Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients
Bibliographic record
Abstract
Abstract Glioblastoma widely affects brain structure and function, and remodels neural connectivity. Characterizing the neural connectivity in glioblastoma may provide a tool to understand tumor invasion. Here, using a structural connectome approach based on diffusion MRI, we quantify the global and regional connectome disruptions in individual glioblastoma patients and investigate the prognostic value of connectome disruptions and topological properties. We show that the disruptions in the normal-appearing brain beyond the lesion could mediate the topological alteration of the connectome ( P <0.001), associated with worse patient performance ( P <0.001), cognitive function ( P <0.001), and survival (overall survival: HR: 1.46, P = 0.049; progression-free survival: HR: 1.49, P = 0.019). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of remodeling, where increased connectivity is associated with better overall survival (log-rank P = 0.005). Our approach reveals the glioblastoma invasion invisible on conventional MRI, promising to benefit patient stratification and precise treatment.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".