NCBI PubMed Central
open access
Development and validation of an integrated machine learning model for recurrence-free survival prediction in clear cell renal cell carcinoma.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120)#378014
- Application of Radiomics-based AI Models in Predicting Clinical Outcome of Patients With Renal Cell Carcinoma After Surgical Treatment#897480
- Machine Learning Models for Predicting Liver Metastasis at Diagnosis and Overall Survival in Ovarian Cancer: A SEER-Based Study.#244278
- Machine Learning Models for Disease-Free Survival Analysis after Liver Resection for Hepatocellular Carcinoma: A Multicentric French Collaborative Study#308231
- Predicting Local and Distant Recurrence in T1 Colorectal Cancer#885054
Record · ID 439328
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive