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.
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 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".