Bibliographic record
Abstract
Breast magnetic resonance imaging (MRI) has become an essential component of breast imaging. Whether it is used as a problem-solving tool or a screening test or for staging patients with breast cancer, it detects many lesions in the breast. The challenge for the radiologist is to distinguish significant from insignificant lesions and to direct their management. A brief summary of the terminology according to the American College of Radiologists lexicon will be provided. This review article will cover the differential diagnosis of enhancing lesions, including masses and nonmass enhancement, from benign and malignant causes. Some of the specific morphologic and kinetic features that help to differentiate benign from malignant lesions will be illustrated, and positive predictive values of these features will be reviewed. The various methods of investigating enhancing lesions of the breast will be discussed, including second-look ultrasound, ultrasound-guided biopsy, stereotactic biopsy, and MRI-guided biopsy. A practical approach to the management of MRI-detected lesions will include timing of follow-up, when to biopsy and when to ignore enhancing lesions in the breast.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".