Clinicopathological Characteristics and Prognosis of Triple-Negative Apocrine Carcinoma: A Case-Control Study
Bibliographic record
Abstract
Background: With a prevalence of only 1% among all breast cancers in Japan, apocrine carcinoma (AC) is a rare type of breast cancer, and its clinicopathological characteristics remain unclear. The aim of this study was to evaluate the characteristics and prognosis of AC, in relation to the presence or absence of androgen receptor (AR). Methods: We conducted a retrospective multi-center case-control study (Yokohama Clinical Oncology Group (YCOG): YCOG1701 study) in Japan. A total of 53 patients were registered who were diagnosed with AC between 2000 and 2017 in YCOG-affiliated hospitals. Results: The median age of the patients was 67 (43 - 94) years, and the median observation time was 6.1 years. Among the 53 cases, 24 had triple-negative pure AC (TN-PAC; AR-positive), whereas 29 had other types of AC (other-AC; estrogen receptor-positive and/or human epidermal growth factor receptor 2-positive or AR-negative). Tumor size was smaller (1.4 vs. 2.1 cm, P = 0.024) and metastasis occurred in fewer nodes (12.5% vs. 37.9%, P = 0.036) in the TN-PAC group than in the other-AC group. The number of patients who were administered perioperative adjuvant chemotherapy did not significantly differ between the two groups (TN-PAC/other-AC = 50.0%/55.2%, P = 0.525); however, there was no recurrence in the TN-PAC group, compared to five cases with relapse in the other-AC group. Conclusions: AR-positive AC patients showed a favorable prognosis without adjuvant chemotherapy, even with the TN subtype. A clinical trial exploring the possibility of treatment de-escalation is anticipated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".