Detectability of radiation-induced changes in magnetic resonance biomarkers following stereotactic radiosurgery: A pilot study
Bibliographic record
Abstract
Our objective was to investigate direct voxel-wise relationship between dose and early MR biomarker changes both within and in the high-dose region surrounding brain metastases following stereotactic radiosurgery (SRS). Specifically, we examined the apparent diffusion coefficient (ADC) from diffusion-weighted imaging and the contrast transfer coefficient (Ktrans) and volume of extracellular extravascular space (ve) derived from dynamic contrast-enhanced (DCE) MRI data. We investigated 29 brain metastases in 18 patients using 3 T MRI to collect imaging data at day 0, day 3 and day 20 following SRS. The ADC maps were generated by the scanner and Ktrans and ve maps were generated using in-house software for dynamic tracer-kinetic analysis. To enable spatially-correlated voxel-wise analysis, we developed a registration pipeline to register all ADC, Ktrans and ve maps to the planning MRI scan. To interrogate longitudinal changes, we computed absolute ΔADC, ΔKtrans and Δve for day 3 and 20 post-SRS relative to day 0. We performed a Kruskall-Wallice test on each biomarker between time points and investigated dose correlations within the gross tumour volume (GTV) and surrounding high dose region > 12 Gy via Spearman's rho. Only ve exhibited significant differences between day 0 and 20 (p < 0.005) and day 3 and 20 (p < 0.05) within the GTV following SRS. Strongest dose correlations were observed for ADC within the GTV (rho = 0.17 to 0.20) and weak correlations were observed for ADC and Ktrans in the surrounding > 12 Gy region. Both ΔKtrans and Δve showed a trend with dose at day 20 within the GTV and > 12 Gy region (rho = -0.04 to -0.16). Weak dose-related decreases in Ktrans and ve within the GTV and high dose region at day 20 most likely reflect underlying vascular responses to radiation. Our study also provides a voxel-wise analysis schema for future MR biomarker studies with the goal of elucidating surrogates for radionecrosis.
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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 | 0.001 | 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".