Improvement in DCIS Detection Rates by MRI Over Time in a High-Risk Breast Screening Study
Bibliographic record
Abstract
Although magnetic resonance imaging (MRI) is much more sensitive than mammography for detecting early invasive breast cancer, in many high-risk screening studies MRI was less sensitive than mammography for detecting ductal carcinoma in situ (DCIS). We reviewed our experience detecting DCIS in our single center study of annual MRI, mammography, ultrasound and clinical breast examination (CBE) for screening very high-risk women. All cases of DCIS±microinvasion and invasive cancer were compared in two time frames: before (period A) and after (period B) July 2001-when we acquired expertise in the detection of DCIS with MRI-with respect to patient demographics, method of detection, and rates of detection of invasive cancer and DCIS. In period A there were 15 cases (3.1% of 486 screens) in 223 women, of which 2 (13%) were DCIS-one with microinvasion-neither detected by MRI. In period B there were 29 cases (3.3% of 877 screens) in 391 women, of which 10 (34%) were DCIS±microinvasion (p=0.04), all 10 detected by MRI but only one by mammography. No DCIS cases were detected by ultrasound or CBE. Specificity was lower in period B than in period A but acceptable. The ability to detect DCIS with screening MRI improves significantly with experience. MRI-guided biopsy capability is essential for a high-risk screening program. In experienced centers the increased sensitivity of MRI relative to mammography is at least as high for DCIS as it is for invasive breast cancer.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".