Detection of Non-Melanoma Skin Cancer by in vivo Fluorescence Imaging with Fluorocoxib A
Bibliographic record
Abstract
Non-melanoma skin cancer (NMSC) is the most common form of cancer in the US and its incidence is increasing. The current standard of care is visual inspection by physicians and/or dermatologists, followed by skin biopsy and pathologic confirmation. We have investigated the use of in vivo fluorescence imaging using fluorocoxib A as a molecular probe for early detection and assessment of skin tumors in mouse models of NMSC. Fluorocoxib A targets the cyclooxygenase-2 (COX-2) enzyme that is preferentially expressed by inflamed and tumor tissue, and therefore has potential to be an effective broadly active molecular biomarker for cancer detection. We tested the sensitivity of fluorocoxib A in a BCC allograft SCID hairless mouse model using a wide-field fluorescence imaging system. Subcutaneous allografts comprised of 1000 BCC cells were detectable above background. These BCC allograft mice were imaged over time and a linear correlation (R(2) = 0.8) between tumor volume and fluorocoxib A signal levels was observed. We also tested fluorocoxib A in a genetically engineered spontaneous BCC mouse model (Ptch1(+/-) K14-Cre-ER2 p53(fl/fl)), where sequential imaging of the same animals over time demonstrated that early, microscopic lesions (100 μm size) developed into visible macroscopic tumor masses over 11 to 17 days. Overall, for macroscopic tumors, the sensitivity was 88% and the specificity was 100%. For microscopic tumors, the sensitivity was 85% and specificity was 56%. These results demonstrate the potential of fluorocoxib A as an in vivo imaging agent for early detection, margin delineation and guided biopsies of NMSCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".