The Role of Pre-Contrast Sequences of an MRI-Guided Breast Biopsy in Localizing a Target
Bibliographic record
Abstract
Objective: This retrospective study aims to assess the role of pre-contrast sequences of an MRI-guided breast biopsy (MRIB) exam in confident and accurate lesion site localization based on tissue landmarks. Methods: The charts of all consecutives MRIB that were performed between January 2018 and December 2020 were reviewed. The images of the eligible exams were analyzed by 3 breast radiologists. Each radiologist independently attempted to identify lesion site on pre-contrast MRIB sequences, while blinded to the post-contrast MRIB images. Confidence levels (I-confident, II-not confident, and III-unknown) were assigned by each reviewer. A fourth radiologist assessed the accuracy (≤5 mm-accurate, >5 mm-inaccurate) in lesion site localization using the actual biopsied lesion site and the post-contrast MRIB images as reference. Descriptive statistics were used to calculate the percentage of confidence and accuracy categories for each reviewer, with Chi-square tests applied to analyze relationships between categorical variables. Results: There were 174 female patients with 181 lesions eligible for the trial. When the lesion site is confidently identified on the pre-contrast MRIB images (level 1 confidence), mean grade 1 accuracy was 93.8% ( P < .001). Accuracy decreased with Level II and III confidence (55.3% and 34.2% respectively). Up to 61.4% improved accuracy was demonstrated when combining the performance of 2 reviewers. No correlation was found between breast density, lesion morphology, or biopsy positioning with confidence level or accuracy grade. Conclusion: Careful review of the pre-contrast MRIB images and familiarization with the surrounding tissue landmarks are important steps in confidently and accurately detecting lesion site.
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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 | 0.011 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".