Sonographic Appearance of Invasive Ductal Carcinoma of the Breast According to Histologic Grade
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare the efficacy of the sonographic features in the BI-RADS lexicon for predicting malignancy grade of invasive ductal breast carcinoma in women assigned a BI-RADS category of 4 or 5. MATERIALS AND METHODS: Two radiologists retrospectively evaluated 299 consecutive cases of grades 1-3 invasive ductal breast carcinoma presenting as a mass in consensus by using the BI-RADS sonographic lexicon. Histologic grade was established on surgical specimens. Effect sizes were calculated via the Goodman and Kruskal tau, an asymmetric measure of strength of nominal association, and results were interpreted in terms of proportional reduction in error. RESULTS: Thirty-eight lesions (13%) were grade 1, 153 (51%) were grade 2, and 108 (36%) were grade 3, with the majority of all masses showing an irregular shape (84%) and hypoechoic echotexture (82%). Of the sonographic features examined, malignancy grade was best predicted by posterior acoustics (τ = 0.13, p < 0.001), lesion boundary (τ = 0.05, p < 0.001), and margin (τ = 0.04, p = 0.001). Among grade 3 lesions, there were significantly more lesions with posterior enhancement (53 vs 27.6; adjusted standardized residuals (z(res)) = 7; p < 0.001), abrupt interfaces (68 vs 51.2; z(res) = 4; p < 0.001), and microlobulated margins (12 vs 5.8; z(res) = 3; p = 0.001) than would be expected. CONCLUSION: Malignancy grade was slightly to moderately predicted by margin, lesion boundary, and acoustic sonographic features. In particular, grade 3 invasive ductal breast carcinomas were more likely than expected to display microlobulated margins, abrupt interfaces, and posterior enhancement.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 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".