Normalized Relative Contrast Improves the Power of Pre-Therapy Contrast-Enhanced MRI to Predict the Prognosis of Uterine Leiomyoma Treated With Uterine Artery Embolization
Bibliographic record
Abstract
Background: Uterine artery embolization (UAE) has emerged as an effective treatment option for women with symptomatic uterine leiomyomas, the most common benign tumor of the female reproductive system. Assessing factors that aid in predicting treatment outcomes is critical for patient selection, procedure planning and post-procedural follow-up. Previous studies have demonstrated variable correlations between MRI predictors and response to UAE. In this study, we investigated if the relative tumor to intratumor myometrium contrast may improve the predictive power of pre-therapy contrast-enhanced MRI. Methods: A retrospective study of a total of 42 uterine leiomyomas treated with UAE was performed. Treated tumors were categorized as either fully or not fully responsive based on if they became completely necrotic 3 - 6 months post-UAE. Results: There was no significant difference (P = 0.34) in the pre-UAE contrast to noise ratio (CNR) between fully responsive (64.6 ± 38.6) and not fully responsive (74.2 ± 24.8) tumors. On the other hand, the pre-UAE relative contrast of not fully responsive tumors was significantly higher than the fully responsive tumors (1.6 ± 0.4 vs. 1.0 ± 0.4, P < 0.05). Pre-UAE tumor relative contrast was found to correctly predict 7/9 not fully responsive and 30/33 fully responsive tumors at a threshold of 1.3. Larger area under the receiver operating characteristic (ROC) curve based on relative contrast than that based on CNR also indicated that relative contrast improved the predictive power of pre-therapy contrast-enhanced MRI. Conclusion: Upon further validation with large studies, pre-UAE relative contrast may prove to be a useful tool to predict UAE treatment outcome of leiomyomas and improve the clinical management of uterine leiomyoma. J Clin Gynecol Obstet. 2015;4(1):164-169 doi: http://dx.doi.org/10.14740/jcgo279w
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".