Molecular Prognostic Factors in Uterine Serous Carcinomas: A Systematic Review
Bibliographic record
Abstract
Uterine serous carcinomas are an aggressive minority of endometrial cancers. They are characterized by mutations in TP53 and extensive copy number alterations and are primarily classified in the copy number-high/p53abn molecular prognostic group, highlighting a unique molecular profile that is crucial for understanding their behavior and treatment responses. Clinical studies have shown that molecular categorization via biomarkers can facilitate proper treatment selection, and this is now widely used. In this context, the scope of this systematic review is to identify molecular characteristics with prognostic significance for these neoplasms to further inform on their treatment needs. We performed a comprehensive literature search of all articles written in English using the PubMed/Medline and Cochrane databases through February 2025. Our review led to the inclusion of 95 studies, from which we identified a total of 66 distinct molecular characteristics along with new cancer signatures that may impact prognosis. These findings have the potential to inform clinical practice by aiding in the development of tailored treatment strategies for patients with uterine serous carcinoma, ultimately improving outcomes in this challenging malignancy.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".