Opening the Blood-Brain Barrier with MR Imaging–guided Focused Ultrasound: Preclinical Testing on a Trans–Human Skull Porcine Model
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Bibliographic record
Abstract
Purpose To develop and test a protocol in preparation for a clinical trial on opening the blood-brain barrier (BBB) with magnetic resonance (MR) imaging–guided focused ultrasound for the delivery of chemotherapy drugs to brain tumors. Materials and Methods The procedures were approved by the institutional animal care committee. A trans–human skull porcine model was designed for the preclinical testing. Wide craniotomies were applied in 11 pigs (weight, approximately 15 kg). A partial human skull was positioned over the animal’s brain. A modified clinical MR imaging–guided focused ultrasound brain system was used with a 3.0-T MR unit. The ultrasound beam was steered during sonications over a 3 × 3 grid at 3-mm spacing. Acoustic power levels of 3–20 W were tested. Bolus injections of microbubbles at 4 μL/kg were tested for each sonication. Levels of BBB opening, hemorrhage, and cavitation signal were measured with MR imaging, histologic examination, and cavitation receivers, respectively. A cavitation safety algorithm was developed on the basis of logistic regression of the measurements and tested to minimize the risk of hemorrhage. Results BBB openings of approximately 1 cm3 in volume were visualized with gadolinium-enhanced MR imaging after sonication at an acoustic power of approximately 5 W. Gross examination of histologic specimens helped confirm Evans blue (bound to macromolecule albumin) extravasation, and hematoxylin-eosin staining helped detect only scattered extravasation of red blood cells. In cases where cavitation signals were higher than thresholds, sonications were terminated immediately without causing hemorrhage. Conclusion With a trans–human skull porcine model, this study demonstrated BBB opening with a 230-kHz system in preparation for a clinical trial. © RSNA, 2016 Online supplemental material is available for this article.
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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 | 0.000 | 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) | 0.000 | 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 it