Pre-Surgical Integration of fMRI and DTI of the Sensorimotor System in Transcortical Resection of a High-Grade Insular Astrocytoma
Bibliographic record
Abstract
Herein we report on a patient with a WHO Grade III astrocytoma in the right insular region in close proximity to the internal capsule who underwent a right frontotemporal craniotomy. Total gross resection of insular gliomas remains surgically challenging based on the possibility of damage to the corticospinal tracts. However, maximizing the extent of resection has been shown to decrease future adverse outcomes. Thus, the goal of such surgeries should focus on maximizing extent of resection while minimizing possible adverse outcomes. In this case, pre-surgical planning included integration of functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), to localize motor and sensory pathways. Novel fMRI tasks were individually developed for the patient to maximize both somatosensory and motor activation simultaneously in areas in close proximity to the tumor. Information obtained was used to optimize resection trajectory and extent, facilitating gross total resection of the astrocytoma. Across all three motor-sensory tasks administered, fMRI revealed an area of interest just superior and lateral to the astrocytoma. Further, DTI analyses showed displacement of the corona radiata around the superior dorsal surface of the astrocytoma, extending in the direction of the activation found using fMRI. Taking into account these results, a transcortical superior temporal gyrus surgical approach was chosen in order to avoid the area of interest identified by fMRI and DTI. Total gross resection was achieved and minor post-surgical motor and sensory deficits were temporary. This case highlights the utility of comprehensive pre-surgical planning, including fMRI and DTI, to maximize surgical outcomes on a case-by-case basis.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".