Is Ductal Carcinoma in Situ with “Possible Invasion” More Predictive of Invasive Carcinoma than Pure Ductal Carcinoma in Situ?
Bibliographic record
Abstract
OBJECTIVES: To compare the underestimation of ductal carcinoma in situ (DCIS) vs DCIS with "possible invasion" at breast biopsy and to determine if any factors related to clinical indication, imaging abnormality, biopsy, or DCIS-grade affected the likelihood of underestimation. METHODS: Of 3836 consecutive lesions that were biopsied by using a 14-gauge needle, 117 lesions revealed DCIS. Surgical pathology results of invasive carcinoma were compared with needle biopsy results of DCIS or DCIS with possible invasion. Clinical indication, imaging abnormality, biopsy guidance modality, sample number, and histologic grade were recorded. Yates corrected χ(2) and Fisher exact tests were used to determine differences between groups. RESULTS: A total of 101 lesions were DCIS and 16 were DCIS with possible invasion at biopsy. Thirty-six of 117 lesions (31%) revealed invasive carcinoma at resection pathology. Invasive carcinoma was present more often when DCIS with possible invasion was diagnosed compared with pure DCIS (7/16 [44%] vs 29/101 [29%], P = .36). No factor, including clinical indication, imaging abnormality, biopsy guidance method, sample number, or grade, was found to significantly affect the likelihood of underestimation for lesions diagnosed as DCIS vs DCIS with "possible invasion." The likelihood of pure DCIS underestimation significantly increased when lesions were high grade compared with either intermediate or low grade (18/44 [41%] vs 9/44 [21%] vs 2/10 [20%], P = .03). CONCLUSION: For lesions biopsied by using a 14-gauge needle, there is a trend towards underestimation of the presence of invasive carcinoma when pathology reveals DCIS with possible invasion compared with pure DCIS. High-grade DCIS was significantly more likely to be underestimated.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".