Evaluating contralateral neck failure in patients with lateralized OPSCC treated with transoral robotic surgery and neck management based on pre-operative SPECT-CT lymphatic mapping
Bibliographic record
Abstract
BACKGROUND: Risk of contralateral nodal metastases in oropharyngeal squamous cell carcinoma (OPSCC) is relatively low, however, many OPSCC patients receive bilateral neck treatment. This study evaluates the oncological outcomes with management of the contralateral cN0 neck based on lymphatic mapping with single photon emission computed tomography (SPECT-CT). METHODS: Retrospective evaluation of patients with lateralized cT1-2 and contralateral cN0 OPSCC treated with primary surgery between December 2017 and October 2019. All patients underwent pre-operative lymphatic mapping using SPECT-CT. Clinical parameters including demographics, tumor characteristics and oncological outcomes were recorded. RESULTS: Thirteen patients underwent primary site resection with transoral robotic surgery (TORS) and ipsilateral neck dissection with or without adjuvant therapy. Twelve patients (92.3%) had ipsilateral drainage on SPECT-CT, whereas 1 (7.7%) patient had bilateral neck lymphatic drainage. Four patients (30.8%) underwent post-operative radiation therapy (PORT). Three patients with unilateral drainage on SPECT-CT underwent PORT with unilateral neck irradiation, and 1 patient with bilateral drainage underwent PORT with bilateral neck irradiation. Seven (53.8%) patients were staged as pT1, 6 (46.2%) patients as pT2, 6 (46.2%) patients were pN0, 3 (23.1%) patients were pN1, 1 (7.7%) patient was pN2a for and 3 (23.1%) patients were N2b. The median distance of the tumor from midline was 1.05 cm (0.0-1.58). Primary sites included tonsil (n = 10, 76.9%) and tongue base (n = 3, 23.1%). The median follow-up time was 15.4 months. All patients were disease free at the latest follow-up with no contralateral neck failures. CONCLUSIONS: Pre-operative mapping of lymphatic drainage in early stage OPSCC with SPECT-CT is a promising tool which can reduce treatment to the contralateral neck potentially without compromising oncological outcomes.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".