AB031. The histopathological and molecular features of breast carcinoma with high-grade tumour budding
Bibliographic record
Abstract
Background: Tumour budding (TB) is an adverse histological feature in many cancers. It is thought to represent epithelial-to-mesenchymal transition, a key step in the metastatic process. The role of TB in breast carcinoma (BC) remains unclear. Methods: A systematic search was performed to identify studies that compared features of BC based on the presence or absence of high-grade TB. Dichotomous variables were pooled as odds ratios (OR) using the Cochran-Mantel-Haenszel method. Quality assessment of the included studies was performed using the Newcastle-Ottawa scale (NOS). Results: Seven studies with a total of 1040 patients (high grade TB n= 519, 49.9%; low grade TB n=521, 50.1%) were included. A moderate- to high-risk of bias was noted. The median NOS was 7 (range, 6–8). High-grade TB was significantly associated with lymph node involvement (OR 2.28, 95% CI: 1.74 to 2.98, P<0.001) and lymphovascular invasion (OR 3.08, 95% CI: 2.13 to 4.47, P<0.001). With regards to molecular subtypes, there was an increased likelihood of high-grade TB in oestrogen- (OR 1.66, 95% CI: 1.21 to 2.29, P=0.002) and progesterone-receptor positive (OR 1.68, 95% CI: 1.10 to 2.59, P=0.02) tumours. In contrast triple negative breast cancer had a reduced incidence of high-grade TB (OR 0.46, 95% CI: 0.30 to 0.72, P=0.0006). Conclusions: High-grade TB is enriched in hormone-positive BC and is associated with known adverse prognostic variables. TB may offer new insights into the metastatic processes of luminal BC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".