Measurement of Serum Levels of Macrophage Inhibitory Cytokine 1 Combined with Prostate-Specific Antigen Improves Prostate Cancer Diagnosis
Bibliographic record
Abstract
PURPOSE: Current serum testing for the detection of prostate cancer (PCa) lacks specificity. On diagnosis, the optimal therapeutic pathway is not clear and tools for adequate risk assessment of localized PCa progression are not available. This leads to a significant number of men having unnecessary diagnostic biopsies and surgery. A search for novel tumor markers identified macrophage inhibitory cytokine 1 (MIC-1) as a potentially useful marker. Follow-up studies revealed MIC-1 overexpression in local and metastatic PCa whereas peritumoral interstitial staining for MIC-1 identified lower-grade tumors destined for recurrence. Consequently, we sought to assess serum MIC-1 measurement as a diagnostic tool. EXPERIMENTAL DESIGN: Using immunoassay determination of serum MIC-1 concentration in 1,000 men, 538 of whom had PCa, we defined the relationship of MIC-1 to disease variables. A diagnostic algorithm (MIC-PSA score) based on serum levels of MIC-1, total serum prostate-specific antigen, and percentage of free prostate-specific antigen was developed. RESULTS: Serum MIC-1 was found to be an independent predictor of the presence of PCa and tumors with a Gleason sum > or =7. We validated the MIC-PSA score in a separate population and showed an improved specificity for diagnostic blood testing for PCa over percentage of free prostate-specific antigen, potentially reducing unnecessary biopsies by 27%. CONCLUSIONS: Serum MIC-1 is an independent marker of the presence of PCa and tumors with a Gleason sum of > or =7. The use of serum MIC-1 significantly increases diagnostic specificity and may be a future tool in the management of PCa.
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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) | 0.001 | 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".