Magnetic resonance-guided high-intensity focused ultrasound combined with radiotherapy for palliation of head and neck cancer—a pilot study
Bibliographic record
Abstract
BACKGROUND: Radiotherapy is a critical component of the multidisciplinary management of cancers of the head and neck. It may comprise the primary curative treatment modality or is used in an adjuvant setting to improve local control and survival by preventing seeding and reseeding of distant metastases from persistent reservoirs of locoregional disease. Although considerable advances have been made recently in the fields of radiotherapy, systemic treatment and surgery for head and neck tumours, locoregional recurrence rates remain high and treatment side effects may have severe impact on patients' quality of life. Magnetic resonance-guided high-intensity focused ultrasound (MRg-HIFU) is a novel technique in the treatment of cancer that has the potential to improve tumour cure rates and decrease treatment-related toxicity. Clinical applications of HIFU are being used increasingly for the treatment of several tumour sites, for example uterine leiomyomas and prostate cancer. METHODS/DESIGN: The pilot study presented here is an initial step toward utilizing MRg-HIFU for head and neck cancer treatment. The rationale for novel treatment options in head and neck cancer is reviewed as well as emerging evidence that support the increasing clinical utilization of MRg-HIFU. DISCUSSION: This pilot study aims to assess safety, toxicity and feasibility of MRg-HIFU treatments to the head and neck region and to evaluate changes caused by MRg-HIFU within the treated tumour regions based on post-treatment MRI.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".