Systemic Therapy in Local Recurrence of Breast Cancer, Report of a case and Decision Making in MDT Meeting
Bibliographic record
Abstract
Background: Locoregional recurrence of breast cancer has significantly decreased over the last decades, particularly due to effective systemic therapy. While there is little controversy regarding local management of locoregional recurrences, in light of previous systemic treatment, additional chemotherapy regimens and their benefit to the patient are still subject to debate in tumors boards.Case Presentation: A 45-year-old woman was referred to our tertiary care center with a local recurrence of breast cancer 9 years after modified radical mastectomy for a ypT2N2a invasive ductal carcinoma. She received neoadjuvant treatment consisting of FEC-D (5-FU-epirubicin-cyclophosphamide, followed by docetaxel) for hormone receptor positive, HER-2-neu negative cancer in 2009, as well as adjuvant radiotherapy and tamoxifen for 9 years. After R0 resection of the hormone receptor positive, HER-2-neu negative recurrence in 2019, adjuvant therapy with ovarian suppression and an aromatase inhibitor was undertaken. A multigene assay identified a recurrence score at 37 and benefit from chemotherapy > 15%.Question: What would the ideal chemotherapy regimen consist of for this patient with an R0 resection of late recurrence of breast cancer?Conclusion: After reviewing history, imaging and pathology, members of the multidisciplinary team recommended treatment with Taxotere and cyclophosphamide (TC) x 4 for our patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".