Evaluating the Impact of Breast Density on Preoperative MRI in Invasive Lobular Carcinoma
Bibliographic record
Abstract
In Brief BACKGROUND: The focus of this study was to assess the accuracy of breast MRI in predicting pathologic tumor size in invasive lobular carcinoma (ILC) and to evaluate the incidence and factors associated with the detection of additional MRI lesions in ILC patients. STUDY DESIGN: We retrospectively reviewed data from patients with stage I to III ILC diagnosed between 2010 and 2016 at our institution. Univariable and multivariable logistic regression were used to determine factors associated with detection of additional suspicious lesions on MRI. RESULTS: The cohort included 99 women with ILC who underwent preoperative MRI, with a median age of 61 years (range 35 to 80 years). The sensitivity of MRI for detecting invasive lobular carcinoma was 99%, higher than that of mammography (68%) and ultrasound (92%). Mammography and ultrasound had a tendency to underestimate ILC, and MRI estimates of final tumor size were concordant in the majority (58.6%) of cases, with a median discordance of −2 mm. Magnetic resonance imaging detected additional ipsilateral malignancy in 23.2%, occult contralateral disease in 3.0%, and altered surgical management in 29.3% of ILC cases. In multivariable analyses, factors significantly associated with additional suspicious findings on MRI included higher breast density (odds ratio 3.19; 95% CI 1.01 to 10.0) and lymph node-positive disease (odds ratio 4.02; 95% CI 0.96 to 16.9). CONCLUSIONS: Preoperative MRI is a useful adjunct to conventional breast imaging in ILC, particularly in women with dense breast tissue. In this retrospective review of 99 women with invasive lobular carcinoma, breast MRI detected additional ipsilateral malignancy in 23.2% of patients and altered surgical management in 29.3% of invasive lobular carcinoma cases. Factors associated with MRI detection of new suspicious lesions included high breast density and lymph node-positive disease.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".