Prognostic and Predictive Molecular Markers in DCIS
Bibliographic record
Abstract
Eighteen percent of all new breast cancers detected on screening mammography are ductal carcinoma in situ (DCIS), a preinvasive lesion that is highly curable. However, some women with DCIS will develop life-threatening invasive breast cancer. Because the determinants of invasive recurrence are unknown, all women with DCIS require the same treatment (usually with surgery and radiation). Therefore, there is a need to identify biologic markers and create a profile that will provide prognostic information that is more accurate than the currently used van Nuys Index to predict invasive recurrence. In the present review, we examined the many biologic markers studied in breast cancer, describe their main biologic role and their expression in DCIS, and review the various studies regarding their ability to serve as prognostic factors in breast cancer with an emphasis on predicting invasive recurrence in patients with DCIS. This review covers established markers, namely, ER, PR and HER2/neu, that are used routinely to make treatment decisions as well as investigative biologic factors involved in cell proliferation, cell cycle regulation, extracellular molecules, factors involved in extracellular matrix degradation, and angiogenesis. However, controversies exist regarding the value of these prognostic factors, their interrelationship, and their advantages over morphologic evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".