Management of MRI-Detected Benign Internal Mammary Lymph Nodes
Bibliographic record
Abstract
Abstract Introduction In this retrospective study, we aimed to evaluate benign internal mammary lymph nodes (IMLNs) in terms of frequency, number, size, long axis/short axis (L/S) ratio, intercostal location, presence of fatty hilum, and stability using breast magnetic resonance imaging (MRI) and discuss the findings by reviewing existing literature. Methods This single-center study consisted of 130 women between the ages of 24 and 76 years, who had at least two breast MRI examinations in our institution, with the latest exam performed between January 1, 2019 and November 1, 2019, were eligible for the study. MRIs of the study group were independently reviewed by two radiologists. Results IMLN was detected in 31.1% of the 427 MRIs, with a total number of 256 nodes. The most common indication for breast MRI was high-risk screening (66.2%). The median number of nodes per patient was 1 (range: 1–6). The median follow-up time was 19.5 months (range: 6–141 months). None of these benign nodes showed significant interval growth. Mean L/R ratio of the nodes was 1.9. One hundred and four nodes (n = 104, 40.6%) had a L/S ratio less than 2 and 43.2% (n = 45) of the nodes with a L/S ratio less than 2, had a long axis measuring less than or equal to 3mm. IMLN of patients with breast implants had the largest mean long axis. The fatty hilum was identified in 34.3% (n = 68) of the 256 nodes. The size of the lymph nodes where fatty hilum was visualized was significantly larger than the ones where fatty hilum was not visualized (p < 0.001). Fatty hilum could be visualized in only 2.7% of the nodes with a long axis smaller than 3 mm. Conclusion IMLN is a frequent finding on breast MRI. We have shown that benign IMLNs might be large sized in specific cases like patients with breast implants. When small sized (≤3mm), they are more likely to be rounded (L/S ratio <2). The fatty hilum that is a feature of benignity might not be visualized in nodes less than or equal to3mm.
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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 | 0.000 | 0.002 |
| 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".