MRI features predict survival and molecular markers in diffuse lower-grade gliomas
Bibliographic record
Abstract
Background: Previous studies have shown that MR imaging features can be used to predict survival and molecular profile of glioblastoma. However, no study of a similar type has been performed on lower-grade gliomas (LGGs). Methods: Presurgical MRIs of 165 patients with diffuse low- and intermediate-grade gliomas (histological grades II and III) were scored according to the Visually Accessible Rembrandt Images (VASARI) annotations. Radiomic models using automated texture analysis and VASARI features were built to predict isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q codeletion status, histological grade, and tumor progression. Results: Interrater analysis showed significant agreement in all imaging features scored (k = 0.703-1.000). On multivariate Cox regression analysis, no enhancement and a smooth non-enhancing margin were associated with longer progression-free survival (PFS), while a smooth non-enhancing margin was associated with longer overall survival (OS) after taking into account age, grade, tumor location, histology, extent of resection, and IDH1 1p/19q subtype. Using logistic regression and bootstrap testing evaluations, texture models were found to possess higher prediction potential for IDH1 mutation, 1p/19q codeletion status, histological grade, and progression of LGGs than VASARI features, with areas under the receiver-operating characteristic curves of 0.86 ± 0.01, 0.96 ± 0.01, 0.86 ± 0.01, and 0.80 ± 0.01, respectively. Conclusion: No enhancement and a smooth non-enhancing margin on MRI were predictive of longer PFS, while a smooth non-enhancing margin was a significant predictor of longer OS in LGGs. Textural analyses of MR imaging data predicted IDH1 mutation, 1p/19q codeletion, histological grade, and tumor progression with high accuracy.
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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 | 0.001 | 0.002 |
| 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".