Breast Imaging Reporting and Data System Lexicon for US: Interobserver Agreement for Assessment of Breast Masses
Bibliographic record
Abstract
PURPOSE: To retrospectively evaluate the interobserver agreement of radiologists who used the Breast Imaging Reporting and Data System (BI-RADS) lexicon to characterize and categorize ultrasonographic (US) features of breast masses. MATERIALS AND METHODS: No institutional review board approval or patient consent was required. Five breast radiologists retrospectively independently evaluated 267 breast masses (113 benign and 154 malignant masses in 267 patients) by using the BI-RADS US lexicon. Reviewers were blinded to mammographic images, medical history, and pathologic findings. Interobserver agreement was assessed with the Aickin revised kappa statistic. RESULTS: Interobserver agreement varied from fair for evaluation of mass margins (kappa = 0.36) to moderate for evaluation of lesion boundary (kappa = 0.48), echo pattern (kappa = 0.58), and posterior acoustic features (kappa = 0.47) to substantial for evaluation of mass orientation (kappa = 0.70) and shape (kappa = 0.64). For small (< or =0.7 cm; n = 49) or malignant (n = 154) masses, low concordance was noted for margin descriptors (kappa = 0.30 and 0.28, respectively) and BI-RADS category (kappa = 0.21 and 0.26, respectively). Overall, only fair agreement was obtained for BI-RADS category (kappa = 0.30). Agreement for subdivisions 4a, 4b, and 4c of BI-RADS category 4 was fair (kappa = 0.33), fair (kappa = 0.32), and poor (kappa = 0.17), respectively. CONCLUSION: Reproducibility of US BI-RADS terminology is good except for margin evaluation. A trend toward lower concordance was noted for the evaluation of small masses and malignant lesions. Classification into subdivisions 4a, 4b, and 4c was poorly reproducible.
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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 | 0.031 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".