Focused ultrasound for brain metastases
Bibliographic record
Abstract
Brain metastases (BMs) increasingly represent a significant cause of morbidity and mortality in cancer patients. The efficacy of systemic therapies for BMs, in contrast to extracranial metastases (EMs), remains limited secondary to a host of challenges. These include insufficient drug delivery due to the blood-brain barrier and blood-tumor barrier, the unique immunological milieu in the tumor microenvironment and cerebrospinal fluid, the diversity of immunogenomic landscapes in BMs across genetically distinct malignancies, the branching evolution of BM from EMs, and the challenges in longitudinally obtaining information regarding clinically actionable genetic alterations in BMs for precision oncology. These complex, long-standing challenges require treatment strategies that address multiple problems concurrently, as represented by the potential of focused ultrasound (FUS) for enhancing effectiveness of several existing BM-specific management strategies. Beyond historically investigated applications of FUS for BMs, including thermoablation and histotripsy, new frontiers include enhanced drug delivery of systemic therapies, plasma sono-liquid biopsy of BM-derived factors, radiosensitization, and immunomodulation. These applications, as discussed here, enable multiple combinatorial opportunities of FUS with targeted- and/or immunotherapies for BMs. With multiple ultrasound delivery platforms (including MR-guided, neuro-navigation-guided, and implantable devices) being investigated in neuro-oncology trials worldwide, this review provides strategies for designing and optimizing future research efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".