An update on the role of focused ultrasound in neuro-oncology
Bibliographic record
Abstract
PURPOSE OF REVIEW: Brain tumor treatment presents challenges for patients and clinicians, with prognosis for many of the most common brain tumors being poor. Focused ultrasound (FUS) can be deployed in several ways to circumvent these challenges, including the need to penetrate the blood-brain barrier and spare healthy brain tissue. This article reviews current FUS applications within neuro-oncology, emphasizing ongoing or recently completed clinical trials. RECENT FINDINGS: Most clinical interest in FUS for neuro-oncology remains focused on exploring BBB disruption to enhance the delivery of standard-of-care therapeutics. More recently, the application of FUS for radiosensitization, liquid biopsy, and sonodynamic therapy is garnering increased clinical attention to assist in tumor ablation, early detection, and phenotypic diagnosis. Preclinical studies show encouraging data for the immunomodulatory effects of FUS, but these findings have yet to be tested clinically. SUMMARY: FUS is a burgeoning area of neuro-oncology research. Data from several forthcoming large clinical trials should help clarify its role in neuro-oncology care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".