New optical technologies for earlier endoscopic diagnosis of premalignant gastrointestinal lesions
Bibliographic record
Abstract
Gastrointestinal malignancies continue to be the second leading cause of cancer-related deaths in the developed world. The early detection and treatment of gastrointestinal preneoplasms has been demonstrated to significantly improve patient survival. Conventional screening tools include standard white light endoscopy (WLE) and frequent surveillance with biopsy. Well-defined endoscopic surveillance biopsy protocols aimed at early detection of dysplasia and malignancy have been undertaken for groups at high risk. Unfortunately, the poor sensitivity associated with WLE is a significant limitation. In this regard, major efforts continue in the development and evaluation of alternative diagnostic techniques. This review will focus on notable developments made at the forefront of research in modern gastrointestinal endoscopy based on novel optical endoscopic modalities, which rely on the interactions of light with tissues. Here we present the 'state-of-the-art' in fluorescence endoscopic imaging and spectroscopy, Raman spectroscopy, optical coherence tomography, light scattering spectroscopy, chromoendoscopy, confocal fluorescence endoscopy, and immunofluorescence endoscopy. These new developments may offer significant improvements in the diagnosis of early lesions by allowing for targeted mucosal excisional biopsies, and perhaps may even provide 'optical biopsies' of equivalent histological accuracy. This enhancement of the endoscopist's ability to detect subtle preneoplastic changes in the gastrointestional mucosa in real time and improved staging of lesions could lead to curative endoscopic ablation of these lesions and, in the long term, improve patient survival and quality of life.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".