Temporal Apparent Diffusion Coefficient Changes During Chemoradiation: An Imaging Biomarker for Tumor Response Monitoring and Spatial Recurrence Prediction in Glioblastoma
Bibliographic record
Abstract
PURPOSE: Apparent diffusion coefficient (ADC) from diffusion-weighted imaging has been shown to detect early treatment response in glioblastoma. This prospective observational serial imaging study aimed to compare ADC changes in gross tumor volume (GTV) regions that developed recurrence versus those that remained recurrence-free. METHODS AND MATERIALS: Patients with glioblastoma underwent diffusion-weighted imaging at radiation planning (baseline, fraction 0), fraction 10, fraction 20, and 1 month after completing a 6-week course of chemoradiation. Recurrence was contoured at the earliest magnetic resonance imaging showing progression. The intersection of the GTV and recurrence was labeled resistant-GTV, whereas nonintersecting GTV was labeled sensitive-GTV. ADC values and percentage changes from fraction 0 were compared between these regions. RESULTS: /ms; IQR, 0.87-1.13) were similar at baseline (P = .193), but statistically significant differences were observed from the start of radiation therapy. Median ADC changes from baseline for resistant- and sensitive-GTV were +2.5% versus +15.1% at fraction 10 (P < .001), +8.1% versus +23.1% at fraction 20 (P < .001), and +21.2% versus +36.4% at 1 month after completing a 6-week course of chemoradiation (P <.001), respectively. Smaller ADC changes at fraction 10 (odds ratio, 0.95; P = .005) and fraction 20 (odds ratio, 0.95; P = .010) were independent predictors of increased risk of GTV failure, adjusting for O6-methylguanine DNA methyltransferase promoter methylation and extent of surgical resection. CONCLUSIONS: Temporal ADC changes are promising imaging biomarkers for treatment response and spatial recurrence prediction and may provide a target for magnetic resonance imaging-guided biologically adapted radiation clinical trials.
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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 | 0.002 |
| 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) | 0.000 | 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 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".