Architectural Patterns of Ovarian/Pelvic High-grade Serous Carcinoma
Bibliographic record
Abstract
We describe the architectural patterns of advanced ovarian/pelvic high-grade serous carcinomas that have been treated with upfront surgery, followed by adjuvant chemotherapy or neoadjuvant chemotherapy, followed by interval debulking to explore the association with the chemotherapeutic response. For 70 cases of advanced (i.e. stage III/IV) high-grade serous carcinomas (33 platinum resistant/intermediate, 37 platinum sensitive; 24 neoadjuvantly treated, 44 primary surgery), all tumor-containing histologic slides were reviewed by 1 of 3 pathologists. Histologic type was confirmed and the following features were assessed: major architectural pattern and the presence of any of 8 predefined minor architectural patterns (papillary, transitional cell carcinoma-like, micropapillary, microcystic, nested papillary, slit-like, glandular, solid). A semiquantitative assessment of psammoma bodies, histiocytic response, necrosis, nuclear atypia, and single-cell invasion was performed. Mitotic count was performed in 10 HPF and 1 HPF was counted for intraepithelial lymphocytes. The morphologic features were tested for an association with previous neoadjuvant chemotherapy and response to chemotherapy (resistant/intermediate versus chemotherapy-sensitive cases stratified by neoadjuvant chemotherapy), which was carried out using χ tests for categorical variables and analysis of variance for continuous data. Combinations of features were analyzed using unsupervised clustering (Wald). Although 8 of 18 features were significantly different when samples from neoadjuvantly treated patients were compared with those not previously treated, no individual histomorphologic feature or a combination of features was associated with response to chemotherapy. Further subtyping of high-grade serous carcinomas will likely need ancillary molecular markers that may have a greater potential to identify cases that will not respond to platinum-based chemotherapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".