Evaluation of recurrence rate in Canadian patients with stage II/III HR+/HER2- early breast cancer in the real-world setting.
Bibliographic record
Abstract
e23303 Background: Breast cancer is the most prevalent cancer and second leading cause of cancer-related mortality among Canadian women. Most of cases belong to the HR+/HER2- subtype, representing approximately two-thirds of all instances. Treatment for this subtype encompasses a multifaceted approach with a curative intent. This real-world evidence study aims to comprehensively analyze the clinical outcomes of Canadian patients diagnosed with early-stage HR+/HER2- breast cancer, focusing on recurrence rates after initiation of adjuvant endocrine therapy to identify opportunities for enhancing patient care. Methods: This is a retrospective, longitudinal cohort study involving 541 patients enrolled in the pan-Canadian cancer patient registry PMT (Personalize My Treatment), with newly diagnosed or recurrent stage II or III HR+/HER2- breast cancer between January 1 st , 1996, and May 31st, 2022. We evaluated disease recurrence rates, time to recurrence, and overall survival (OS). Furthermore, the study explored duration of endocrine therapy (ET) in the adjuvant setting. Results: 409 patients received adjuvant ET representing 75.6% of the total cohort, emphasizing the role of ET as standard of care for adjuvant treatment in this patient population. The median duration of adjuvant ET was 4.5 years. In the overall adjuvant patient population, recurrence rates progressively increased over time from 13.2% after 2 years, 21.4% after 3 years, 30.3% after 5 years, and peaking at 58.4% after 10 years. Median time to recurrence for the overall patient population on ET was 7.76 years. OS rate for patients on ET was 94.6% at 5 years and 78.3% at 10 years. Conclusions: This study marks a pioneering real-world analysis utilizing pan-Canadian EHR data, demonstrating increasing recurrence rates over time in HR+/HER2- early breast cancer. It highlights the high unmet need in stage II and stage III breast cancer, with 1 patient out of 3 recurring after 5 years, and more than half recurring after 10 years despites adjuvant treatment with ET alone. This indicates the need for more efficacious and tolerable treatment options to reduce short- and long-term recurrence and to prolong survival, not only in the higher risk patient population, but in the overall HR+/HER2- breast cancer patients at increased risk of recurrence.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".