Analysis of IDH mutation, 1p/19q deletion, and PTEN loss delineates prognosis in clinical low-grade diffuse gliomas
Bibliographic record
Abstract
BACKGROUND: Grades II and III gliomas have unpredictable rates of progression, making management decisions difficult. Currently, several clinical and radiological characteristics are utilized to predict progression and survival but collectively are suboptimal. METHODS: In this study, we analyzed a set of 108 nonenhancing hemispheric grade II-III gliomas. Demographic variables, including patient age, tumor diameter, extent of resection, and performance status, were combined with molecular data (IDH mutation status [mIDH], 1p/19q codeletion, PTEN deletion, and EGFR amplification). A complete dataset for all variables was compiled for 70 of the 108 patients. Both univariable and multivariable analyses were performed to determine whether the molecular data singly or in combination offer advantages over tumor type and grade for prediction of overall survival (OS) and/or progression-free rate (PFR). RESULTS: Patient age, clinical variables (tumor diameter, extent of resection, performance status), and pathology (tumor type and grade) were not predictive of OS or PFR. IDH mutation status alone was predictive of longer OS and PFR for the entire group of tumors; 1p/19q deletion alone was predictive of OS but not PFR. In the multivariable analysis, none of the clinical or demographic factors were predictive of OS or PFR. IDH mutation status, 1p/19q codeletion, and PTEN deletion were predictive of OS (P = .003, P = .005, P = .02, respectively). Both mIDH (P < .001) and the interaction term of 1p/19q and PTEN (P < .001) were found to be predictive of PFR. CONCLUSIONS: We conclude that the combination of mIDH, 1p/19q codeletion, and PTEN deletion may be particularly effective in discriminating good prognosis from poor prognosis hemispheric gliomas. We propose that such a scheme merits testing on larger prospective cohorts. Should our findings be confirmed, routine clinical analysis of hemispheric gliomas for mIDH, 1p/19q codeletion, and PTEN deletion would be justified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".