Abstract IA12: Fluorescence guided surgery: Rapidly evolving applications
Bibliographic record
Abstract
Abstract Despite advances in operative technology, intraoperative methods have not improved cancer detection during surgery in the past 30 years. We propose to test the combination of high-resolution/depth-limited imaging properties of optical imaging agents with the low-resolution/tissue penetrating properties of nuclear agents for detection of primary tumors and regional lymph nodes. We propose that combined optical and nuclear imaging can detect tumor fragments less than 1 mm3 in vivo during removal of head and neck cancers. In fact, nuclear PET imaging alone may be highly successful - The current standard of care for evaluation and surveillance of regional and metastatic disease in head and neck squamous cell carcinoma (HNSCC) is FDG-PET/CT scan, which is highly sensitive but not specific. To address this, we show that a head and neck cancer, tumor-specific PET radiopharmaceutical can improve diagnostic specificity in this patient population. Citation Format: Eben L. Rosenthal, Marisa L. Hom, Goulan Lu. Fluorescence guided surgery: Rapidly evolving applications [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr IA12.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".