Clinical utility of human glandular kallikrein 2 within a neural network for prostate cancer detection
Bibliographic record
Abstract
OBJECTIVE: To assess, using artificial neural networks (ANNs), human glandular kallikrein 2 (hK2), prostate-specific antigen (PSA), and percentage free/total PSA (f/tPSA), for discriminating between prostate cancer and benign prostatic hyperplasia (BPH). MATERIAL AND METHODS: Serum samples from 475 patients with prostate cancer (n = 347) or BPH (n = 128) within the PSA range of 1-20 ng/mL were analysed for tPSA, fPSA and hK2 (research assay, Toronto, Canada). Data were analysed in the ranges of 1-4, 2-4, 4-10, and 2-20 ng/mL tPSA. Back-propagation ANN models with the variables PSA, f/tPSA, and hK2, hK2/fPSA and hK2/(f/tPSA) were constructed. The diagnostic validity was evaluated by receiver-operating characteristic (ROC) curve analysis. RESULTS: Whereas the median concentration of hK2 was not significantly different between patients with BPH or prostate cancer in any of the tPSA ranges, the f/tPSA, hK2/fPSA and hK2/(f/tPSA), and the hK2-based ANN outputs were always significantly different between patients with prostate cancer or BPH. Using ROC curve comparison, all variables were significantly better than hK2 in all ranges. The hK2-based ANN performed better than f/tPSA except in the 4-10 ng/mL tPSA range. At 90% and 95% sensitivity, the hK2-based ANN was also significantly better than f/tPSA in the 1-4 ng/mL tPSA range. hK2/(f/tPSA) achieved equal results to the hK2-based ANN except in the range 2-20 ng/mL tPSA. CONCLUSIONS: The hK2-based ANN improves the outcome of f/tPSA but not hK2/(f/tPSA) in almost all analysed subgroups. When comparing the results at 90% and 95% sensitivity the hK2-based ANN only performed significantly better than f/tPSA in the lowest tPSA range. Only in lower tPSA ranges do hK2-based ANNs show an advantage for further improving prostate cancer detection.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".