Predictive Value of Plasma Hepatocyte Growth Factor/Scatter Factor Levels in Patients with Clinically Localized Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Hepatocyte growth factor/scatter factor (HGF/SF) is a multifunctional cytokine that is involved in cancer growth, motility, invasion, and angiogenesis. We assessed whether preoperative plasma levels of HGF can enhance the accuracy of standard models for predicting pathologic features and clinical outcomes. EXPERIMENTAL DESIGN: The study comprised 421 consecutive patients treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostatic adenocarcinoma. HGF/SF was measured using a commercially available immunoassay. Multivariate logistic regression was used to assess the relationship between plasma HGF/SF and pathologic features. Multivariate Cox regression was used to predict disease recurrence. One thousand bootstrap replicates were created for internal validation and predictive accuracies were estimated for each model. RESULTS: Plasma HGF/SF levels were significantly elevated in patients with lymph node and/or seminal vesicle invasion (P < 0.0001 and P = 0.007, respectively). Preoperative plasma HGF/SF level was an independent predictor of lymph node invasion [odds ratio (OR) for every 100 pg/mL increase in HGF/SF, 1.82; 95% confidence interval (95% CI), 1.33-2.49] and seminal vesicle invasion (OR, 1.18; 95% CI, 1.06-1.3). Addition of HGF/SF increased the accuracy of a base model that included standard preoperative variables for prediction of lymph node invasion by 6.7% (predictive accuracy, 98.4%). HGF/SF also independently predicted disease recurrence after surgery (hazard ratio, 1.07; 95% CI, 1.0-1.15). CONCLUSIONS: Preoperative plasma level of HGF/SF is an independent predictor of prostate cancer metastasis to lymph nodes and disease recurrence after surgery. Use of HGF may help in therapeutic decision-making and enrollment into clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".