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 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".