Bridging the macro to micro resolution gap with angiographic optical coherence tomography and dynamic contrast enhanced MRI
Bibliographic record
Abstract
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is emerging as a valuable tool for non-invasive volumetric monitoring of the tumor vascular status and its therapeutic response. However, clinical utility of DCE-MRI is challenged by uncertainty in its ability to quantify the tumor microvasculature ([Formula: see text] scale) given its relatively poor spatial resolution (mm scale at best). To address this challenge, we directly compared DCE-MRI parameter maps with co-registered micron-scale-resolution speckle variance optical coherence tomography (svOCT) microvascular images in a window chamber tumor mouse model. Both semi and fully quantitative (Toft's model) DCE-MRI metrics were tested for correlation with microvascular svOCT biomarkers. svOCT's derived vascular volume fraction (VVF) and the mean distance to nearest vessel ([Formula: see text]) metrics were correlated with DCE-MRI vascular biomarkers such as time to peak contrast enhancement ([Formula: see text] and [Formula: see text] respectively, [Formula: see text] for both), the area under the gadolinium-time concentration curve ([Formula: see text] and [Formula: see text] respectively, [Formula: see text] for both) and [Formula: see text] ([Formula: see text] and [Formula: see text] respectively, [Formula: see text] for both). Several other correlated micro-macro vascular metric pairs were also noted. The microvascular insights afforded by svOCT may help improve the clinical utility of DCE-MRI for tissue functional status assessment and therapeutic response monitoring applications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".