Integrated biophotonics in endoscopic oncology
Bibliographic record
Abstract
Gastrointestinal endoscopy has made great progress during last decade. Diagnostic accuracy can be enhanced by better training, improved dye-contrast techniques method, and the development of new image processing technologies. However, diagnosis using conventional endoscopy with white-light optical imaging is essentially limited by being based on morphological changes and/or visual attribution: hue, saturation and intensity, interpretation of which depends on the endoscopist's eye and brain. In microlesions in the gastrointestinal tract, we still rely ultimately on the histopathological diagnosis from biopsy specimens. Autofluorescence imaging system has been applied for lesions which have been difficult to morphologically recognize or are indistinct with conventional endoscope, and this approach has potential application for the diagnosis of dysplastic lesions and early cancers in the gastrointestinal tract, supplementing the information from white light endoscopy. This system has an advantage that it needs no administration of a photosensitive agent, making it suitable as a screening method for the early detection of neoplastic tissues. Narrow band imaging (NBI) is a novel endoscopic technique which can distinguish neoplastic and non-neoplastic lesions without chromoendoscopy. Magnifying endoscopy in combination with NBI has an obvious advantage, namely analysis of the epithelial pit pattern and the vascular network. This new technique allows a detailed visualization in early neoplastic lesions of esophagus, stomach and colon. However, problems remain; how to combine these technologies in an optimum diagnostic strategy, how to apply them into the algorithm for therapeutic decision-making, and how to standardize several classifications surrounding them. 'Molecular imaging' is a concept representing the most novel imaging methods in medicine, although the definition of the word is still controversial. In the field of gastrointestinal endoscopy, the future of endoscopic diagnosis is likely to be impacted by a combination of biomarkers and technology, and 'endoscopic molecular imaging' should be defined as "visualization of molecular characteristics with endoscopy". These innovations will allow us not only to locate a tumor or dysplastic lesion but also to visualize its molecular characteristics (e.g., DNA mutations and polymorphisms, gene and/or protein expression), and the activity of specific molecules and biological processes that affect tumor behavior and/or its response to therapy. In the near future, these methods should be promising technologies that will play a central role in gastrointestinal oncology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".