Can strain elastography improve the characterization of breast lesions identified during second‐look MRI‐directed sonographic examination?
Bibliographic record
Abstract
PURPOSE: To evaluate strain elastography as a complementary tool for characterization of lesions identified during second-look MRI-directed sonographic examination. METHODS: We reviewed 83 breast lesions evaluated with MRI, secondlook ultrasound (US) and strain elastography in 75 consecutive patients (median age, 56 years). US-guided biopsies were performed in all cases. RESULTS: After histopathological examination, 44 lesions were benign, 38 were malignant and 1 was high-risk. At MRI, the mean size of the lesions was 12 mm. Forty lesions (48.2%) appeared as masses, 30 (36.1%) as "non-masses" and 13 (15.7%) as "foci." At second-look US examination, 56 (67.5%) appeared as masses (mean size, 7 mm) and 27 (32.5%) as non-masses (mean size, 14 mm). At strain elastography, among the 39 malignant/high risk lesions, 5 (12.8%) had a score of 4 or 5, whereas 16 (41%) had a score of 1 and 2 (false negative). Among the 44 benign lesions, 36 (82%) had a score of 1 or 2, whereas none had a score of 5. Sensitivity and specificity of strain elastography in the diagnosis of breast cancer were 58% and 81%, respectively. CONCLUSION: The addition of strain elastography offers no benefit in the characterization of lesions identified on second-look US after breast MRI.(E1, 3).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".