Comparison of triple negative (TN) and HER2 positive (HER2+) invasive lobular carcinomas (ILCs) to a matched cohort of invasive ductal carcinomas.
Bibliographic record
Abstract
e13088 Background: Most ILCs are estrogen receptor (ER) positive and tend to relapse at different sites than IDCs, suggesting distinct biology. We explored prognosis for TN and HER2+ ILCs and IDCs. Methods: From the prospective British Columbia (BC) Cancer Outcomes Unit database, we identified TN and HER2+ ILCs diagnosed 01/2003 to 12/2013 referred to BC Cancer. Exclusions: stage IV, mixed ILC/IDC, unknown HER2, and prior / subsequent breast cancer. ILCs were propensity-score matched (1:2) to IDCs for age, T/N stage, biomarkers, grade, chemotherapy (CT), and nodal radiation (RT). Kaplan-Meier method was used to estimate relapse-free survival (RFS) and overall survival (OS). Results: Of 2925 ILCs diagnosed, 2587 were referred, and 143 were TN or HER2+. By comparison, there were 5526 TN and HER2+ IDCs. After exclusions, 127 TN and HER2+ cases (23% TN; 77% HER2+) were matched as shown in Table 1. Five-year RFS was ILCs 81% (95% confidence interval [CI], 73-87%) and IDCs 84% (95% CI, 79-88%). Five-year OS was ILCs 79% (95% CI, 71-85%) and IDCs 83% (95% CI, 78-87%). Conclusions: Fewer than 5% of ILCs are TN or HER2+. Low CT use may explain the similar low RFS in both ILCs and IDCs. For the ILC subgroup, therapy should mirror standard of care, which includes CT (plus anti-HER2 therapy for HER2+) for most T/N stages. Table 1: ILCs and matched IDCs ILC (n = 127) IDC (n = 254) Stage T1 51 (40.2%) 101 (39.8%) T2 51 (40.2%) 104 (40.9%) T3 15 (11.8%) 27 (10.6%) N0 60 (47.2%) 115 (45.3%) N1 31 (24.4%) 67 (26.4%) N2 13 (10.2%) 17 (6.7%) N3 10 (7.9%) 24 (9.5%) Grade 1 12 (9.5%) 22 (8.7%) 2 72 (56.7%) 143 (56.3%) 3 41 (32.3%) 88 (34.7%) CT1 87 (68.5%) 162 (63.8%) Hormone therapy2 77 (60.6%) 168 (66.1%) Nodal RT 53 (41.7%) 99 (39.0%) ER positive 93 (73.2%) 186 (73.2%) PR positive 55 (43.3%) 106 (41.7%) HER2 positive 98 (77.2%) 193 (76.0%) 1 p-value = 0.36 2not matched, p-value = 0.29
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".