The Utility of Short-Interval Follow-Up for Baseline High-Risk Screening Breast MRI
Bibliographic record
Abstract
OBJECTIVE: Patients with a high lifetime risk of developing breast cancer undergo annual screening magnetic resonance imaging (MRI) starting at age 30. A proportion of these patients are subsequently required to undergo short-interval follow-up MRI 6 months after their baseline examination. The purpose of this study was to evaluate the utility and outcome of these short-interval follow-up examinations in this population. METHODS: A retrospective review was performed of 523 patients who received their baseline high-risk breast screening MRI at our institution between 2013 and 2017. The proportion of patients who received a short-interval follow-up MRI at 6 months was recorded. The findings at baseline and follow-up MRI were recorded, as well as the outcomes and results of any interventions performed. RESULTS: Ninety-six (17.6%) patients (age range: 25-67, mean age: 41) received a short-interval follow-up MRI following their baseline screening examination. Indications for follow-up included moderate to marked background parenchymal enhancement, nonmass enhancement, and likely benign enhancing mass. Of the 92 patients, 5 (5.4%) went on to have a biopsy, with none revealing a malignant pathology. The remainder either returned to routine screening (91.3%) or received further imaging in the form of ultrasound or additional follow-up MRI (3.3%). CONCLUSION: Short-interval follow-up breast MRI in high-risk patients after a baseline screening study with likely benign findings is unlikely to yield clinically significant findings. This retrospective study can be considered a starting point for additional future work looking at the rate, indications, and yield of short-interval follow-up following baseline high-risk screening breast MRI studies.
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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 | 0.023 |
| 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".