Assessing glymphatic dysfunction using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index in gliomas and metastases
Bibliographic record
Abstract
Purpose To evaluate glymphatic dysfunction using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index in patients with low-grade gliomas (LGGs), high-grade gliomas (HGGs), and metastases, assess its feasibility as a non-invasive biomarker for tumour differentiation, and examine its relationship with tumour-specific characteristics. Material and methods This single-centre retrospective study examined patients with LGGs (n = 30), HGGs (n = 30), and metastases (n = 20), calculating the DTI-ALPS index. Tumour volume, peritumoral oedema, tumour volumeto-total brain volume ratio (TV/TBV), and peritumoral oedema-to-total brain volume ratio (PTE/TBV) were obtained using 3D segmentation. The DTI-ALPS index was compared across the 3 tumour groups, and its relationships with tumour-associated oedema, peritumoral oedema, TV/TBV, and PTE/TBV were analysed within and between the groups. Additionally, the DTI-ALPS index was compared between isocitrate dehydrogenase-1 (IDH1) mutant and IDH1 wild-type gliomas. Results There was a significant difference in the DTI-ALPS index between the 3 groups (p < 0.001), with HGGs having the lowest DTI-ALPS index values, followed by metastases and LGGs. Receiver operating characteristic (ROC)analysis showed that the DTI-ALPS index had excellent sensitivity and specificity for LGGs (> 1.3838) and HGGs (< 1.317). No significant correlation was found between the DTI-ALPS index and tumour volume, TV/TBV, PTE/TBV, or peritumoral oedema. Furthermore, the mean DTI-ALPS index in IDH1 wild-type gliomas (1.23 ± 0.08) was significantly lower than that observed in IDH1 mutant tumours (1.42 ± 0.10; p < 0.001). Conclusions The DTI-ALPS index provides valuable insights into glymphatic dysfunction in brain tumours. This study underscores its potential as a non-invasive biomarker in differentiating these tumour groups.
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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 | 0.001 | 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) | 0.000 | 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".