Characteristics associated with upgrading to invasiveness after surgery of a DCIS diagnosed using percutaneous biopsy.
Bibliographic record
Abstract
BACKGROUND/AIM: Ductal carcinoma in situ (DCIS) is a non-invasive malignant breast lesion. Patients diagnosed with a DCIS on percutaneous biopsy usually undergo resection, and the final pathology may reveal that the lesion was in fact invasive (upgrading at surgery), this leading to treatment strategy change during its course. The aim of the present study was to identify factors associated with DCIS-upgrading to invasive carcinoma at surgery, and to identify a subgroup of patients more likely to have an invasive cancer. PATIENTS AND METHODS: A retrospective study was performed in patients diagnosed with DCIS on percutaneous biopsy between April 1997 and December 2010. Based on available data and on previous studies, 21 clinical, radiological and pathological variables were evaluated using univariate analyses. Variables identified in univariate analyses, when p≤0.10, were included in a multivariate model. RESULTS: Among 608 DCIS lesions, 177 (29.1%) were invasive carcinomas after surgery. Using univariate analyses, core needle biopsy (odds ratio (OR)=1.8), physical symptoms (OR=2.9), palpable masses (OR=4.1), number of specimen obtained (1-9 cores, OR=2.2) and a measurable mammographic lesion (OR=1.7) were significantly associated with upgrading at surgery. However, using multivariate analysis, no factor was significantly associated. CONCLUSION: No characteristic was identified to be independently associated with DCIS upgrading at surgery, and no sub-group of patients could be identified in whom the appropriate surgery could have been performed first.
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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".