Past, present and future of Focused Ultrasound as an adjunct or complement to DIPG/DMG therapy: A consensus of the 2021 FUSF DIPG meeting
Bibliographic record
Abstract
Diffuse Intrinsic Pontine Glioma (DIPG), now known as Diffuse Midline Glioma (DMG) is a devastating pediatric brain tumor with limited treatment options and a very poor prognosis. Despite more than 250 clinical trials aimed to treat children diagnosed with DMG, no curative therapies currently exist for this patient population. A major obstacle has been the intact blood brain barrier (BBB) which prevents most therapeutics from crossing into the tumor bed. Focused Ultrasound (FUS) is an emerging, noninvasive medical technology which has been shown in both preclinical and clinical research to disrupt the blood brain barrier safely and temporarily. FUS blood brain barrier opening has been studied in combination with chemotherapies in preclinical DMG models, and this technology is now being investigated in clinical trials for the treatment of pediatric brain tumors. Focused ultrasound has additional mechanisms of action, including sonodynamic therapy and radiation sensitization, that hold promise as future DMG therapies as well. This paper, largely based off the proceedings from a workshop held by the Focused Ultrasound Foundation in October of 2021, summarizes the current state of the field of focused ultrasound for DIPG/DMG, including preclinical, technical, and clinical summaries in addition to recommended next steps for continued advancement of the game changing technology of Focused Ultrasound.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| 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".