Relationship Between Histologic Features of Primary Breast Carcinomas and Axillary Lymph Node Micrometastases: Detection and Prognostic Significance
Bibliographic record
Abstract
The incidence and prognostic significance of micrometastases (Mic-Met) in axillary lymph nodes (LNs) is still controversial. We compared Mic-Met detection of invasive mammary carcinomas (IMCs) in axillary LNs using second review of hematoxylin and eosin (H&E)-stained slides and immunohistochemistry (IHC) relating them with features of the primary tumor, and determining their influence on overall survival (OS) and disease-free survival (DFS). We studied 188 cases of IMCs with no axillary metastases in the initial reports. The original H&E slides of LN were re-viewed and new sections were submitted for IHC using pancytokeratin (AE1/AE3). All primary breast tumors were re-viewed and classified according to Page et al (1998) and College of American Pathologists criteria (2000). Tumors were graded using the Nottingham grading system. Kaplan-Meier curves were used to evaluate OS and DFS of 147 patients. Mic-Met detection was correlated to histologic features of primary tumor (size, type, grade, lymphatic/blood vessel invasion). Mic-Met were detected in 26/188 cases (by IHC: 23/188, 12.2%; by H&E: 12/188, 6.4%). The re-view of H&E slides showed good specificity (98.2%), but low sensitivity (39.1%), when compared with IHC. There was no relationship between features of primary tumor and Mic-Met detection, including patients with lobular carcinomas or IMCs with lobular features. There was no statistical difference in OS and DFS of patients with and without Mic-Met, but patients with Mic-Met presented lower survival curves. In conclusion, there was no relationship between histologic features of primary tumor and presence of Mic-Met, nor between Mic-Met detection and patients survival.
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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 | 0.000 | 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".