DTI Abnormalities Related to Glioblastoma: A Prospective Comparative Study with Metastasis and Healthy Subjects
Bibliographic record
Abstract
(1) Background: Glioblastoma multiforme (GBM) shows complex mechanisms of spreading of the tumor cells, up to remote areas, and little is still known of these mechanisms, thus we focused on MRI abnormalities observable in the tumor and the brain adjacent to the lesion, up to the contralateral hemisphere, with a special interest on tensor diffusion imaging informing on white matter architecture; (2) Material and Methods: volumes, macroscopic volume (MV), brain-adjacent-tumor (BAT) volume and abnormal color-coded DTI volume (aCCV), and region-of-interest samples (probe volumes, ipsi, and contra lateral to the lesion), with their MRI characteristics, apparent diffusion coefficient (ADC), fractional anisotropy (FA) values, and number of fibers (DTI fiber tracking) were analyzed in patients suffering GBM (n = 15) and metastasis (n = 9), and healthy subjects (n = 15), using ad hoc statistical methods (type I error = 5%) (3) Results: GBM volumes were larger than metastasis volumes, aCCV being larger in GBM and BAT ADC was higher in metastasis, ADC decreased centripetally in metastasis, FA increased centripetally either in GBM or metastasis, MV and BAT FA values were higher in GBM, ipsi FA values of GBM ROIs were higher than those of metastasis, and the GBM ipsi number of fibers was higher than the GBM contra number of fibers; (4) Conclusions: The MV, BAT and especially the aCCV, as well as their related water diffusion characteristics, could be useful biomarkers in oncology and functional oncology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".