confirmed this uncertainty and highlighted theoretical concerns for increased side effects with concurrent treatment. Respondents showed keen interest in obtaining real world, randomized data to guide clinical practice. REaCT-RETT is a pragmatic, randomized, non-inferiority trial comparing concurrent and sequential ET and RT in early breast cancer (EBC). The primary endpoint will assess the change in ET side effects at baseline and 3 months post radiation, using the Functional Assessment of Cancer Therapy-Endocrine Subscale (FACT-ES), with primary analysis based on an analysis of covariance (ANCOVA). With a sample size of 176 patients (88 per arm), an ANCOVA would have 80% power (α=0.05) to detect effect sizes as small as 0.25 regardless of the correlation with covariates. It is hypothesized that concurrent therapy will be non-inferior to sequential therapy in terms of ET side effects. Secondary endpoints will examine RT toxicity, ET compliance, quality of life, and cost-effectiveness. Patients with HR positive EBC planned to receive both adjuvant ET and RT were eligible. Patients who previously received ET for invasive breast cancer, or RT in the same breast, were excluded. The trial is conducted by The Ottawa Hospital’s (TOH) innovative Rethinking Clinical Trials (REaCT) program (https://react.ohri.ca/) which strives to improve access to patient-centered, pragmatic clinical trials by removing barriers for patients and researchers. Integral features of the program include broad eligibility criteria, a verbal consent model, and pragmatic data collection and assessment procedures. REaCT is the largest pragmatic cancer clinical trials program in Canada, with over 3,200 patients randomized in 18 clinical trials at 15 sites across Canada. REaCT-RETT accrued patients from September 2019 to January 2021. Data collection is ongoing, with final patient follow up expected April 2022. The timing of accrual provided a unique opportunity to adapt in response to restrictions due to the COVID-19 pandemic, which began to impact trial sites in March 2020. The target sample size was met with 262 patients randomized (1:1) across 3 sites in Ontario, 98% from TOH. A mean of 19 patients/month were accrued prior to the pandemic, compared to a mean of 13 patients/month after March 2020. Twenty-two patients were removed due to withdrawal of consent, ineligibility, or physician choice, and the pandemic was not a significant contributing factor. Since March 2020 there have been 772 patient follow ups, of which 47% (364/772) have been virtual. Only 10% (102/1028) of trial mandated appointments have been missed to date. Compliance with baseline and 3-month FACT-ES questionnaires for the primary endpoint in evaluable patients was 90% (215/240) and 83% (198/240), respectively. The pandemic posed several challenges to the REaCT-RETT study including a decline in patient accrual, poor accrual at peripheral sites due to delayed opening, and a rapid switch to virtual patient care. However, the nimble REaCT methodology enabled virtual patient consent and data collection during the pandemic, allowing the trial to continue successfully, with final data expected for presentation summer 2022. Finally, despite the challenges of COVID-19 we have seen that patients and physicians remain interested in research, and we are applying valuable lessons learned to forthcoming REaCT trials to strengthen their performance during and beyond the ongoing pandemic. Citation Format: Sharon F Mc Gee, Mark Clemons, Michelle Liu, Mashari Jemaan Alzahrani, Terry Ng, Arif Awan, Sandeep Sehdev, John Hilton, Jean Michel Caudrelier, Marie France Savard, Lesley Fallowfield, Vikaash Kumar, Orit Freedman, Dean Fergusson, Gregory Pond, Brian Hutton, Jean Marc Bourque. A randomized, pragmatic trial investigating the timing of radiotherapy and endocrine in patients with early stage breast cancer (REaCT-RETT trial) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT1-01-01.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".