A Multiparametric Serum Marker Panel as a Complementary Test to Mammography for the Diagnosis of Node-Negative Early-Stage Breast Cancer and DCIS in Young Women
Bibliographic record
Abstract
BACKGROUND: The sensitivity of mammography for the detection of small lesions, including node-negative early-stage (T1N0) primary breast cancer (PBC) and ductal carcinoma in situ (DCIS), is significantly decreased in young patients. From a clinical standpoint, an inconclusive mammogram reflects the inability of clinicians to confidently decide whether patients should be referred for biopsy or for follow-up with repeat imaging. METHODS: Specific ELISAs were developed for a panel of 13 well-recognized breast autoantigens (HSP60, FKBP52, PRDX2, PPIA, MUC1, GAL3, PAK2, P53, CCNB1, PHB2, RACK1, RUVBL1, and HER2). Circulating autoantibody levels were measured in a cohort of 396 serum samples from histologically confirmed DCIS (n = 87) or T1N0 PBC (n = 153) and healthy controls (n = 156). RESULTS: Individually, antibodies against CCNB1, FKBP52, GAL3, PAK2, PRDX2, PPIA, P53, and MUC1 demonstrated discriminatory power between breast cancer and healthy control groups. At 90% sensitivity, the overall combined specificity of the autoantibody serum screening test was 42%. Adjustment for higher sensitivities of 95% and 99% resulted in 30% and 21% specificities, respectively (33% and 18% in T1N0 PBC and 28% and 21% in DCIS). Finally, in patients with node-negative early-stage breast cancer younger than 50 years, the autoantibody assay exhibited 59% specificity with a fixed sensitivity at 90%. CONCLUSIONS: Our autoantibody panel allows accurate detection of early breast cancer and DCIS, notably in younger patients. IMPACT: Clinical assessment of this autoantibody panel displays a potential to facilitate clinical management of early-stage breast cancer detection in cases of inconclusive mammogram.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".