LIMITATIONS OF CANCER MARGIN DELINEATION BY MEANS OF AUTOFLUORESCENCE IMAGING UNDER CONDITIONS OF LASER SURGERY
Bibliographic record
Abstract
Limitations of cancer margin delineation and surgical guidance by means of autofluorescence imaging under conditions of laser ablation were investigated and preliminary results are presented. PinPoint™ (Novadaq Technologies Inc., Canada) was used to capture digital images and Er:YAG laser (2.94 μm, Glissando, WaveLight™, Germany) was exploited to cause laser ablation on both normal and cancer sites of the specimen. It was shown that changes of the autofluorescence image after ablation extend beyond the actual sizes of the ablation loci. The tumor tissue after the laser ablation starts to emit fluorescent light within the green wavelength band (490–550 nm) similar to normal tissue stating that the current technology of in-process tissue classification fails. However, when the autofluorescence was collected in the red range (600–750 nm), then the abnormal/normal contrast was reduced, but still present even after the laser ablation. The present study highlights the importance of finding a proper technology for surgical navigation of cancer removal under conditions of high power effects in biological tissues.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".