In situ optical feedback in brain tumor biopsy: A multiparametric analysis
Bibliographic record
Abstract
Abstract Background Brain tumor needle biopsy interventions are inflicted with nondiagnostic or biased sampling in up to 25% and hemorrhage, including asymptomatic cases, in up to 60%. To identify diagnostic tissue and sites with increased microcirculation, intraoperative optical techniques have been suggested. The aim of this study was to investigate the clinical implications of in situ optical guidance in frameless navigated tumor biopsies. Methods Real-time feedback on protoporphyrin IX (PpIX) fluorescence, microcirculation, and gray-whiteness was given before tissue sampling (272 positions) in 20 patients along 21 trajectories in total. The primary variables of investigation were fluorescence in relation to neuropathological findings and gadolinium (Gd) enhancement, increased cerebral microcirculation in relation to bleeding incidence, number of trajectories, and impact on operation time. Results PpIX fluorescence was detected in Glioblastoma IDH-wildtype CNS WHO grade 4 (n = 12), Primary diffuse large B-cell lymphoma (n = 3), astrocytoma IDH-mutated CNS WHO grade 4 (n = 1) (Ki67 indices ≥ 15%). For 2 patients, no PpIX fluorescence or Gd was found, although samples contained tumorous tissue (Ki67 index 6%). Increased microcirculation was found along 9 trajectories (34 sites), located in cortical, tumorous, or tentorium regions. Postoperative bleedings (n = 10, nine asymptomatic) were related to skull opening or tissue sampling. This study strengthens the proposed independence from intraoperative neuropathology as PpIX fluorescence is detected. Objective real-time feedback resulted in fewer trajectories compared to previous studies indicating reduced operation time. Conclusions The integrated optical guidance system provides real-time feedback in situ, increasing certainty and precision of diagnostic tissue before sampling during frameless brain tumor biopsies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".