Chemodosimetry of in vivo tumor liposomal drug concentration using MRI
Bibliographic record
Abstract
Effective cancer chemotherapy depends on the delivery of therapeutic drugs to cancer cells at cytotoxic concentrations. However, physiologic barriers, such as variable vessel permeability, high interstitial fluid pressure, and heterogeneous perfusion, make it difficult to achieve that goal. Efforts to improve drug delivery have been limited by the lack of noninvasive tools to evaluate intratumoral drug concentration and distribution. Here we demonstrate that tumor drug concentration can be measured in vivo using T(1)-weighted MRI, following systemic administration of liposomes containing both drug (doxorubicin (DOX)) and contrast agent (manganese (Mn)). Mn and DOX concentrations were calculated using T(1) relaxation times and Mn:DOX loading ratios, as previously described. Two independent validations by high-performance liquid chromatography (HPLC) and histologic fluorescence in a rat fibrosarcoma (FSA) model indicate a concordant linear relationship between DOX concentrations determined using T(1) and those measured invasively. This method of imaging exhibits potential for real-time evaluation of chemotherapeutic protocols and prediction of tumor response on an individual patient basis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".