The Revised FIGO Staging System for Uterine Malignancies: Implications for MR Imaging
Bibliographic record
Abstract
Cancers of the uterine corpus and cervix are the most common gynecologic malignancies worldwide. The International Federation of Gynecology and Obstetrics (FIGO) staging system was first established in 1958, when it was recognized that the recurrence rate and patient outcomes were directly related to the degree of tumor spread at the patient's initial presentation. Changes in understanding of tumor biology led to a recent update in the FIGO staging system that reflects the variation in treatment strategies between endometrial and cervical cancer. Patients with endometrial cancer are primarily treated with hysterectomy; thus, staging is done at surgery and histologic analysis. Magnetic resonance (MR) imaging may accurately depict the extent of endometrial cancer at diagnosis and, in conjunction with the tumor grade and histologic subtype, help stratify risk, which determines the therapeutic course. Cervical carcinoma is staged at clinical examination because many tumors are inoperable at the time of patient presentation. Preoperative MR imaging criteria are not formally included in the revised FIGO staging system because cervical carcinoma is most prevalent in developing countries, where imaging resources are limited. However, MR imaging is highly sensitive and specific for depicting important prognostic factors and, when available, is recommended as an adjunct to clinical examination. The MR imaging findings of uterine carcinoma should be discussed in a multidisciplinary setting in conjunction with clinical and histologic findings, an approach that provides accurate staging and risk stratification and allows for individualized treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".