Revisiting classic MRI findings of venous malformations: Changes in protocols may lead to potential misdiagnosis
Bibliographic record
Abstract
Introduction Magnetic resonance imaging (MRI) is most sensitive and specific for characterizing venous malformations (VMs). VMs typically demonstrate central enhancement on delayed-contrast imaging. Fluid-fluid levels (FFLs) are uncommon in VMs and common in lymphatic malformations (LMs). Technology has advanced since the initial description of these findings. Rates of detection of these MRI findings in VMs may have changed as MRI technology and techniques have evolved. Methods and methods A prospectively maintained database from a multidisciplinary vascular anomalies clinic was reviewed to identify patients with final diagnosis of VM or LM. Patients with reviewable contrast-enhanced MRIs were selected, reviewing the oldest MRI studies in the database against the newest MRI studies to identify equal numbers of patients from the temporal extremes. Imaging was reviewed to assess for presence of FFLs. Enhancement was quantified by measuring signal in the same location of the lesion both on pre- and postcontrast sequences Results Forty patients were identified for analysis. Twenty studies with sufficient archived imaging for review were performed between 1995 and 2006; 20 such studies were performed between 2011 and 2012. The new imaging cohort had higher rates of FFL visualization ( p = 0.001). Correlation was found between time to imaging following contrast and degree of enhancement ( p < 0.001). Inverse correlation was found between scan date and time to contrast ( p = 0.001) and scan date and enhancement ( p = 0.021). Conclusion FFLs should no longer be considered exclusionary for the diagnosis of VMs. Timing following contrast administration should be maximized to increase degree of enhancement to confirm the diagnosis of VMs.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".