New Pattern-Based Classification System for Invasive Endocervical Adenocarcinomas With Emphasis on "Pattern A": A Multi-Institutional Study
Bibliographic record
Abstract
The depth of invasion (DOI) of endocervical adenocarcinoma (ECA) determines stage and ultimately guides treatment. The decision of whether to perform lymphadenectomy at the time of hysterectomy depends on this critical yet difficult-to-measure parameter. Furthermore, more than 95% of resected lymph nodes (LNs) are negative; therefore, patients are exposed to unnecessary risks for complications. We have recently proposed a new classification system for invasive ECA based on pattern rather than DOI. Our method has consistently predicted risk of LN metastasis with 0% positive LNs in cases with pattern A (well-demarcated glands), 8% with pattern B (early stromal invasion arising from well-demarcated glands), and 24% with pattern C (diffuse destructive invasion). The objective of this study was to better characterize clinical and pathologic parameters of pattern A ECA. Hence, cases with this pattern of invasion diagnosed and treated at 12 participating institutions were reviewed, and the following were assessed: age, DOI, stage, tumor differentiation, positive LNs, and recurrences. Data were compared with patients with patterns B and C. Of 360 patients with ECA, 79 (21.9%) ranging from 25 to 64 years (mean, 44.12 years) were included. DOI ranged from 0.1 to 30 mm (mean, 4.74 mm). All cases were stage I. Tumors were well- (56.4%) or moderately differentiated (43.6%). No single cells, desmoplastic reaction, high-grade cytologic features, or lymphovascular invasion were present. Complex intraglandular growth was seen in some instances (ie, cribriform), and relationship to large cervical vessels was irrelevant. All LNs were negative for metastatic disease, and no recurrences or deaths due to disease were identified after a mean follow-up of 48.27 months. Patients with pattern A ECA have an excellent prognosis, and recognition of this pattern can avoid unnecessary lymphadenectomy in almost one fourth of patients. Future projects will concentrate on patterns B and C.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".