Phyllodes Tumors—The Predictors and Detection of Recurrence
Bibliographic record
Abstract
BACKGROUND: Phyllodes tumors are rare breast neoplasms and the histopathological grade and surgical margins help guide treatment and follow-up. The traditional surgical teaching is resection with ≥10 mm margins, but are narrower surgical margins acceptable? The purpose of our study was to identify predictors of local recurrence. METHODS: A retrospective analysis was performed to identify patients with phyllodes tumors who underwent surgery between 2002 and 2014 using a regional pathology database. Electronic medical records were used to identify surgical management, pathological characteristics, and follow-up encounters. RESULTS: A total of 150 phyllodes tumors were included: 110 of 150 (73%) benign, 21 of 150 (14%) borderline, and 19 of 150 (13%) malignant. At initial surgery, 29 specimens had a positive margin and 15 (56%) underwent re-excision. Seventy tumors had a surgical margin of ≤1 mm, 40 had a margin of 2 to 9 mm, and 11 had a margin of ≥10 mm. There were 11 of 150 (7.3%) locally recurrent tumors: 5 of 11 (45%) benign, 3 of 11 (27%) borderline, and 3 of 11 (27%) malignant. In total, 10 of 11 locally recurrent tumors had a positive margin or ≤1 mm margin at initial surgery. CONCLUSIONS: Phyllodes tumors can have a personalized treatment approach based on histopathological grade and surgical margins. Borderline and malignant phyllodes tumors with a positive or ≤1 mm surgical margin have an increased risk of recurrence. In benign phyllodes tumors, an optimal narrow negative margin may exist but the traditional ≥10 mm excisional margin is not necessary. Local recurrence rates may be sufficiently low in benign phyllodes tumors that imaging can be performed on the presence of clinical symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".