Imaging chemical exchange saturation transfer (CEST) effects following tumor‐selective acidification using lonidamine
Bibliographic record
Abstract
Increased lactate production through glycolysis in aerobic conditions is a hallmark of cancer. Some anticancer drugs have been designed to exploit elevated glycolysis in cancer cells. For example, lonidamine (LND) inhibits lactate transport, leading to intracellular acidification in cancer cells. Chemical exchange saturation transfer (CEST) is a novel MRI contrast mechanism that is dependent on intracellular pH. Amine and amide concentration-independent detection (AACID) and apparent amide proton transfer (APT*) represent two recently developed CEST contrast parameters that are sensitive to pH. The goal of this study was to compare the sensitivity of AACID and APT* for the detection of tumor-selective acidification after LND injection. Using a 9.4-T MRI scanner, CEST data were acquired in mice approximately 14 days after the implantation of 10(5) U87 human glioblastoma multiforme (GBM) cells in the brain, before and after the administration of LND (dose, 50 or 100 mg/kg). Significant dose-dependent LND-induced changes in the measured CEST parameters were detected in brain regions spatially correlated with implanted tumors. Importantly, no changes were observed in T1- and T2-weighted images acquired before and after LND treatment. The AACID and APT* contrast measured before and after LND injection exhibited similar pH sensitivity. Interestingly, LND-induced contrast maps showed increased heterogeneity compared with pre-injection CEST maps. These results demonstrate that CEST contrast changes after the administration of LND could help to localize brain cancer and monitor tumor response to chemotherapy within 1 h of treatment. The LND CEST experiment uses an anticancer drug to induce a metabolic change detectable by endogenous MRI contrast, and therefore represents a unique cancer detection paradigm which differs from other current molecular imaging techniques that require the injection of an imaging contrast agent or tracer.
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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 | 0.001 |
| 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 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".