Quantitative and Depth-resolved Fluorescence Guidance for the Resection of Glioma
Bibliographic record
Abstract
The clinical management of glioma remains a challenge. The prognosis is poor—for glioblastoma multiforme, the most virulent of these brain cancers, survival is only ~1 year. Surgical resection of the tumor is the first line of defense. Several studies demonstrate a survival advantage in patients who undergo near-complete tumor resection; however, achieving complete resection is limited by the difficulty of visualizing residual tumor after de-bulking. Intraoperative fluorescence guidance is a promising candidate to better visualize residual tumor. The most clinically developed form uses protoporphyrin IX fluorescence, the precursor to heme in its biosynthesis which preferentially accumulates in tumor cells after the administration of 5-aminolevulinic acid. Challenges remain in quantitatively assessing the fluorescence to reduce variability of outcome and improve tumor detection specificity, and in observing sub-surface tumor fluorescence. To these ends, this work outlines the development of intraoperative techniques to 1) quantify tissue fluorescence using a handheld fiberoptic probe and 2) improve detection by reconstructing the depth-resolved fluorescence topography of sub-surface tumor. \n\tAs a critical component to achieve these objectives, a technique to measure the tissue optical properties was developed. This technique used diffuse reflectance measurements mediated by a handheld fiberoptic probe to derive the tissue optical properties. The handheld fiberoptic probe was further developed to include fluorescence spectroscopy. A novel algorithm to combine the fluorescence measurement and the tissue optical properties was derived in order to extract the quantitative fluorescence spectrum, i.e. fluorescence without confounding effects of tissue optical properties. The concentration of fluorescent tumor biomarker can then be extracted. The quantitative fluorescence work culminated in deployment of the fiberoptic probe in clinical trials for the resection of intracranial tumors. The quantitative fluorescence probe out-performed a state-of-the-art fluorescence surgical microscope for a broad range of brain tumor pathologies.\n\tA novel technique for depth-resolved fluorescence detection was developed utilizing multi-excitation fluorescence imaging. An algorithm to extract depth information from the multi-excitation images was derived, with validation in phantoms and a rat brain tumor model. This demonstrates the potential for depth-resolved fluorescence imaging, which there is a clear need for in tumor resection 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".