Clinical Integration of NIR-II Fluorescence Imaging for Cancer Surgery: A Translational Evaluation of Preclinical and Intraoperative Systems
Bibliographic record
Abstract
Background/Objectives: Back table fluorescence imaging performed on freshly excised tissue specimens represents a critical step in fluorescence-guided surgery, enabling rapid assessment of tumor margins before final pathology. While most preclinical NIR-II imaging platforms, such as the IR VIVO (Photon, etc.), offer high-resolution and depth-sensitive imaging under controlled, enclosed conditions, they are not designed for intraoperative or point-of-care use. This study compares the IR VIVO with the LightIR system, a more compact and clinically adaptable imaging platform using the same Alizé 1.7 InGaAs detector, to evaluate whether the LightIR can offer comparable performance for back table NIR-II imaging under ambient light. Methods: Standardized QUEL phantoms containing indocyanine green (ICG) and custom agar-based tissue-mimicking phantoms loaded with IR-1048 were imaged on both systems. Imaging sensitivity, spatial resolution, and depth penetration were quantitatively assessed. LightIR was operated in pulse-mode under ambient lighting, mimicking back table or intraoperative use, while IR VIVO was operated in a fully enclosed configuration. Results: The IR VIVO system achieved high spatial resolution (~125 µm) and detected ICG concentrations as low as 30 nM in NIR-I and 300 nM in NIR-II. The LightIR system, though requiring longer exposure times, successfully resolved features down to ~250 µm and detected ICG to depths ≥4 mm. Importantly, the LightIR maintained robust NIR-II contrast under ambient lighting, aided by real-time background subtraction, and enabled clear visualization of subsurface IR-1048 targets in unshielded phantom setups, conditions relevant to back table workflows. Conclusions: LightIR offers performance comparable to the IR VIVO in terms of depth penetration and spatial resolution, while also enabling open-field NIR-II imaging without the need for a blackout enclosure. These features position the LightIR as a practical alternative for rapid, high-contrast fluorescence assessment during back table imaging. The availability of such clinical-grade systems may catalyze the development of new NIR-II fluorophores tailored for real-time surgical applications.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".