Development and evaluation of a connective tissue phantom model for subsurface visualization of cancers requiring wide local excision
Bibliographic record
Abstract
Wide local excision (WLE) of tumors with negative margins remains a challenge because surgeons cannot directly visualize the mass. Fluorescence-guided surgery (FGS) may improve surgical accuracy; however, conventional methods with direct surface tumor visualization are not immediately applicable, and properties of tissues surrounding the cancer must be considered. We developed a phantom model for sarcoma resection with the near-infrared fluorophore IRDye 800CW and used it to iteratively define the properties of connective tissues that typically surround sarcoma tumors. We then tested the ability of a blinded surgeon to resect fluorescent tumor-simulating inclusions with ∼1-cm margins using predetermined target fluorescence intensities and a Solaris open-air fluorescence imaging system. In connective tissue-simulating phantoms, fluorescence intensity decreased with increasing blood concentration and increased with increasing intralipid concentrations. Fluorescent inclusions could be resolved at ≥1-cm depth in all inclusion concentrations and sizes tested. When inclusion depth was held constant, fluorescence intensity decreased with decreasing volume. Using targeted fluorescence intensities, a blinded surgeon was able to successfully excise inclusions with ∼1-cm margins from fat- and muscle-simulating phantoms with inclusion-to-background contrast ratios as low as 2∶1. Indirect, subsurface FGS is a promising tool for surgical resection of cancers requiring WLE.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".