Manganese-Enhanced MRI for Early Detection of Breast Cancer Lung Metastases
Bibliographic record
Abstract
Breast cancer is the leading cause of cancer-related mortality in women, with early detection of metastases being crucial for improving patient outcomes. Magnetic Resonance Imaging (MRI) is a valuable diagnostic tool, with Gadolinium-enhanced MRI (Gd-MRI) serving as the clinical standard. However, its reliance on vasculature limits its effectiveness for detecting avascular micrometastases. This thesis investigates Manganese-enhanced MRI (Mn-MRI) as an alternative to Gadolinium-enhanced MRI (Gd-MRI) for metastasis imaging, focusing on the role of differential ferritin expression in modulating Mn-MRI contrast, and comparing Mn-MRI and Gd-MRI for early detection of lung metastases in a preclinical breast cancer model.In vitro, we demonstrate that ferritin expression modulates Mn uptake and Mn-MRI contrast. In vivo, Mn-MRI provides uniform tumor enhancement, while Gd-MRI highlights tumor peripheries due to vascular dependence. However, in lung metastases, Gd-MRI detected micrometastases as early as Mn-MRI, likely due to pre-existing lung vasculature. Immunohistochemical analysis found extensive vasculature in tumor peripheries, as well as reduced Ki-67 and ferritin staining in metastases relative to primary tumors. These findings highlight a critical trade-off between Mn-MRI and Gd-MRI: while Mn-MRI offers vascular-independent contrast for early metastatic detection, its sensitivity is modulated by ferritin expression and metabolic activity. Conversely, Gd-MRI is effective in detecting metastases in highly vascularized environments but may fail in avascular regions. This study refines our understanding of Mn-MRI and Gd-MRI’s context-specific advantages and informs future applications in early cancer detection.
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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) | 0.000 | 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) | 0.002 | 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".