Personalized Multimodal Demarcation of Peritumoral Tissue in Glioma
Bibliographic record
Abstract
PURPOSE: Gliomas are life-threatening brain tumors, and the extent of surgical resection is one of the strongest influences on survival rate. However, the proper distinction of infiltrated tissue remains elusive. The aim of this study was to use multimodal analyses to demarcate peritumoral tissue (PT) from tumoral (TT) and healthy tissue (HT). METHODS: A total of 40 patients with histologically confirmed glioma were recruited. We analyzed resting-state functional magnetic resonance imaging (rs-fMRI) using the voxel-based mean blood-oxygen-level-dependent (BOLD) signal and the corresponding structural MRI (s-MRI) alongside RNA sequencing, whole-exome sequencing, and histology results of biopsy samples obtained from PT, HT, and TT. RESULTS: We demarcated a functionally defined PT area where the mean BOLD signal gradually decreased near the edge of the tumor and extended beyond the TT borders (as defined by s-MRI), which was confirmed on a case-by-case basis. Correspondingly, genetic analyses showed a gene expression pattern and mutational landscape of the PT that were distinct from that seen in HT and TT. The genetic characterization of PT relative to HT and TT converged with the MRI-defined PT zones. This was confirmed in three individual cases after additional histologic analysis. A wider PT was associated with a longer progression-free survival, which suggests PT might act as an intermediate area between TT and HT. CONCLUSION: Combined multimodal imaging and genetic analyses can allow for an objective demarcation of the PT in glioma and a robust classification of the degree of infiltration of the PT. These findings could help improve both neurosurgical resection and radio-oncologic therapy.
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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 | 0.000 |
| 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) | 0.001 | 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".