A multimodal nano agent for image-guided cancer surgery
Bibliographic record
Abstract
Intraoperative imaging technologies including computed tomography and fluorescence optical imaging are becoming routine tools in the cancer surgery operating room. They constitute an enabling platform for high performance surgical resections that assure local control while minimizing morbidity. New contrast agents that can increase the sensitivity and visualization power of existing intraoperative imaging techniques will further enhance their clinical benefit. We report here the development, detection and visualization of a dual-modality computed tomography and near-infrared fluorescence nano liposomal agent (CF800) in multiple preclinical animal models of cancer. We describe the successful application of this agent for combined preoperative computed tomography based three-dimensional surgical planning and intraoperative target mapping (>200 Hounsfield Units enhancement), as well as near-infrared fluorescence guided resection (>5-fold tumor-to-background ratio). These results strongly support the clinical advancement of this agent for image-guided surgery with potential to improve lesion localization, margin delineation and metastatic lymph node detection.
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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".