MRI of Adenomyosis: Where Are We Today?
Bibliographic record
Abstract
Purpose of Review: The purpose of this review is to (i) summarize the current literature regarding the role of magnetic resonance imaging (MRI) in diagnosing adenomyosis, (ii) examine how to integrate MRI phenotypes with clinical symptomatology and histological findings, (iii) review recent advances including proposed MRI classifications, (iv) discuss challenges and pitfalls of diagnosing adenomyosis, and (v) outline the future role of MRI in promoting a better understanding of the pathogenesis, diagnosis, and treatment options for patients with uterine adenomyosis. Recent Findings: Recent advances and the widespread use of MRI have provided new insights into adenomyosis and the range of imaging phenotypes encountered in this disorder. Summary: Direct and indirect MRI features allow for accurate non-invasive diagnosis of adenomyosis. Adenomyosis is a complex and poorly understood disorder with variable MRI phenotypes that may be correlated with different pathogeneses, clinical presentations, and patient outcomes. MRI is useful for the assessment of the extent of findings, to evaluate for concomitant gynecological conditions, and potentially can help with the selection and implementation of therapeutic options. Nevertheless, important gaps in knowledge remain. This is in part due to the lack of standardized criteria for reporting resulting in heterogeneous and conflicting data in the literature. Thus, there is an urgent need for a unified MRI reporting system incorporating standardized terminology for diagnosing adenomyosis and defining the various phenotypes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".