Ductal Carcinoma In Situ of the Breast: Can Biomarkers Improve Current Management?
Bibliographic record
Abstract
BACKGROUND: Screening for invasive cancer has led to a marked increase in the detection of ductal carcinoma in situ (DCIS). DCIS is, if appropriately managed, a low-risk disease which has a small chance of impacting on patient life expectancy. However, despite significant advances in prognostic marker development in invasive breast cancer, there are no validated diagnostic assays to inform treatment choice for women with DCIS. Therefore we are unable to target effective treatment strategies to women at high risk and avoid over-treatment of women at low risk of progression to invasive breast cancer. Paradoxically, one effect of this uncertainty is undertreatment of some women. CONTENT: We review current practice and research in the field to identify key challenges in the management of DCIS. The impact of clinical research, particularly on the over and undertreatment of women with DCIS is assessed. We note slow progress toward development of diagnostic biomarkers and highlight key opportunities to accelerate advances in this area. SUMMARY: DCIS is a low-risk disease, its incidence is increasing, and current treatment is effective. However, many women are either over- or undertreated. Despite repeated calls for development of diagnostic biomarkers, progress in this area has been slow, reflecting a relative lack of investment of research effort and funding. Given the low event rate in treated patients and the lateness of recurrences, many previous studies have only limited power to identify independent prognostic and predictive biomarkers. However, the potential for such biomarkers to personalize treatment for DCIS is extremely high.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".