Use of MR signal intensity variations to highlight structures at risk along brain biopsy trajectories
Bibliographic record
Abstract
OBJECTIVE: Postoperative intracerebral hemorrhages are significant complications following brain stereotactic biopsy. They can derive from anatomical structure (sulci, vessels) damage that is missed during stereotactic trajectory planning. In this study, the authors investigated the ability to detect contact between structures at risk and stereotactic trajectories using signal analysis from MRI obtained during clinical practice, with the aim to propose a visual tool to highlight areas with anatomical structures at risk of damage along the biopsy trajectory. METHODS: The authors retrospectively analyzed actual stereotactic trajectories using intraoperative imaging (intraoperative 2D radiographs in the exploratory data set and intraoperative 3D scans in the confirmatory data set). The MR signal variation along each biopsy trajectory was matched with the patient's anatomy. RESULTS: In the exploratory data set (n = 154 patients), 32 contacts between the actual biopsy trajectory and an anatomical structure at risk were identified along 28 (18.2%) biopsy trajectories, corresponding to 8 preventable intracerebral hemorrhages. Variations of the mean derivative of the MR signal intensity were significantly different between trajectories with and without contact (the pathological threshold of the mean derivative of the MR signal intensity was defined as ± 0.030 arbitrary units; p < 0.0001), with a sensitivity of 89.3% and specificity of 74.6% to detect a contact. In the confirmatory data set (n = 73 patients), the sensitivity and specificity of the 0.030 threshold to detect a contact between the actual stereotactic trajectory and an anatomical structure at risk were 81.3% and 68.4%, respectively. CONCLUSIONS: Variations of the mean derivative of the MR signal intensity can be converted into a green/red color code along the planned biopsy trajectory to highlight anatomical structures at risk, which can help neurosurgeons during the surgical planning of stereotactic procedures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".