Prognostic Value of Hepatocyte Growth Factor for Muscle-invasive Bladder Cancer
Bibliographic record
Abstract
Abstract PurposeThe HGF/MET pathway is involved in cell motility, angiogenesis, proliferation, and cancer invasion. We assessed the clinical utility of plasma HGF level as a prognostic biomarker in patients with MIBC.MethodsWe retrospectively analyzed 565 patients with MIBC who underwent radical cystectomy. Logistic regression and Cox regression models were used, and predictive accuracies were estimated using the area under the curve and concordance index. To estimate the clinical utility of HGF, DCA and MCID were applied.ResultsPlasma HGF level was significantly higher in patients with advanced pathologic stage and LN metastasis ( p =0.01 and p <0.001, respectively). Higher HGF levels were associated with an increased risk of harboring LN metastasis and non-organ-confined disease (OR1.21, 95%CI 1.12-1.32, p <0.001, and OR1.35, 95%CI 1.23-1.48, p <0.001, respectively) on multivariable analyses; the addition of HGF improved the predictive accuracies of a standard preoperative model (+7%, p <0.001 and +8%, p <0.001, respectively). According to the DCA and MCID, half of the patients had a net benefit by including HGF, but the absolute magnitude remained limited. In pre- and postoperative predictive models, a higher HGF level was significant prognosticator of worse RFS, OS, and CSS; in the preoperative model, the addition of HGF improved accuracies by 6% and 5% for RFS and CSS, respectively.ConclusionPreoperative HGF identified MIBC patients who harbored features of clinically and biologically aggressive disease. Plasma HGF could serve, as part of a panel, as a biomarker to aid in preoperative treatment planning regarding intensity of treatment in patients with clinically MIBC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".