Three-dimensional ultrasound imaging for oral cavity squamous cell carcinoma
Bibliographic record
Abstract
Early detection of oral squamous cell carcinoma improves survival and patient outcomes, but standard imaging modalities have limitations, including low sensitivity and high cost. This study evaluates the clinical utility of three-dimensional ultrasound for pre-operative oral cavity cancer imaging, comparing tumor volume measurements to gold-standard magnetic resonance imaging. A healthy volunteer study optimized imaging protocols using custom and commercial ultrasound transducers, with the latter providing superior anatomical visualization of the region. A clinical study is ongoing to assess pre-operative tumor volume with three-dimensional ultrasound imaging, with analysis of nine patients currently completed. The initial results identified bone interference, leading to adjustments in protocol and the exclusion of certain tumor locations. Optimized imaging now provides clear anatomical visualization, with preliminary data suggesting three-dimensional ultrasound tumor volume measurements within 10% of magnetic resonance imaging delineation. This study introduces extraoral and submental three-dimensional ultrasound as a cost-effective, accessible alternative for oral squamous cell carcinoma evaluation, with ongoing analysis to confirm its clinical feasibility.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".