The Potential for MR Enhancement to Predict Tumor Hypoxia Reduction with Manganese Dioxide Nanoparticles in MR-Guided Radiotherapy
Bibliographic record
Abstract
It is widely known that tumor hypoxia worsens radiotherapy (RT) outcomes across many solid cancer types. Previous research on manganese dioxide nanoparticles have shown promise in their concurrent MR enhancement and tumor hypoxia reduction abilities. I hypothesized that MR enhancement can predict the efficacy of tumor hypoxia reduction. In orthotopic ME-180 and patient-derived OCICx 34 cervical cancer tumors, manganese dioxide nanoparticle generated sustained and consistent tumor-specific MR enhancement but differed in their enhancement pattern. Although both tumor models showed some tumor hypoxia reduction, the overall response to T-MX was heterogenous. Furthermore, manganese dioxide nanoparticle efficacy in RT is likely driven by both oxygen-dependent and independent mechanisms, with tumor-specific factors such as perfusion, tumor-stromal architecture, and hypoxia contributing to differences in therapeutic response. Our work suggests that while MR enhancement does not reliably predict tumor hypoxia reduction efficacy, it provides insights into tumor characteristics that may support predictions of treatment response.
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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 | 0.000 | 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".