Nomogram to Predict Risk of Lymph Node Metastases in Patients With Endometrioid Endometrial Cancer
Bibliographic record
Abstract
Pelvic lymphadenectomy in early-stage endometrial cancer is controversial, but the findings influence prognosis and treatment decisions. Noninvasive tools to identify women at high risk of lymph node metastasis can assist in determining the need for lymph node dissection and adjuvant treatment for patients who do not have a lymph node dissection performed initially. A retrospective review of surgical pathology was conducted for endometrioid endometrial adenocarcinoma at our institution. Univariate and multivariate logistic regression analysis of selected pathologic features were performed. A nomogram to predict for lymph node metastasis was constructed. From August 1996 to October 2013, 296 patients underwent total abdominal or laparoscopic hysterectomy, bilateral salpingo-oophorectomy, and selective lymphadenectomy for endometrioid endometrial adenocarcinoma. Median age at surgery was 62.7 yr (range, 24.9-93.6 yr). Median number of lymph nodes removed was 13 (range, 1-72). Of all patients, 38 (12.8%) had lymph node metastases. On univariate analysis, tumor size ≥4 cm, grade, lymphovascular space involvement, cervical stromal involvement, adnexal or serosal or parametrial involvement, positive pelvic washings, and deep (more than one half) myometrial invasion were all significantly associated with lymph node involvement. In a multivariate model, lymphovascular space involvement, deep myometrial invasion, and cervical stromal involvement remained significant predictors of nodal involvement, whereas tumor size of ≥4 cm was borderline significant. A lymph node predictive nomogram was constructed using these factors. Our nomogram can help estimate risk of nodal disease and aid in directing the need for additional surgery or adjuvant therapy in patients without lymph node surgery. Lymphovascular space involvement is the most important predictor for lymph node metastases, regardless of grade, and should be consistently assessed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".