Preoperative Plasma Insulin-Like Growth Factor-I and Its Binding Proteins-Based Risk Stratification of Patients Treated With Radical Nephroureterectomy for Upper Tract Urothelial Carcinoma
Bibliographic record
Abstract
INTRODUCTION: We evaluate the predictive and prognostic value of insulin-like growth factor-I (IGF-1), IGF binding protein-2 (IGFBP-2) and -3 (IGFBP-3) in patients treated with radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC). METHODS: This is a retrospective analysis of a multi-institutional database comprising 753 patients who underwent RNU for UTUC and had a preoperative plasma available. Logistic and Cox regression analyses were performed. The discriminative ability and clinical utility of the models was calculated using the lasso regression test, area under receiver operating characteristics curves, C-index, and decision curve analysis (DCA). RESULTS: Lower preoperative plasma levels of IGFBP-2 and -3 independently correlated with increased risks of lymph node metastasis, pT3/4 disease, nonorgan confined disease, and worse recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS) (all P ≤ .004). The addition of both IGFBP-2 and -3 to a postoperative multivariable model, that included standard clinicopathologic characteristics, improved the model's concordance index by 10%, 9%, and 8% for RFS, CSS, and OS, respectively. On DCA, addition of both IGFBP-2 and -3 to base models improved their performance for RFS, CSS, and OS by a statistically and clinically significant margin. Plasma IGF-1 was not associated with any of outcomes. CONCLUSIONS: We confirmed that a lower plasma levels of IGFBP-2 and -3 both are independent and clinically significant predictors of adverse pathological features and survival outcomes in UTUC patients treated with RNU. These findings might help guide the clinical decision-making regarding perioperative systemic therapy and follow-up scheduling.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".