T2 Hyperintense Lesions on Breast MRI – Is the Assumption of Benignity Justified?
Bibliographic record
Abstract
Introduction: This study aims to evaluate the outcomes of breast MRI-guided vacuum assisted biopsies (MVAB) performed on lesions with high T2 signal. Materials and Methods: We retrospectively collected of all MVAB performed at our institution between January 2016 and December 2021 for high T2 lesions. T2 hyperintensity was defined as equal or higher signal than normal lymph node. The correlation between various demographic and imaging characteristics and the binary pathological outcome (benign vs malignant) was evaluated. Results: In total, 174 biopsies from 165 women met the inclusion criteria and were included in the cohort. Malignancy was detected in 35 lesions (20%), most commonly ductal carcinoma in situ (DCIS, 57%), followed by infiltrating ductal carcinoma (IDC, 40%). The most common benign diagnosis was fibrocystic changes (FCC, 38%). In 19 lesions MVAB detected high-risk pathology, 3 of which were upgraded to malignancy. Older age (Mean 61 vs 54 years, P = .04), washout kinetics (29% vs 13%, P = .01), and indication for extent of disease evaluation (53% vs 32%, P = .06) were the strongest predictors of malignancy. Lesion size and morphology were not significantly associated with outcome. Conclusions: Given the considerable cancer rate, T2 hyperintensity should be used with caution as a benign indicator and not as a sole criterion for ruling out malignancy. Additional factors such as patient age, kinetic features, and MRI indication should be considered to improve diagnostic accuracy.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".