Now or Later? The Role of Neoadjuvant Treatment in Advanced Endometrial Cancer: A Systematic Review
Bibliographic record
Abstract
Background: Endometrial cancer (EC) is, nowadays, the most frequent gynecological malignancy worldwide. The main treatment approach for EC is surgery, especially for early-stage tumors. For advanced EC, chemotherapy (CT) with carboplatin and paclitaxel is the standard treatment, especially for women with metastatic or recurrent disease. The present systematic review aimed to establish whether neoadjuvant treatment regimens with CT and/or radiotherapy (RT) lead to better survival outcomes compared to upfront surgery in advanced EC. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, through the string “((“Endometrial Neoplasms”[Mesh]) AND “Hysterectomy”[Mesh]) AND “Radiotherapy”[Mesh] AND Chemotherapy”, the selection of articles was made. A quality assessment was conducted using the Newcastle–Ottawa Scale (NOS). The studies included patients with EC with survival and recurrence outcomes—patients treated with upfront surgery or neoadjuvant CT ± External Beam Radiation Therapy (EBRT) or CT ± Brachytherapy (BT). Results: According to the selected evidence in the scientific literature, the 5-year DFS was 21.3% for upfront surgery and ranged from 42 to 73% for neoadjuvant chemotherapy. Also, the 5-year OS was 6.2 to 49.7% with upfront surgery and 15.5 to 100% for neoadjuvant schemes. None of the studies dedicated to surgery reported the 5-year Recurrence Rate (RR), while in the neoadjuvant treatments, it ranged from 27 to 64.7%. Conclusions: The literature’s paucity of data makes it difficult to compare neoadjuvant therapy regimens with upfront surgery in advanced endometrial carcinoma. Nevertheless, the current data show more encouraging results for the neoadjuvant treatment group.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".