Image-Guided Fluorescence Endomicroscopy: From Macro- to Micro-Imaging of Radiation-Induced Pulmonary Fibrosis
Bibliographic record
Abstract
Radiation-induced pulmonary fibrosis (RIPF) is a debilitating side effect of radiation therapy (RT) of several cancers including lung and breast cancers. Current clinical methods to assess and monitor RIPF involve diagnostic computed tomography (CT) imaging, which is restricted to anatomical macroscopic changes. Confocal laser endomicroscopy (CLE) or fluorescence endomicroscopy (FE) in combination with a fibrosis-targeted fluorescent probe allows to visualize RIPF in real-time at the microscopic level. However, a major limitation of FE imaging is the lack of anatomical localization of the endomicroscope within the lung. In this work, we proposed and validated the use of x-ray fluoroscopy-guidance in a rat model of RIPF to pinpoint the location of the endomicroscope during FE imaging and map it back to its anatomical location in the corresponding CT image. For varying endomicroscope positions, we observed a positive correlation between CT and FE imaging as indicated by the significant association between increased lung density on CT and the presence of fluorescent fiber structures with FE in RT cases compared to Control. Combining multimodality imaging allows visualization and quantification of molecular processes at specific locations within the injured lung. The proposed image-guided FE method can be extended to other disease models and is amenable to clinical translation for assessing and monitoring fibrotic damage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".