Characterization of Breast Cancer with Manganese-enhanced Magnetic Resonance Imaging
Bibliographic record
Abstract
Highly metastatic cancer cells are more likely to escape and form metastases,\nand only minimal improvements in treatment can be achieved. Despite metas-\ntases being the primary cause of cancer-related mortality, they often proceed\nunnoticed. Current imaging modalities rely solely on the morphological fea-\ntures of the tumor for characterization, rather than cellular differences. Our\ngoal is to develop an MR cellular imaging capability for characterizing the po-\ntential of breast cancer cells to metastasize and enable early cancer detection\nusing manganese. Experiments on breast cell lines demonstrated that aggres-\nsive cancer cells significantly enhanced on T1 -weighted MR images as a result\nof a higher uptake and retention of manganese. These results suggest that dif-\nferences in uptake of manganese can help the detection and characterization\nof breast cancers. The proposed technique can also be useful for other cancers,\nand could bring a critically needed dimension to cancer imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".