Nonmasslike Enhancement at Breast MR Imaging: The Added Value of Mammography and US for Lesion Categorization
Bibliographic record
Abstract
PURPOSE: To determine the value of adding conventional imaging (mammography and ultrasonography [US]) to nonmasslike enhancement (NMLE) analysis with breast magnetic resonance (MR) imaging for predicting malignancy and for building an interpretation model incorporating all imaging modalities. MATERIALS AND METHODS: The institutional ethics committees approved the study and granted a waiver of informed consent. In 115 women (mean age, 48.3 years; range, 21-76 years; 56 malignant, 12 high-risk, and 63 benign lesions), 131 NMLE lesions were analyzed. Two independent readers first classified MR images by using descriptive Breast Imaging Reporting and Data System (BI-RADS) criteria (BI-RADS classification with MR images alone [BI-RADS(MR)]) and later repeated this classification, adding information from conventional imaging (BI-RADS classification with combination of MR images and conventional images [BI-RADS(MR+Con)]). Lesion diagnosis was established with surgical histopathologic findings (n = 68), percutaneous biopsy results (n = 25), or 2 years of stability at MR imaging (n = 38). Receiver operating characteristic curves were built to compare BI-RADS(MR) with BI-RADS(MR+Con). A multivariate interpretation model was constructed and validated in a distinct cohort of 44 women. RESULTS: Values for inter- and intraobserver agreement, respectively, were better for BI-RADS(MR+Con) (κ = 0.847 and 0.937) than for BI-RADS(MR) (κ = 0.748 and 0.861). For both readers, the areas under the receiver operating characteristic curve (AUCs) for diagnosis of malignancy were also superior when BI-RADS(MR+Con) (AUC = 0.91 [reader 1] and 0.93 [reader 2]) was compared with BI-RADS(MR) (AUC = 0.84 [reader 1] and 0.87 [reader 2]) (P < .05). An interpretation model combining conventional imaging with MR imaging criteria showed very good discrimination (AUC = 0.89 [training set] and 0.90 [validating set]). CONCLUSION: Adding conventional imaging to NMLE lesion characterization at breast MR imaging improved the diagnostic performance of radiologists, and the interpretation model used offers good accuracy with the potential to optimize the reproducibility of NMLE analysis at MR imaging.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".