Circulating tumor cell number and endocrine therapy index in ER positive metastatic breast cancer patients
Bibliographic record
Abstract
Circulating tumor cells (CTC) are prognostic in metastatic breast cancer (MBC). The CTC-endocrine therapy index (CTC-ETI), consisting of CTC-ER (estrogen receptor), BCL2, human epidermal growth factor receptor (HER2), and Ki67 expression, might predict resistance to endocrine therapy (ET) in patients with ER-positive MBC. One hundred twenty-one patients with ER-positive/HER2-negative MBC initiating a new ET after ≥1 lines of ET were enrolled in a prospective, multi-institutional clinical trial. CTC-ETI and clinical/imaging follow-up were performed at baseline and serial time points. Progression-free survival (PFS) and rapid progression (RP; determined at the 3-month time point) were primary endpoints. Associations with clinical outcomes used logrank and Fisher's exact tests. At baseline, 36% (38/107) of patients had ≥5 CTC/7.5 ml whole blood (WB). Patients with ≥5 vs. <5 CTC/7.5 ml WB had significantly worse PFS (median 3.3 vs. 5.9 months, P = 0.03). Elevated CTC at 1 month was associated with even worse PFS (1.9 vs. 5.0 months from the 1-month sample, P < 0.001). Low, intermediate, and high CTC-ETI were observed in 71 (66%), 8 (8%), and 28 (26%) patients, with median PFS of 6.9, 8.5, and 2.8 months, respectively (P = 0.008). Patients with high vs. low CTC and CTC-ETI more frequently experienced RP (CTC: 66% vs. 41%; P = 0.03; CTC-ETI: 79% vs. 40%; P = 0.002). In conclusion, CTC enumeration and the CTC-ETI assay are prognostic at baseline and follow-up in patients with ER-positive/HER2-negative MBC starting new ET. CTC at first follow-up might identify a group of patients with ER-positive MBC that could forego ET, but CTC-ETI did not contribute further.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".