PREDICTORS OF TUMOR DYNAMICS DURING A COURSE OF 30-FRACTION RADIOTHERAPY FOR GLIOBLASTOMA
Bibliographic record
Abstract
Abstract We aim to identify predictors of tumor dynamics during radiotherapy for glioblastoma. METHODS: This is a prospective serial MRI study in patients with glioblastoma. MRIs were obtained at radiotherapy planning (F0), fraction-10 (F10), and fraction-20 (F20). The tumor dynamics metrics (relative to F0) assessed included: relative gross tumor volume (GTV) changes (Vrel) and migration distance (dM). RESULTS: 129 patients were included in this study. Median GTV was 20.9cc at F0, 17.6cc at F10 (Vrel 0.85), and 16.1cc at F20 (Vrel 0.78). Patients with no corpus-callosum involvement (CCI) (vs with CCI) had more marked GTV volume reduction: Vrel 0.82 vs 1.02 at F10 (P=0.05), and Vrel 0.77 vs 0.88 at F20 (P=0.03). Patients with gross total resection (GTR) had more marked GTV volume reduction (vs subtotal resection (STR) vs biopsy (Bx)): Vrel 0.78, 0.85 and 1.07 at F10 (P=0.001), and Vrel 0.69, 0.80, 1.04 at F20 (P=0.001). The median dM were 4.7mm at both F10 and F20. Patients with CCI had larger dM (vs no CCI): 41% vs 12% had dM>10mm at F10 (P=0.01), and 45% vs 9% had dM>10mm at F20 (P<0.001). At F20, 0%, 25% and 19% of patients with GTR, STR and Bx had dM>10mm (P=0.002). Age, sex, MGMT methylation and IDH-mutation status were not associated with tumor dynamics during treatment. CONCLUSION: CCI and extent of surgery predict for tumor dynamics, which may allow us to better select patients who benefit the most from re-planning/ treatment adaptation during radiotherapy.
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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".