Relationship between tumor markers, Ca 15-3 and Ca 125, levels and Sodium-18 Fluoride Positron Emission Tomography Scan in breast cancer bone metastasis
Bibliographic record
Abstract
1232 Objectives: F-18 Sodium fluoride Positron Emission Tomography (NaF PET) is replacing conventional Tc99m-MDP bone scan for bone metastasis investigations, proving to be the more accurate test (1, 2). In breast cancer patients, rising Ca 15-3 and Ca 125 levels may represent tumor recurrence or bone metastasis and may be a poor prognosis factor. Understanding the relationship between Ca 15-3 and Ca 125 and NaF PET findings can potentially clarify the ideal time to request the scan. We decided to review NaF PET scan results performed in the last two years for breast cancer and correlate the results with Ca 15-3 and Ca 125 serum levels. Methods: A total of 359 NaF PET scans in 359 patients were retrospectively analyzed from May 2016 to December 2017 at a single academic university center. The population average age was 62 and the median Ca15-3 and Ca125 levels were 18.2 and 18 U/ml, respectively. NaF PET results were categorized as positive (presence of bone metastasis), negative (absence of bone metastasis) and suspicious (inconclusive for bone metastasis). A receiver operating characteristic curve analysis was conducted. Results: There was statistically significant correlation between NaF PET scan results and tumor markers Ca15-3 and Ca125 values (P-value = 0.001). According to scan result, there was significant difference in Ca15-3 and Ca 125 levels (P-value=0.001). The optimal cut-off value of Ca15-3 and Ca125 in a positive scan was calculated at 31.35 and 27.5 U/ml respectively. (Area under curve= 0.976 and 0.964, P-value =0.011 and 0.014). Conclusions: Knowing the appropriate cutoff values of Ca15-5 and Ca125 allows better timing for bone metastasis investigations using NaF PET.
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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 | 0.003 |
| 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 | 0.001 | 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".