A multispectral fluorescence imaging system: Design and initial clinical tests in intra‐operative Photofrin‐photodynamic therapy of brain tumors
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Intra-operative identification of tumor by fluorescence may improve surgical resection or photodynamic therapy (PDT). A novel instrument was designed, constructed, and tested for this purpose. STUDY DESIGN/MATERIALS AND METHODS: The instrument was designed to provide high-resolution, multi-spectral (five band) fluorescence imaging, and non-contact point spectroscopy, with long working distance ( approximately 50 cm), large field-of-view ( approximately 3 cm diameter), large depth of view ( approximately 2 cm), and 'point-and-shoot' operation. Its performance was determined in tissue-simulating phantoms and in pilot studies in brain tumor resection patients, with or without intra-operative Photofrin-PDT. RESULTS: In phantoms the imaging resolution was approximately 150 microm, while Photofrin concentrations as low as 0.05 or 0.1 microg/g could be detected at the tissue surface or at 0.5 mm depth, respectively. Red Photofrin fluorescence could be clearly visualized post radical resection in all PDT patients, with biopsy confirmation of residual tumor tissue in regions that were not seen as tumor under white light. Photobleaching of Photofrin during PDT was also demonstrated. CONCLUSIONS: The system performed to specification under realistic operating conditions and could reveal unresected residual tumor tissue. It may be used for either PDT dosimetry/monitoring and/or for surgical guidance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".