Optical spectroscopy and imaging in surgical management of cancer patients
Bibliographic record
Abstract
Abstract Surgery is a pillar of cancer management, the general goal being complete removal of solid tumor tissue with minimal damage to normal tissue structure and function. Optical imaging and spectroscopy may contribute to this at several points in the procedural chain, including preoperative tumor localization and staging by biopsy, intra/perioperative identification and localization of tumor margins, detection of residual tumor tissue and tumor‐involved lymph nodes as well as critical normal tissue structures, and assessment of the viability of reconstructed tissues following tumor resection. The numerous optical modalities that can be implemented clinically are discussed, a few of which are already in clinical practice and many more are in clinical trials. These modalities utilize different light‐tissue interactions and technical approaches. The resulting biological information obtained, the current or potential clinical impact, limitations and potential future developments and roles are considered. This article is intended to inform the biophotonics community of the clinical needs and scientific/technical challenges and opportunities in photonics‐enabled surgical guidance and to guide potential surgical users on the potential advantages and limitations in this rapidly evolving landscape.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".