Feasibility of targeting canine soft tissue sarcoma with MR-guided high-intensity focused ultrasound
Bibliographic record
Abstract
PURPOSE: Magnetic resonance imaging-guided high-intensity-focused ultrasound (MR-HIFU) is a non-invasive treatment modality that precisely focuses ultrasound energy within a tumour and can be customised to result in a wide range of local bioeffects. The purpose of this study was to determine the feasibility of using MR-HIFU to treat soft tissue sarcoma (STS) in dogs. MATERIALS AND METHODS: Medical records of dogs admitted to the Virginia-Maryland College of Veterinary Medicine from 1 January 2012 to 31 December 2016 were searched for a diagnosis of sarcoma with available cross-sectional imaging of the tumour (MRI or CT). Fifty-three (53) dogs were eligible for inclusion. Tumor tissue (in bone as well as in soft tissue) was considered targetable unless: (1) the ultrasound path was completely obstructed by bone or gas and (2) the MR-HIFU target was within the spinal cord or less than 1 cm from the margin of the spinal cord. Tumors were categorised as <50% targetable, ≥50% targetable or non-targetable. RESULTS: Eighty-one percent of STS (81.1%, 43/53) were targetable. The head/spine tumour sites had the highest proportion of non-targetable tumours (36%, 9/25). The majority of truncal and axillary tumours were ≥50% targetable (88.9%, 16/18) ,and all extremity tumours were considered ≥50% targetable (100%, 5/5). CONCLUSIONS: The majority of STS were targetable. This is the first study to evaluate MR-HIFU targetability of canine STS. HIFU has potential as a therapeutic modality for treating STS in dogs, and this veterinary application is a possible model for treatment of naturally occurring STS in humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".