OS1 - 136 Time-Delayed Contrast Enhanced MRI Improves Detection of Brain Metastases: A Prospective Validation of Diagnostic Yield
Bibliographic record
Abstract
The radiological detection of BMs is essential for optimizing a patient’s treatment. This statement is even more valid when stereotactic radiosurgery (SRS), a non-invasive image guided treatment that can target BM as small as 1-2 mm, is delivered as part of that care. The timing of image acquisition after contrast administration can influence the diagnostic sensitivity of contrast enhanced MRI for BM. Objective: Investigate the effect of time delayed acquisition after administration of intravenous Atavist® (Gadobutrol 1mmol/ml) on the detection of BM. Methods: This is a prospective IRB approved study of 50 patients with BM who underwent post-contrast MRI sequences immediately after injection of 0.1 mmol/kg Gadavist® as part of clinical care (t0), followed by axial T1 sequences after a 10 minutes (t1) and 20 minute delay (t2). MRI studies were blindly compared by 3 neuro-radiologists. Results: Single measure intraclass correlation coefficients were very high (0.914, 0.904 and 0.905 for t0, t1 and t2 respectively), corresponding to a reliable inter-observer correlation. The t2 delayed sequences showed a significant and consistently higher diagnostic sensitivity for BM by every participating neuroradiologist as well as for the entire cohort (p=0.016, p=0.035 and 0.034 respectively). A disproportionately high representation of BM detected on the delayed studies was located within posterior circulation territories (compared to predictions based on tissue volume and blood-flow volumes). Conclusion: Considering the safe and potentially high yield nature of delayed MRI sequences, it should supplement the basic MRI sequences in all patients in need of precise delineation of their intracranial disease.
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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 | 0.006 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".