Towards transcervical ultrasound-guided transoral robotic surgery
Bibliographic record
Abstract
BACKGROUND: In the context of transoral robotic surgery (TORS) for oropharyngeal squamous carcinoma (OPSCC), preoperative imaging and intraoperative visualization plays a pivotal role in optimizing resection margins. Prior work has demonstrated the ability of transoral ultrasound (US) in identifying OPSCC margins and vascular structures. This study evaluates the effectiveness of transcervical ultrasound (TUS), as well as other preoperative imaging modalities, in evaluating OPSCC volumes and compares this to post TORS pathological OPSCC volumes. METHODS: Forty-one patients undergoing TORS between 2021 and 2023 were included. TUS was performed in all 41 patients, of which 37 had preoperative CT, 16 had PET-CT and 15 had MRI. Tumor dimensions on TUS, CT, and MRI were measured in craniocaudal, anteroposterior, and mediolateral planes to compute tumor volumes. Preoperative PET-CTs were analyzed to compute the metabolic tumour volume (MTV). Pathological tumor volumes served as the gold standard for comparison. RESULTS: No statistically significant differences were found between pathological tumor volumes and those measured by TUS, CT, PET-CT, or MRI (p = 0.57, 0.47, 0.28, 0.29). Both TUS and PET-CT showed strong correlation with pathology (R = 0.92, p < 0.0001), followed by CT (R = 0.83, p < 0.0001) and MRI (R = 0.55, p = 0.031). The percent difference of radiologic volumes from pathology volumes was lowest for MRI (19.37 % ± 28.28), followed by TUS (26.12 % ± 20.97), PET-CT (32.59 % ± 21.95), and CT (39.94 % ± 62.94). CONCLUSIONS: TUS demonstrates comparable accuracy to CT, PET-CT, and MRI in assessing primary tumor volumes in patients with oropharyngeal squamous cell carcinoma (OPSCC) undergoing TORS. The strong correlation of TUS with final pathology, combined with its relatively non-invasive transcervical (versus transoral) approach and real-time acquisition, suggests that TUS has the potential to supplement TORS with image guidance.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".