Examining the value PSA=10ng/ml as a cutoff for predicting metastatic bone disease in NaF18 PET/CT bone scans: a pilot study
Bibliographic record
Abstract
Objectives: NaF18 PET/CT is considered to be more sensitive than Tc99m-MDP bone scan in detecting osseous metastasis. Some studies have suggested that for newly diagnosed prostate cancer patients with PSA<10ng/ml, a Tc99m-MDP bone scan is unnecessary. The main goal of this study is to assess if PSA= 10ng/ml is a good cutoff value to predict metastatic bone disease in newly diagnosed prostate cancer patients imaged with NaF18 PET/CT. Methods: From the NaF18 PET/CT ordered to evaluate for prostate cancer metastasis between January 2010 and April 2011 (n=91), newly diagnosed biopsy proven prostate cancer cases before treatment were chosen (n=28). The sample was divided into two groups: group I (bone metastasis) and group II (no bone metastasis). PSA values were also reviewed. Results: Group I (n=4) had mean PSA 121.29ng/ml, range 8.9-297.55ng/ml, mean age 74.5 years) and group II (n=24) had (mean PSA 27.43ng/ml, range 0.05- 348.68ng/ml, mean age 69.6 years). In our sample, 1 patient (25%) from group I with PSA<10ng/ml had bone metastasis. PSA cutoff value of 10ng/ml has a negative predictive value of 92.86% (odds ratio=3.55, 95% confidence interval 0.32 to 39.14, P =0.596). Conclusions: For our study with veterans, there appears to be no significant relationship between PSA of < 10ng/ml and negative bone metastasis in newly diagnosed prostate cancer cases. 1 in 4 patients with PSA<10ng/ml from group I had bone metastasis. With the new introduction of NaF18 PET/CT as a more sensitive technique than MDP-99m whole body bone scans, we question the strict use of PSA=10ng/ml as a cutoff value. Age, race and region specific guidelines for bone scan use need to be developed. Both retrospective and prospective studies involving multiple institutions and larger sample sizes are needed to further confirm the association between PSA value alone and positive NaF18 PET/CT bone scans.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".