Improved risk prediction in HPV-associated endocervical adenocarcinoma through assessment of binary silva pattern-based classification: international multicenter retrospective study of the international society of gynecological pathologists
Bibliographic record
Abstract
Introduction Endocervical adenocarcinomas (EACs) are neoplasms associated with diverse pathogenesis, morphology, and clinical behavior. The Silva pattern-based classification categorizes HPV-associated EACs based on the morphology of the invasion and predicts lymph node metastasis and recurrence. Traditionally the Silva classification was a three-tier system (pattern A, B, and C). A two-tier/binary system has recently been proposed whereby tumors are classified into low risk (pattern A/pattern B without lymphovascular invasion (LVSI)) and high risk (pattern B with LVSI/pattern C). Our aim was to develop a prognostic model for surgically treated FIGO stage IA2-IB3 EACs that incorporates patient age, LVSI, FIGO stage and three- and two-tier Silva systems. Methods The International Society of Gynecological Pathologists (ISGyP) established a multicenter consortium to pool de-identified individual patient data for patients with HPV-associated EACs. All participating pathologists completed mandatory online training. Results Our cohort comprised 792 HPV-associated EACs (table 1). On multivariate analysis a binary Silva system was associated with recurrence-free and disease specific survival (p<0.05) while FIGO 2018 stage I substages were not. In the current three-tiered system, disease specific survival for patients with pattern B tumors did not significantly differ from those with pattern C tumors while those with pattern A tumors did (table 2). Conclusion/Implications These findings highlight the need for future 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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".