Advanced quantitative MRI radiomics features for recurrence prediction in glioblastoma multiform patients
Bibliographic record
Abstract
Introduction: Advanced quantitative information such as radiomics features derived from magnetic resonance (MR) image may be useful for outcome prediction, prognostic models or response biomarkers in Glioblastoma (GBM). The main aim of this study was to evaluate MRI radiomics features for recurrence prediction in glioblastoma multiform. Materials and Methods: 86 patients with recurrent GBM who underwent MRI were subjected to this study. The axial T1-weighted contrast-enhanced and axial T2-weighted FLAIR images were included for analysis. All images were preprocessed by different bin width (32, 64 and 128). For each lesion we manually segmented Active, Necrosis and whole Tumor region in T1-CE and Edema region in T2-FLAIR. 105 quantitative 3D features and texture based on intensity histograms (IH), gray level run-length (GLRLM), gray level co-occurrence (GLCM), gray level size-zone texture matrices (GLSZM), neighborhood-difference matrices (NDM), and geometric features were extracted from the 3D-tumor volumes of each segment. Random Forest (RF) machine learning with 10-fold cross validation was used to recurrence prediction in GBM. Results: Area under ROC curve (AUC) as an assessment index on RF with bin width of 32, 64 and 128 achieved in Active (0.616, 0.586, 0.509), Necrosis (0.521, 0.521, 0.545), whole Tumor (0.639, 0.602, 0.547) and Edema regions (0.629, 0.669, 0.621), respectively. Conclusion: The main purpose of this assay was to assess the power of MRI radiomics features in GBM patients for recurrence prediction. The proposed method can effectively predict recurrence in GBM by application of advanced MRI quantitative radiomics features and machine learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".