Analysis of Risk Factors for Lymphatic Metastasis in Endometrial Carcinoma and Utility of Three-Dimensional Magnetic Resonance Imaging in Gynecology
Bibliographic record
Abstract
Background: The aim of our study was to evaluate the utility of three-dimensional magnetic resonance imaging (3D-MRI) in gynecologic fields. We examined the relation between tumor volume measured with 3D-MRI and lymph node metastasis in patients with endometrial carcinoma. Methods: A retrospective analysis of 84 patients with endometrial carcinoma who underwent hysterectomy, bilateral salpingo-oophorectomy with pelvic/para-aortic lymphadenectomy at our institute was performed. Of these, the tumor volume of 59 patients could be calculated using 3D-MRI. Age, serum CA125 level, histologic type and grade, volume of tumors were examined in relation to pelvic/para-aortic lymph node metastasis as preoperative risk factors. Tumor volume measurements were calculated using 3D-MRI with AqariusNET Server 4G software. Univariate and multivariate associations between the preoperative risk factors and pelvic/para-aortic lymph node metastasis were analyzed. Receiver operating characteristic (ROC) curves were used to determine the best cut-off points for CA125 levels and tumor volume to predict lymph metastasis. Results: The mean age, CA125 value and tumor volume were 61.6 years, 51.6 (IU/L) and 11.6 (cm 3 ), respectively. Lymphatic metastasis occurred in 16.0% (10 of 59) patients. Univariate analysis indicated that a high CA125 level and a tumor volume were risk factors (P = 0.0111, 0.0123 respectively). Multivariate analysis revealed that tumor volume was an independent risk factor for lymphatic metastasis (hazard ratio (HR) 12.7, 95% CI 1.06 - 154). The potential cut-off values of CA-125 level and tumor volume were 29 IU/L (sensitivity: 0.744; specificity: 0.821) and 12.79 cm 3 (sensitivity: 0.821; specificity: 0.744), respectively. Conclusions: Our results suggest that tumor volume calculated with 3D-MRI correlates with lymph node metastasis in endometrial carcinoma. World J Oncol. 2018;9(3):74-79 doi: https://doi.org/10.14740/wjon1106w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".