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Record W4407568310 · doi:10.1117/12.3047317

Three-dimensional ultrasound imaging for oral cavity squamous cell carcinoma

2025· article· en· W4407568310 on OpenAlexaff
Tiana Trumpour, Freeman Paczkowski, Mary-Ellen Empey, Carla du Toit, Claire Keun Sun Park, Jacob Wihlidal, David Tessier, Aaron Fenster, Jane Topple, Adrian Mendez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsSt. Joseph’s Healthcare HamiltonUniversity of Toronto
Fundersnot available
KeywordsBasal cellOral cavityUltrasoundUltrasonic imagingUltrasound imagingRadiologyMedicinePathologyDentistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.272
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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