Unraveling differences and hallmarks in suspected diffuse low-grade gliomas: a multicenter database study
Bibliographic record
Abstract
Abstract The natural history of suspected diffuse low-grade gliomas (DLGG) depends heavily upon the molecular status. To fully comprehend this integrated information preoperatively, a clinical phenotype incorporating both clinical and radiological information may be of value. We aimed to analyze this systematically in a large multicenter study to identify clinical/radiological phenotypes of DLGG molecular subgroups at the onset. Patients from 9 Scandinavian centers, with confirmed World Health Organization (WHO) grade 2 at the time of diagnosis (according to WHO 2016/2007 classification), known molecular status (isocitrate dehydrogenase [IDH] status and 1p19q co-deletion status) and preoperative images of adequate quality, were analyzed. MRI-based tumor volume segmentation was used to create a frequency map of their locations into the Montreal Neurological Institute space. The Brain-Grid (BG) system was used for tumor invasiveness analysis. Variables were analyzed for each subgroup of DLGG with regression analyses. 235 patients were included. The three molecular subgroups differed in age, tumor location, epileptic onset, and cognitive status. Seizure onset was linked to the number of BG voxels and A3C2S2 location in all three molecular groups. Cognitive deficits were related to increasing age (IDH-mutated-Oligodendrogliomas), female gender (IDH-wildtype) and tumor volume (Oligodendrogliomas). Patients with IDH-mutated astrocytomas (n=65) displayed younger age, left sided fronto-insular preferential location, infiltration of anterior ventral inferior fronto-occipital fasciculus (IFOF) and external capsule, and seizure as onset symptom. Oligodendrogliomas (n=116) were more often found in patients >40yo, with frontal location, dorsal IFOF, frontal aslant tract and superior longitudinal fasciculus invasion, and seizures as onset symptom. IDH-wildtype astrocytomas (n=54) displayed: age >40yo, left-sided temporo-insular preferential location, invasion of posterior IFOF and Cortico-spinal tract, cognitive deficits at onset and the infiltration of posterior left peri-insular voxel (A3C2S3) as a strong predictor of IDH-wildtype final diagnosis. Using an integrated clinic-radiological approach we identified differences in age, clinical presentation, preferential location, and white matter infiltration among specific molecular subgroups of suspected DLGG. The systematic combination of patient-specific variables (age/clinical onset) and tumor-specific features (sub-lobar preferential location) may be relevant to create future prediction models and to better understand the onco-functional trajectory already at the preoperative stage. Prediction models may benefit from combining information rather than, for instance, analyzing images only.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".