In-Vivo Retention of 5-Fluorouracil Using 19F Magnetic Resonance Chemical Shift Imaging in Colorectal Cancer in a Murine Model
Bibliographic record
Abstract
Colorectal cancer is the third leading cause of cancer death worldwide. 5-Fluorouracil (5-FU) is one of the most commonly used chemotherapies for treatment of solid tumours, including colorectal cancer. The efficacy of treatment is dependent on tumour type and can only be determined six weeks after beginning chemotherapy, with only 40-50% of patients responding positively to the 5-FU therapy. In this paper, we demonstrate the potential of using Magnetic Resonance (MR) Chemical Shift Imaging (CSI) for in-vivo monitoring of 5-FU tumor-retention in two different colorectal tumour types (HT-29 & H-508). Time curves for 5-FU signals from the liver and bladder were also acquired. We observed significant differences (p < 0.01) in 5-FU signal time dependencies for the HT-29 and H-508 tumours. Retention of 5-FU occurred in the H-508 tumour, whereas the HT-29 tumour is not expected to retain 5FU due to the observation of the negative b time constant indicating a decline in 5FU within the tumour. This study successfully demonstrates that CSI may be a useful tool for early identification of 5-FU responsive tumours based on observed tumour retention of the 5-FU.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".