Intraoperative ultrasound localization microscopy of human brain tumors and arteriovenous malformations
Bibliographic record
Abstract
Abstract The microvasculature and hemodynamics of human brain tumors and other lesions have remained largely unexplored in vivo due to the limited resolution of conventional imaging techniques, and may present new opportunities in biomarker identification and neurosurgeries. Ultrasound Localization Microscopy (ULM), which tracks freely circulating intravascular microbubbles used as contrast agents, overcomes these limitations and has been used to visualize vascular structures and flow. In this study, we performed intraoperative ULM during brain surgeries involving resection of brain tumors (N = 3 meningiomas, N = 3 brain metastasis, N = 3 high grade gliomas) and an arteriovenous malformation (AVM) (N = 1). ULM provided microvascular images of the human brain, resolving vessels down to 35 μm, revealing clear differences in structure and dynamics between tumor and surrounding healthy tissue. By following individual microbubbles, vessel connectivity was probed and used to identify feeding, draining, and non-tumoral vessels. In one case, a 3D ULM map of an AVM was generated with 226 μm resolution, allowing us to resolve its complex internal structure, including feeding and draining vessels. Intraoperative ULM enables visualization of human brain vasculature and hemodynamics at unprecedented resolutions, and may directly aid tumor and AVM resections by providing flow paths and speeds through compact niduses. One Sentence Summary Ultrasound localization microscopy during human brain surgery revealed vascular structure and dynamics of brain lesions at sub-millimeter resolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".