Quantitative Magnetization Transfer in Monitoring Glioblastoma (GBM) Response to Therapy
Bibliographic record
Abstract
Abstract Quantitative magnetization transfer (qMT) was used as a biomarker to monitor glioblastoma (GBM) response to chemo-radiation and identify the earliest time-point qMT could differentiate progressors from non-progressors. Nineteen GBM patients were recruited and MRI-scanned before (Day 0 ), two weeks (Day 14 ), and four weeks (Day 28 ) into the treatment, and one month after the end of the treatment (Day 70 ). Comprehensive qMT data was acquired, and a two-pool MT model was fit to the data. Response was determined at 3–8 months following the end of chemo-radiation. The amount of magnetization transfer ( $${\bf{R}}{{\bf{M}}}_{{\bf{0b}}}/{{\bf{R}}}_{{\bf{a}}}$$ R M 0b / R a ) was significantly lower in GBM compared to normal appearing white matter (p < 0.001). Statistically significant difference was observed in $${\bf{R}}{{\bf{M}}}_{{\bf{0b}}}/{{\bf{R}}}_{{\bf{a}}}$$ R M 0b / R a at Day 0 between non-progressors (1.06 ± 0.24) and progressors (1.64 ± 0.48), with p = 0.006. Changes in several qMT parameters between Day 14 and Day 0 were able to differentiate the two cohorts with $${\bf{R}}{{\bf{M}}}_{{\bf{0b}}}/{{\bf{R}}}_{{\bf{a}}}$$ R M 0b / R a providing the best separation (relative $${\bf{R}}{{\bf{M}}}_{{\bf{0b}}}/{{\bf{R}}}_{{\bf{a}},{\bf{Non}}-{\bf{progressor}}}$$ R M 0b / R a , Non − progressor = 1.34 ± 0.21, relative $${\bf{R}}{{\bf{M}}}_{{\bf{0b}}}/{{\bf{R}}}_{{\bf{a}},{\bf{progressor}}}$$ R M 0b / R a , progressor = 1.07 ± 0.08, p = 0.031). Thus, qMT characteristics of GBM are more sensitive to treatment effects compared to clinically used metrics. qMT could assess tumor aggressiveness and identify early progressors even before the treatment. Changes in qMT parameters within the first 14 days of the treatment were capable of separating early progressors from non-progressors, making qMT a promising biomarker to guide adaptive radiotherapy for GBM.
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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 | 0.001 |
| 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) | 0.001 | 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".