Imaging Cutaneous Head and Neck Cancer with Panitumumab‐IRDye
Bibliographic record
Abstract
Objective To determine if intraoperative, real‐time fluorescence imaging hardware (SPY System, Novadaq, Toronto, Canada) and lab‐based fluorescence optical imaging of histological sections (Odyssey scanner, LiCor) are capable of detecting micrometastatic cutaneous squamous cell carcinoma (SCC) in preclinical models. Method An NIR fluorescent probe (IRDye800) was covalently linked to a monoclonal antibody targeting EGFR (panitumumab) or non‐specific IgG and injected into mice bearing flank xenografts derived from a cutaneous SCC cell line (SCC‐13). The primary tumor and regional lymph nodes were imaged and dissected using fluorescence guidance with the SPY system and verified with a charge‐coupled NIR system (Pearl). An NIR charged‐coupled device (Odyssey) was used to measure fluorescence intensity of cut sections of tumor and were confirmed with immunohistochemical staining (cytokeratin, CD147). Results Tumors were clearly delineated from normal tissue with tumor‐to‐background ratios of 4.5 (Pearl) and 3.4 (SPY). Disease detection was significantly improved with panitumumab‐IRDye compared to IgG‐IRDye800 (P <. 05). Tissue biopsies positive for fluorescence were confirmed for pathologic disease by histology and immunohistochemistry (n = 10/10); while biopsies of non‐fluorescent tissue were proven to be negative for malignancy (n = 12/12). The SPY system was able to detect regional lymph node metastasis (<1.0 mm) and microscopic areas of disease as small as 200 micrometers in diameter. Additionally, the Odyssey successfully detected residual microscopic disease in both frozen and paraffin‐embedded histologic specimens. Conclusion These data suggest that panitumumab‐IRDye800 may have clinical utility in detection and removal of cutaneous SCC using existing optical imaging hardware. This technique may prove useful in the detection of microscopic disease grossly and in histological sections.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".