Fluorescently labeled therapeutic antibodies for detection of microscopic melanoma
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Detection of microscopic disease during surgical resection of melanoma remains a significant challenge. To assess real-time optical imaging for visualization of microscopic cancer, we evaluated three US Food and Drug Administration (FDA)-approved therapeutic monoclonal antibodies. STUDY DESIGN: Prospective, basic science. METHODS: Melanoma cell lines (A375 and SKMEL5) were xenografted into the ears of immunodeficient mice. Bevacizumab, panitumumab, tocilizumab, or a nonspecific immunoglobin G (IgG) were covalently linked to a near-infrared (NIR) fluorescent probe (IRDye800CW) and systemically injected. Primary tumors were imaged and then resected under fluorescent guidance using the SPY (Novadaq, Toronto, Ontario, Canada), an NIR imaging system used in plastic and reconstructive surgeries to evaluate perfusion. Mice were also imaged with the Pearl Impulse small animal imager (LI-COR Biosciences, Lincoln, NE), an NIR imaging system designed for use with IRDye800CW. Postresection, small tissue fragments were fluorescently imaged and the presence of tumor subsequently confirmed by correlation with histology. RESULTS: All fluorescently labeled therapeutic monoclonal antibodies could adequately delineate tumor from normal tissue based on tumor-to-background ratios (TBR) compared to IgG-IRDye800CW. On serial imaging, panitumumab achieved the highest TBRs with both SPY and Pearl (3.8 and 6.6, respectively). When used to guide resections, the antibody-dye conjugates generated TBRs in the range of 1.3 to 2.2 (average, 1.6) using the SPY and 1.9 to 6.3 (average, 2.7) using the Pearl. There was no significant difference among the antibodies with either imaging modality or cell line (one-way analysis of variance). CONCLUSIONS: Our data suggest that FDA-approved antibodies may be suitable targeting agents for the intraoperative fluorescent detection of melanoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".