Value of pre-operative breast MRI for the size assessment of ductal carcinoma<i>in situ</i>
Bibliographic record
Abstract
OBJECTIVE: To retrospectively evaluate the accuracy of pre-operative breast MRI and mammography in determining the size of ductal carcinoma in situ (DCIS) compared with the histopathological results. METHODS: 79 patients [mean age: 56.5 (standard deviation 10.2) years] with pathologically proven DCIS (79 lesions) obtained a bilateral mammogram and a pre-operative contrast-enhanced MRI. The accuracy of MRI and mammography to detect tumour size were estimated and compared, using histopathological size as the gold standard, on the subjects with measurements with both modalities (n = 60). RESULTS: MRI detected 67 (85%) lesions, mammography detected 72 (91%) and both modalities detected 60 (76%). Median DCIS size detected by mammography vs MRI was smaller (1.55 vs 1.65 cm). Out of these 60 cases, compared with the histopathological size, the accuracy of MRI and mammography was 0.66 and 0.56, respectively (p = 0.045). MRI showed better accuracy than mammography for younger patients (age ≤ 50 years, p = 0.003). For tumour nuclear grade, there was a statistically significant difference for the intermediate level, with higher accuracy for MRI (p = 0.03). CONCLUSION: MRI was more accurate than mammography in DCIS size assessment when visible, particularly in lesions of intermediate grade and in patients less than 50 years of age. ADVANCES IN KNOWLEDGE: Breast MRI may help in management of DCIS of intermediate grade and in females less than 50 years of age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".