A multimodality imaging model to track viable breast cancer cells from single arrest to metastasis in the mouse brain
Bibliographic record
Abstract
Cellular MRI involves sensitive visualization of iron-labeled cells in vivo but cannot differentiate between dead and viable cells. Bioluminescence imaging (BLI) measures cellular viability, and thus we explored combining these tools to provide a more holistic view of metastatic cancer cell fate in mice. Human breast carcinoma cells stably expressing Firefly luciferase were loaded with iron particles, injected into the left ventricle, and BLI and MRI were performed on days 0, 8, 21 and 28. The number of brain MR signal voids (i.e., iron-loaded cells) on day 0 significantly correlated with BLI signal. Both BLI and MRI signals decreased from day 0 to day 8, indicating a loss of viable cells rather than a loss of iron label. Total brain MR tumour volume on day 28 also correlated with BLI signal. Overall, BLI complemented our sensitive cellular MRI technologies well, allowing us for the first time to screen animals for successful injections, and, in addition to MR measures of cell arrest and tumor burden, provided longitudinal measures of cancer cell viability in individual animals. We predict this novel multimodality molecular imaging framework will be useful for evaluating the efficacy of emerging anti-cancer drugs at different stages of the metastatic cascade.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".