Optical Techniques for the Endoscopic Detection of Early Dysplastic Colonic Lesions
Bibliographic record
Abstract
PURPOSE OF REVIEW: Within the past decade, advances in biomedical optics have been applied to overcome sensitivity and specificity limitations in conventional diagnostic endoscopy for detecting dysplasia and noninvasive cancer. This review presents the current status of emerging optical techniques that rely on the complex interaction of light with tissue, and their possible roles in detection, mapping, staging, and therapy of dysplasia. RECENT FINDINGS: Progress in endoscopic autofluorescence imaging has been dominated by efforts to enhance the detection of dysplasia with tumor-localizing prodrugs. Raman and light-scattering spectroscopy have had very limited reporting for the colon. Endoscopic optical coherence tomography has now acquired real-time Doppler capabilities, and rudimentary endoscopic criteria for hyperplastic and adenomatous polyps have been described. Narrow-band imaging endoscopy awaits large-scale testing in the colon. The greatest activity has been reported for chromoendoscopy. Although contradictory results of diagnostic accuracy must be clarified and optimal methodologies are yet to be outlined, magnifying endoscopy has resuscitated chromoendoscopy. Single-cell subsurface imaging has been achieved with confocal fluorescence microendoscopy on excised tissues and now the first clinical experience has been reported. New and exciting research using highly specific and bright tumor-targeted fluorescence contrast agents is thrusting diagnostic endoscopy into a new era of "molecular endoscopy." SUMMARY: Most studies reported to date have mainly focused on refining existing prototype instruments or continue on a basic research level, with the ultimate goal of improving sensitivity and specificity for colonic dysplasia, compared with the gold standard of histopathology. It is more than likely that the future diagnostic role of these evolving technologies will involve the combination of two or more complementary technologies, and "molecular targeting" approaches will be crucial for earliest detection. However, despite innovative applications and refinement of the existing ones, large, controlled, prospective multicenter trials are still anxiously awaited by the practicing endoscopist to evaluate the diagnostic role and cost-effectiveness of these emerging technologies in the gastroenterology clinic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".