Using Quality Function Deployment to Design an Image-Guided, Multibiopsy Tool for Neurosurgical Applications
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The ability to molecularly characterize spatially heterogeneous tumors, such as primary brain tumors, depends on the efficient and consistent collection of spatially defined tissue samples free of cross-contamination. Currently available neurosurgical tools, designed for clinical use rather than modern molecular characterization, limit our view of heterogeneous processes to snapshots of single regions. This study introduces a novel biopsy device that enables the precise, reproducible, and spatially registered collection of tissue across a tumor and surgical cavity, paving the way for advancements in personalized tumor characterization and treatment. METHODS: Prototypes were developed using a Quality Function Deployment framework to prioritize user requirements and technical needs. Iterative modeling and 3D printing produced prototypes that underwent proof-of-concept and phantom testing. Final validation involved comparative testing of the novel biopsy tool and Yasargil tumor-grasping forceps by 6 neurosurgeons and 6 students. Clinical feasibility was assessed through the collection of 10 intraoperative tissue samples using each device. RESULTS: The lead design, which met all Quality Function Deployment requirements, consists of an optically tracked capsule that attaches to a Frazier suction. When suction is applied, a piston is pulled up and the sample is securely contained. After releasing the suction, manual depression ejects the tissue. In comparative testing, the capsule method reduced variability in sample weight and collection time compared with the Yasargil forceps. It also demonstrated greater ease of use, enabling students to achieve results comparable with experienced surgeons. Clinical testing revealed no differences in sample variability, tissue preservation, or instrument failure. CONCLUSION: This optically tracked navigated biopsy tool offers a low-cost, efficient, easily used, and consistent method for brain biopsy collection. The novel device is well suited for precision medicine and translational research needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".