Identification of breast calcification using magnetic resonance imaging
Bibliographic record
Abstract
MRI phase and magnitude images provide information about local magnetic field variation (DeltaB0), which can consequently be used to understand tissue properties. Often, phase information is discarded. However, corrected phase images are able to produce contrast as a result of magnetic susceptibility differences and local field inhomogeneities due to the presence of diamagnetic and paramagnetic substances. Three-dimensional (3D) susceptibility weighted imaging (SWI) can be used to probe changes in MRI phase evolution and, subsequently, result in an alternate form of contrast between tissues. For example, SWI has been useful in the assessment of negative phase induced DeltaB0 modulation due to the presence of paramagnetic substances such as iron. Very little, however, has been done to assess positive phase induced contrast changes resulting from the presence of diamagnetic substances such as precipitated calcium. As ductal carcinoma in situ, which is the precursor of invasive ductal cancer, is often associated with breast microcalcification, the authors proposed using SWI as a possible visualization technique. In this study, breast phantoms containing calcifications (0.4-1.5 mm) were imaged using mammography, computed tomography (CT), and SWI. Corrected phase and magnitude images acquired using SWI allowed identification and correlation of all calcifications seen on CT. As the approach is a 3D technique, it could potentially allow for more accurate localization and biopsy and maybe even reduce the use of gadolinium contrast. Furthermore, the approach may be beneficial to women with dense breast tissue where the ability to detect microcalcification with mammography is reduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".