A Preliminary Analysis of Circulating Tumor Microemboli from Breast Cancer Patients during Follow-Up Visits
Bibliographic record
Abstract
BACKGROUND: Most breast cancer-related deaths are caused by distant metastases and drug resistance. It is important to find appropriate biomarkers to monitor the disease and to predict patient responses after treatment early and accurately. Many studies have found that clustered circulating tumor cells, with more correlations with metastatic cancer and poor survival of patients than individual ones, are promising biomarkers. METHODS: Eighty samples from eleven patients with breast cancer during follow-up visits were examined. By using a microfluidic chip and imaging system, the number of circulating tumor cells and microemboli (CTC/CTM) were counted to assess the distribution in stratified patients and the potential in predicting the disease condition of patients after treatments during follow-up visits. Specific components and subtypes of CTM were also preliminarily investigated. RESULTS: Compared to CTC, CTM displayed a distinguishable distribution in stratified patients, having a better AUC value, in predicting the disease progression of breast cancer patients during follow-up visits in this study. Four subtypes were categorized from the identified CTM by considering different components. In combination with CEA and CA153, enumerated CTC and CTM from individual patients were applied to monitor the disease condition and patient response to the therapy during follow-up visits. CONCLUSIONS: The CTM and its subtypes are promising biomarkers and valuable tools for studying cancer metastasis and longitudinally monitoring cancer patients during follow-up visits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".