Feasibility of a Novel 3D Ultrasound Imaging Technique for Intraoperative Margin Assessment during Tongue Cancer Surgery
Bibliographic record
Abstract
Squamous cell carcinoma (SCC) of the tongue is the most prevalent form of oral cavity cancer, with surgical intervention as the preferred method of treatment. Achieving negative or free resection margins of at least 5 mm is associated with improved local control and prolonged survival. Nonetheless, margins that are close (1–5 mm) or positive (less than 1 mm) are often observed in practice, especially for the deep margins. Ultrasound is a promising tool for assessing the depth of invasion, providing non-invasive, real-time imaging for accurate evaluation. We conducted a clinical trial using a novel portable 3D ultrasound imaging technique to assess ex vivo surgical margin assessment in the operating room. During the operation, resected surgical specimens underwent 3D ultrasound scanning. Four head and neck surgeons measured the surgical margins (deep, medial, and lateral) and tumor area on the 3D ultrasound volume. These results were then compared with the histopathology findings evaluated by two head and neck pathologists. Six patients diagnosed with tongue SCC (three T1 stage and three T2 stage) were enrolled for a consecutive cohort. The margin status was correctly categorized as free by 3D ultrasound in five cases, and one case with a “free” margin status was incorrectly categorized by 3D ultrasound as a “close” margin. The Pearson correlation between ultrasound and histopathology was 0.7 (p < 0.001), 0.6 (p < 0.001), and 0.3 (p < 0.05) for deep, medial, and lateral margin measurements, respectively. Bland–Altman analysis compared the mean difference and 95% limits of agreement (LOA) for deep margin measurement by 3D ultrasound and histopathology, with a mean difference of 0.7 mm (SD 1.15 mm). This clinical trial found that 3D ultrasound is accurate in deep margin measurements. The implementation of intraoperative 3D ultrasound imaging of surgical specimens may improve the number of free margins after tongue cancer treatment.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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 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".