P.151 The development of an image-guided, multi-biopsy tool for neurosurgical applications
Bibliographic record
Abstract
Background: Molecular characterization of spatially heterogeneous tumors, such as primary brain tumors, requires precise, contamination-free tissue collection. This study develops a novel biopsy instrument enabling efficient, spatially registered tissue collection across tumors and surgical cavities, advancing personalized tumor characterization and treatment. Methods: Iterative modeling and 3D printing were used to develop prototypes, which underwent proof-of-concept and phantom testing. Final device evaluation compared the novel capsule biopsy tool to Yasargil tumor grasping forceps, with six neurosurgeons and six students conducting tests on porcine brain. Additionally, intraoperative samples were collected, with spatial coordinates recorded on preoperative scans. Results: The design features a capsule that attaches to the end of a Frazier suction. When suction is applied, an internal piston secures the sample, which can later be released before or after tissue fixation. The capsule method reduced variability in sample weight and collection time compared to Yasargil forceps, maintaining tissue integrity without contamination or instrument failure. Notably, students demonstrated proficiency comparable to experienced surgeons, highlighting the tool’s ease of use. Conclusions: This low-cost, optically tracked biopsy tool provides an efficient, reliable method for spatially precise tissue collection, meeting the demands of precision medicine and translational research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 0.003 |
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".