De Novo Metastatic Renal Cell Carcinoma Diagnostic Nomogram Data (2016–2025)

Xu Chen developed a diagnostic nomogram for predicting metastasis in initially diagnosed renal cell carcinoma patients. The dataset likely contains clinical variables used to build the model, including lymphocyte-to-monocyte ratio, serum albumin-to-alkaline phosphatase ratio, and PADUA score. The retrospective study involved patients treated at the First Hospital of Lanzhou University from January 2016 to August 2025.

Use Cases

• Validate predictive models for renal cell carcinoma metastasis based on the identified independent predictors (LMR, AAPR, PADUA score)
• Benchmark machine learning algorithms (LASSO, SVM-RFE, Boruta) for variable selection in clinical datasets
• Assess model performance metrics (ROC curves, AUC, calibration curves, DCA) for diagnostic nomograms
• Study the relationship between systemic immune-inflammatory biomarkers and cancer metastasis
• Reproduce or extend the single-center study's findings using the underlying clinical data

Strengths

• Model validation performed with a training and validation cohort split in a 7:3 ratio
• The nomogram achieved an AUC of 0.771 in training and 0.747 in validation
• Three independent predictors were identified with specific odds ratios and confidence intervals

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The study is single-center and retrospective; data may reflect geographic and methodological bias

Provenance

Source

Xu Chen

Collection Method

Retrospective analysis of patients treated at the First Hospital of Lanzhou University

Time Range

January 2016 to August 2025

Freshness

Last updated 2026-03-19 06:49:52

Geography

Lanzhou University hospital (single-center study)