Monitoring Early Changes in Tumor Metabolism in Response to Therapy Using Hyperpolarized 13C MRSI in a Preclinical Model of Glioma
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Bibliographic record
Abstract
This study shows the use of hyperpolarized 13C magnetic resonance spectroscopic imaging (MRSI) to assess therapeutic efficacy in a preclinical tumor model. 13C-labeled pyruvate was used to monitor early changes in tumor metabolism based on the Warburg effect. High-grade malignant tumors exhibit increased glycolytic activity and lactate production to promote proliferation. A rodent glioma model was used to explore altered lactate production after therapy as an early imaging biomarker for therapeutic response. Rodents were surgically implanted with C6 glioma cells and separated into 4 groups, namely, no therapy, radiotherapy, chemotherapy and combined therapy. Animals were imaged serially at 6 different time points with magnetic resonance imaging at 3 T using hyperpolarized [1-13C]pyruvate MRSI and conventional 1H imaging. Using hyperpolarized [1-13C]pyruvate MRSI, alterations in tumor metabolism were detected as changes in the conversion of lactate to pyruvate (measured as Lac/Pyr ratio) and compared with the conventional method of detecting therapeutic response using the Response Evaluation Criteria in Solid Tumors. Moreover, each therapy group expressed different characteristic changes in tumor metabolism. The group that received no therapy showed a gradual increase of Lac/Pyr ratio within the tumor. The radiotherapy group showed large variations in tumor Lac/Pyr ratio. The chemo- and combined-therapy groups showed a statistically significant reduction in tumor Lac/Pyr ratio; however, only combined therapy was capable of suppressing tumor growth, which resulted in low endpoint mortality rate. Hyperpolarized 13C MRSI detected a prompt reduction in Lac/Pyr ratio as early as 2 days post combined chemo- and radiotherapies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it