Thallium SPECT-Based Stereotactic Targeting for Brain Tumor Biopsies
Bibliographic record
Abstract
MR or CT images acquired under stereotactic conditions are often used to plan and guide brain tumor biopsies. The objective of this study was to design and test a methodology to increase target selection reliability by acquiring stereotactic 201Tl-SPECT data and by integrating them into the surgical planning. The three-headed Philips gamma camera system (Prism 3000) was adapted to stereotactic acquisitions (patient pallet, headholder). A software was developed for the stereotactic target determination based on SPECT images (pixel with the highest metabolic activity inside the tumor). The whole system accuracy was tested with the Elekta phantom adapted to SPECT imaging. The methodology was applied to one brain tumor biopsy. Comparison of the specific phantom coordinates evaluated in SPECT with the theoretical ones did not reveal any significant difference. In this way, our methodology including our homemade software (identification of the stereotactic frame, determination of the pixel with highest metabolic activity within the tumor in the stereotactic coordinate system) was validated. No significant geometric deformations were detected. Clinical feasibility was confirmed in 1 patient with a brain glioma. This study illustrates the feasibility and the accuracy of SPECT acquisitions with the stereotactic Leksell G-frame. The clinical relevance of this methodology is under evaluation. This definition of the target, based on the point with the highest metabolic activity within the tumor, might lead to improved diagnosis in biopsies and patient management. Furthermore, it might prepare the future for therapy aimed at delivering a therapeutic agent within a tumor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".