Improved Risk Prediction in Human Papillomavirus–Associated Endocervical Adenocarcinoma Through Assessment of Binary Silva Pattern-based Classification: An International Multicenter Retrospective Observational Study Led by the International Society of Gynecological Pathologists (ISGyP)
Bibliographic record
Abstract
Endocervical adenocarcinomas (EACs) are a group of malignant neoplasms associated with diverse pathogenesis, morphology, and clinical behavior. As a component of the International Society of Gynecological Pathologists International Endocervical Adenocarcinoma Project, a large international retrospective cohort of EACs was generated in an effort to study potential clinicopathological features with prognostic significance that may guide treatment in these patients. In this study, we endeavored to develop a robust human papillomavirus (HPV)-associated EAC prognostic model for surgically treated International Federation of Gynecology and Obstetrics (FIGO) stage IA2 to IB3 adenocarcinomas incorporating patient age, lymphovascular space invasion (LVSI) status, FIGO stage, and pattern of invasion according to the Silva system (traditionally a 3-tier system). Recently, a 2-tier/binary Silva pattern of invasion system has been proposed whereby adenocarcinomas are classified into low-risk (pattern A/pattern B without LVSI) and high-risk (pattern B with LVSI/pattern C) categories. Our cohort comprised 792 patients with HPV-associated EAC. Multivariate analysis showed that a binary Silva pattern of invasion classification was associated with recurrence-free and disease-specific survival (P < 0.05) whereas FIGO 2018 stage I substages were not. Evaluation of the current 3-tiered system showed that disease-specific survival for those patients with pattern B tumors did not significantly differ from that for those patients with pattern C tumors, in contrast to that for those patients with pattern A tumors. These findings underscore the need for prospective studies to further investigate the prognostic significance of stage I HPV-associated EAC substaging and the inclusion of the binary Silva pattern of invasion classification (which includes LVSI status) as a component of treatment recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".