Magnetic Resonance–guided High-intensity Focused Ultrasound (MRgHIFU) Virtual Treatment Planning for Abdominal Neuroblastoma Utilizing Retrospective Diagnostic 3D CT Images
Bibliographic record
Abstract
Magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) is a novel treatment for neuroblastoma using ultrasound-induced thermal ablation with real-time MR thermometry. It is unclear which patients would be amenable to MRgHIFU given the retroperitoneal location of many neuroblastomas within the smaller pediatric abdomen. In addition, planning relies on MR scans, which are not routine in the standard pediatric neuroblastoma workup. This study sought to demonstrate that neuroblastomas are targetable with MRgHIFU and available computed tomographic imaging could be utilized for MRgHIFU virtual treatment. Cross-sectional images of 88 pediatric abdominal neuroblastoma patients were retrospectively processed with custom software to be made compatible with the Sonalleve MRgHIFU platform. Targetability measured percent treatment to lesion volume, within adequate safety margins from critical structures. All images were successfully converted into treatment planning files. Median lesion size was 191±195 cm and depth was 29±17 mm. Up to 78 (85%) patients had targetable lesions with a median targetable volume of 15% and ranging up to 79%. Targetability was highest in superficial, right upper quadrant lesions >200 cm, but limited by proximity to bowel and ribs. This study demonstrates the capacity for MRgHIFU to potentially treat the majority of abdominal neuroblastomas and the feasibility of using computed tomographic images for MRgHIFU virtual treatment planning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".