Contrast-Enhanced Mammography for Detection and Characterization of Invasive Lobular Carcinoma
Bibliographic record
Abstract
Invasive lobular carcinoma (ILC) poses distinct diagnostic challenges due to its infiltrative single-file growth pattern, which often renders it mammographically occult, particularly in dense breast tissue. Contrast-enhanced mammography (CEM) combines the anatomical detail of conventional mammography with functional information from contrast uptake, likely improving the detection, staging, and assessment of ILC compared to conventional imaging techniques. CEM shows value in evaluating ILC tumor size and disease extent, especially in multifocal and multicentric disease, although MRI remains the gold standard. This review outlines the spectrum of ILC imaging features on CEM, including findings on both low-energy and recombined images. While CEM can provide ILC size and extent estimates comparable to MRI, its accuracy may be reduced in cases of non-mass enhancement or tumors larger than 3 cm. Additionally, ILC may demonstrate lower conspicuity enhancement than invasive ductal carcinoma (IDC), necessitating careful image interpretation. As clinical adoption of CEM increases, radiologists must become familiar with the variable imaging characteristics of ILC, to facilitate more accurate interpretation. Improved recognition of these features has the potential to support more precise treatment planning and better patient outcomes.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".