Noninvasive Manganese-Enhanced Magnetic Resonance Imaging for Early Detection of Breast Cancer Metastatic Potential
Bibliographic record
Abstract
Cancer cells with a high metastatic potential will more likely escape and form distant tumors. Once the cancer has spread, a cure is rarely possible. Unfortunately, metastasis often proceeds unnoticed until a secondary tumor has formed. The culprit is that current imaging-based cancer screening and diagnosis are limited to assessing gross physical changes, not the earliest cellular changes that drive cancer progression. The purpose of this study is to develop a novel noninvasive magnetic resonance (MR) cellular imaging capability for characterizing the metastatic potential of breast cancer and enable early cancer detection. This MR method relies on imaging cell uptake of manganese, an endogenous calcium analogue and an MR contrast agent, to detect aggressive cancer cells. Studies on normal breast epithelial cells and three breast cancer cell lines, from nonmetastatic to highly metastatic, demonstrated that aggressive cancer cells appeared significantly brighter on MR as a result of altered cell uptake of manganese. In vivo results in nude rats showed that aggressive tumors that are otherwise unseen on conventional gadolinium-enhanced MR imaging are detected after manganese injection. This cellular MR imaging technology brings a critically needed, unique dimension to cancer imaging by enabling us to identify and characterize metastatic cancer cells at their earliest appearance.
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 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 | 0.001 |
| 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 | 0.000 | 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 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".