[99mTc]Tilmanocept Accurately Detects Sentinel Lymph Nodes and Predicts Node Pathology Status in Patients with Oral Squamous Cell Carcinoma of the Head and Neck: Results of a Phase III Multi-institutional Trial
Bibliographic record
Abstract
BACKGROUND: [(99m)Tc]Tilmanocept, a novel CD206 receptor-targeted radiopharmaceutical, was evaluated in an open-label, phase III trial to determine the false negative rate (FNR) of sentinel lymph node biopsy (SLNB) relative to the pathologic nodal status in patients with intraoral or cutaneous head and neck squamous cell carcinoma (HNSCC) undergoing tumor resection, SLNB, and planned elective neck dissection (END). Negative predictive value (NPV), overall accuracy of SLNB, and the impact of radiopharmaceutical injection timing relative to surgery were assessed. METHODS AND FINDINGS: This multicenter, non-randomized, single-arm trial (ClinicalTrials.gov identifier NCT00911326) enrolled 101 patients with T1-T4, N0, and M0 HNSCC. Patients received 50 µg [(99m)Tc]tilmanocept radiolabeled with either 0.5 mCi (same day) or 2.0 mCi (next day), followed by lymphoscintigraphy, SLNB, and END. All excised tissues were evaluated for tissue type and tumor presence. [(99m)Tc]Tilmanocept identified one or more SLNs in 81 of 83 patients (97.6 %). Of 39 patients identified with any tumor-positive nodes (SLN or non-SLN), one patient had a single tumor-positive non-SLN in whom all SLNs were tumor-negative, yielding an FNR of 2.56 %; NPV was 97.8 % and overall accuracy was 98.8 %. No significant differences were observed between same-day and next-day procedures. CONCLUSIONS: Use of receptor-targeted [(99m)Tc]tilmanocept for lymphatic mapping allows for a high rate of SLN identification in patients with intraoral and cutaneous HNSCC. SLNB employing [(99m)Tc]tilmanocept accurately predicts the pathologic nodal status of intraoral HNSCC patients with low FNR, high NPV, and high overall accuracy. The use of [(99m)Tc]tilmanocept for SLNB in select patients may be appropriate and may obviate the need to perform more extensive procedures such as END.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".