Focused ultrasound in pediatric neuro-oncology: Current applications and future directions
Bibliographic record
Abstract
Focused ultrasound (FUS) is a minimally invasive procedure with recent applications to patients with neurosurgical conditions. To date, most neuro-oncologic applications of FUS have occurred in the adult population to target high-grade gliomas and brain metastases. Its potential applications in pediatric neuro-oncology are only just starting to be realized. In children, high-intensity focused ultrasound (HIFU) has been used to treat benign intracranial lesions such as hypothalamic hamartomas and subependymal giant cell astrocytomas. Experience is now accruing with the use of low-intensity focused ultrasound (LIFU) in conjunction with systemically administered microbubbles in children to disrupt the blood-brain barrier (BBB) using magnetic resonance-guided focused ultrasound (MRgFUS). The pediatric brain tumor for which this application has been used is diffuse intrinsic pontine glioma (DIPG), a typically fatal neoplasm in children ages 5-7 years. Here, the history of FUS is reviewed, the principles of FUS therapy are delineated, and a discussion of its applications in neuro-oncology with a focus on pediatric neuro-oncology is provided. Innovations in MRgFUS are ushering a new and exciting era of minimally invasive treatments for children with brain tumors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".