Duration of Trastuzumab in Patients with HER2-Positive Metastatic Breast Cancer in Prolonged Remission
Bibliographic record
Abstract
BACKGROUND: Outcomes in metastatic breast cancer (mbc) positive for her2 (human epidermal growth factor receptor 2) are generally unfavourable. Trastuzumab has revolutionized the prognosis of her2-positive mbc. Some her2-positive mbc patients go into prolonged remission, and a few patients remain in remission even after discontinuation of trastuzumab, suggesting the possibility of a cure. In our practice, 4 her2-positive mbc patients treated with chemotherapy and trastuzumab have remained in remission on maintenance therapy for 5 years or more. Of those 4 patients, 2 have continued in remission after discontinuation of trastuzumab for more than 1 year. The objective of the present paper was therefore to address the duration of trastuzumab therapy in her2-positive mbc patients in prolonged remission. METHODS: We conducted a literature review of the duration of trastuzumab in her2-positive mbc patients in remission. We also conducted an online survey of oncologists in Ontario to determine their treatment practices in her2-positive mbc patients. RESULTS: The literature search found no specific evidence about the optimal duration of trastuzumab maintenance therapy in her2-positive mbc in prolonged remission. However, retrospective studies suggest predictive markers of good prognosis in patients in complete remission taking maintenance trastuzumab. Identifying those markers could lead to more personalized treatment. Our survey of oncologists about their treatment practices in her2-positive mbc patients revealed that 82.93% of respondents (n = 34) follow the currently available guidelines. CONCLUSIONS: With the emergence of patients in prolonged remission, duration of trastuzumab in her2-positive mbc has become an important and relevant clinical question worldwide. Collaborative efforts are needed for the further study of this topic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".