MRI biomarkers identify the differential response of glioblastoma multiforme to anti-angiogenic therapy
Bibliographic record
Abstract
BACKGROUND: Although anti-angiogenic therapy (AATx) holds great promise for treatment of malignant gliomas, its therapeutic efficacy is not well understood and can potentially increase the aggressive recurrence of gliomas. It is essential to establish sensitive, noninvasive biomarkers that can detect failure of AATx and tumor recurrence early so that timely adaptive therapy can be instituted. We investigated the efficacy of MRI biomarkers that can detect response to different classes of AATxs used alone or in combination with radiation. METHODS: Murine intracranial glioma xenografts (NOD/SCID) were treated with sunitinib, VEGF-trap or B20 (a bevacizumab equivalent) alone or in combination with radiation. MRI images were acquired longitudinally before and after treatment, and various MRI parameters (apparent diffusion coefficient, T1w + contrast, dynamic contrast-enhanced [DCE], initial area under the contrast enhancement curve, and cerebral blood flow) were correlated to tumor cell proliferation, overall tumor growth, and tumor vascularity. RESULTS: Combinatorial therapies reduced tumor growth rate more efficiently than monotherapies. Apparent diffusion coefficient was an accurate measure of tumor cell density. Vascular endothelial growth factor (VEGF)-trap or B20, but not sunitinib, resulted in significant reduction or complete loss of contrast enhancement. This reduction was not due to a reduction in tumor growth or microvascular density, but rather was explained by a reduction in vessel permeability and perfusion. We established that contrast enhancement does not accurately reflect tumor volume or vascular density; however, DCE-derived parameters can be used as efficient noninvasive biomarkers of response to AATx. CONCLUSIONS: MRI parameters following therapy vary based on class of AATx. Validation of clinically relevant MRI parameters for individual AATx agents is necessary before incorporation into routine practice.
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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) | 0.001 | 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".