Biopsy-proven stromal fibrosis: what is the appropriated management approach?
Bibliographic record
Abstract
Abstract Purpose: Assess the upgrade rate of biopsy-proven stromal fibrosis without associated atypiato inform the most appropriate management approach in patients with biopsy-proven stromal fibrosis without associated atypia.Materials and Methods: REB-approved consecutive retrospective review of patient’s data of imaging-guided biopsy-proven stromal fibrosis carried out between 2014 and 2019. The upgrade rate of malignancy and the imaging features of the upgraded cases on surgical excision were recorded. The results were correlated with surgical histopathology as the ground truth or an uneventful clinical and radiological follow-up of at least 2 years. Predictors for malignancy were examined, and appropriate statistical tests were applied. Results: Out of 9814 consecutive imaging-guided breast biopsies performed during the study period, 334 patients (4.8%) median age 52 years, had biopsy-proven stromal fibrosis without atypia and fulfilled the inclusion criteria, representing the study cohort. Out of 334 cases, 4 cases (1.2%) were upgraded to malignancy in the two years follow-up. Older patients (> 50 years) are associated with an upgrade to malignancy (p <0.001), and those with a personal history of breast cancer have a trend to upgrade to malignancy (p=0.066). No other clinical and imaging features are related to the malignancy upgrade.Conclusion: For benign radiology-pathology concordant stromal fibrosis on imaging-guided breast biopsy, the upgrade for biopsy results demonstrating stromal fibrosis without atypia is low at 1.2% but not 0; therefore diagnostic follow-up for 2 years as BI-RADS 3 (defined as <2% chance of malignancy) is warranted. Older patient age (> 50 years) and those with a personal history of breast cancer are subgroups of patients that may justify multidisciplinary discussion and possible re-sampling.
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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 | 0.002 | 0.007 |
| 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".