Focused Ultrasound in Brain Tumors: Mechanisms, Imaging Guidance, and Emerging Clinical Applications
Bibliographic record
Abstract
ABSTRACT Focused ultrasound (FUS) is an emerging therapeutic and diagnostic technology in neuro-oncology, offering new strategies for molecular diagnosis, drug delivery, and tumor ablation across a range of brain tumors, including glioblastoma (GBM), brain metastases, and diffuse intrinsic pontine glioma (DIPG). The prognosis for aggressive brain tumors remains poor, despite advances in surgery, radiation, and chemotherapy. A considerable challenge is the limited ability to deliver therapeutics across the blood-brain barrier (BBB), particularly to infiltrative or non-enhancing tumor regions. FUS introduces an incisionless approach to the molecular subtyping of brain tumors, enhancing therapeutic delivery, and offers novel therapeutic approaches such as sonodynamic therapy (SDT). This review summarizes the FUS mechanisms and highlights the critical role of imaging modalities confirming target engagement, assessing bioeffects and outcomes, and ensuring safety. We also explore future directions, including the integration of liquid biopsy, artificial intelligence, and outpatient-ready FUS platforms, which will position FUS as a promising adjunct to standard neuro-oncologic care. ABBREVIATIONS: GBM = glioblastoma; FUS = focused ultrasound; HIFU = high-intensity focused ultrasound; LIFU = low-intensity focused ultrasound; MRgFUS = magnetic resonance guided focused ultrasound; BBBO = blood brain barrier opening; SDT = sonodynamic therapy; 5-ALA = 5-aminolevulinic acid
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".