MRI detection of distinct incidental cancer in women with primary breast cancer studied in IBMC 6883
Bibliographic record
Abstract
BACKGROUND: Prior single institution studies suggest MRI may improve the assessment of the extent of cancer within the breast, and thus reduce the risk of leaving macroscopic disease in the breast following breast conservation therapy. We report on the rate of MRI and mammography detection of foci of distinct incidental cancer in a prospective, multi center trial involving 426 women with confirmed breast cancer at 15 institutions in the US, Canada, and Germany. METHODS: Women underwent mammography and MRI prior to biopsy of the suspicious index lesion. Additional incidental lesions (IL) greater than 2 cm from the index lesion that were detected by mammography and MRI were noted and characterized. Biopsy recommendations were associated with ILs given an assessment of suspicious or highly suspicous (BiRads 4 and 5). These assessments were considered a positive test. RESULTS: MRI had a significantly higher yield of confirmed cancer ILs than mammography (0.18 (95%CI: 0.142-0.214) for MRI versus 0.072 (95%CI: 0.050-0.100) for mammography). The cancer ILs detected by MRI alone appeared to be similar to those detected by mammography with respect to size and histology. The percentage of biopsies of ILs that resulted in a cancer diagnosis was similar between the modalities (MRI 0.72(95%CI: 0.6-0.81); Mammography 0.85 (95%CI: 0.62-0.96)). CONCLUSIONS: These results suggest that consideration needs to be given regarding the integration of breast MRI into the pretreatment evaluation of women seeking breast conservation therapy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".