MRI surveillance of cancer cell fate in a brain metastasis model after early radiotherapy
Bibliographic record
Abstract
PURPOSE: Incidence of brain metastasis attributed to breast cancer is increasing and prognosis is poor. It is thought that disseminated dormant cancer cells persist in metastatic organs and may evade treatments, thereby facilitating a mechanism for recurrence. Radiotherapy is used to treat brain metastases clinically, but assessment has been limited to macroscopic tumor volumes detectable by clinical imaging. Here, we use cellular MRI to understand the concurrent responses of metastases and nonproliferative or slowly cycling cancer cells to radiotherapy. METHODS: MRI cell tracking was used to investigate the impact of early cranial irradiation on the fate of individual iron-labeled cancer cells and outgrowth of breast cancer brain metastases in the human MDA-MB-231-BR-HER2 cell model. RESULTS: Early whole-brain radiotherapy significantly reduced the outgrowth of metastases from individual disseminated cancer cells in treated animals compared to controls. However, the numbers of nonproliferative iron-retaining cancer cells in the brain were not significantly different. CONCLUSIONS: Radiotherapy, when given early in cancer progression, is effective in preventing the outgrowth of solitary cancer cells to brain metastases. Future studies of the nonproliferative cancer cells' clonogenic potentials are warranted, given that their persistent presence suggests that they may have evaded treatment. Magn Reson Med 78:1506-1512, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".