Association of tumor markers CA 15-3, CEA, and CA 125 with [18F]NaF PET findings in breast cancer patients
Bibliographic record
Abstract
Introduction Breast cancer often metastasizes to bone, and [ 18 F]NaF PET is commonly used to detect skeletal involvement. This study examines the association of serum CA 15-3, CEA, and CA 125 with [ 18 F]NaF PET findings in breast cancer to guide clinical decision-making. Methods This retrospective study included 360 breast cancer patients who underwent [ 18 F]NaF PET. Associations between serum tumor markers (CA 15-3, CEA, CA 125) and [ 18 F]NaF PET findings and lesion count were analyzed. Optimal cut-off values for predicting [ 18 F]NaF PET positivity were determined using ROC analysis. Multivariable logistic regression identified independent predictors. Results Among 360 patients (mean age 61.1 ± 13.9 years), median serum CA 15-3, CEA, and CA 125 levels were significantly elevated in patients with positive versus negative PET scans (all p<0.001). Marker levels revealed a dose–response relationship, rising with increasing numbers of skeletal lesions. In multivariable analysis, CA 15-3 (OR 1.053, p=0.002) and CEA (OR 1.264, p=0.001) independently predicted PET positivity, whereas CA 125 showed a marginal trend (p=0.081). ROC analysis identified optimal cut-offs of 19.25 U/mL for CA 15-3 (sensitivity 70.1%, specificity 90.4%, AUC 0.837) and 3.15 ng/mL for CEA (sensitivity 65.6%, specificity 85.2%, AUC 0.821). Combined model incorporating all three markers (probability cut-off 0.29) improved diagnostic performance (AUC 0.863; sensitivity 79.7%, specificity 92.3%). Invasive lobular histology and restaging indication were significant predictors of PET positivity. Conclusion Elevated CA 15–3 and CEA independently predict [ 18 F]NaF PET positivity in breast cancer. Optimal cut-offs were 19.25 U/mL for CA 15-3 (sensitivity 70.1%, specificity 90.4%, LR + 7.38, AUC 0.837) and 3.15 ng/mL for CEA (sensitivity 65.6%, specificity 85.2%, LR + 4.43, AUC 0.821). The clinical utility of CA 15–3 and CEA lies in rule-in and risk-stratification strategies. Patients above these thresholds, particularly those with invasive lobular carcinoma undergoing restaging, may benefit from prioritized [ 18 F]NaF PET evaluation or improved interpretation of equivocal PET findings. CA 15–3 threshold, lower than routine laboratory reference, may guide aggressive screening and prioritized [ 18 F]NaF PET in patients with high clinical suspicion. Multivariable model combining CA 15-3, CEA, and CA 125 (probability cut-off 0.29) improved diagnostic performance (sensitivity 79.7%, specificity 92.3%, AUC 0.863). Integrating CA 15–3 and CEA into clinical decision-making may enable a nuanced, risk-adapted approach, optimizing metastasis detection and resource allocation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".