ClinicalTrials.gov
public full text
Public Domain
Artificial Intelligence Prediction Tool in Non-muscle Invasive Bladder Cancer (NMIBC)
This is a multi-center study and the aim is to develop and validate an Artificial Intelligence (AI) -based histologic analysis tool to predict responsiveness to intravesical Bacillus Calmette-Guérin (BCG) and intravesical chemotherapy in intermediate and high-risk non-muscle invasive bladder cancer patients.
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
- Predicting Response to Intravesical Bacillus Calmette-Guérin in High-Risk Nonmuscle-Invasive Bladder Cancer Using an Artificial Intelligence-Powered Pathology Assay: Development and Validation in an International 12-Center Cohort#1092438
- Study on the Staging and Prognosis Model of Bladder Cancer#805315
- Study of Bacillus Calmette-Guerin (BCG) Combined With PANVAC Versus BCG Alone in Adults With High Grade Non-Muscle Invasive Bladder Cancer Who Failed At Least 1 Course of BCG#373594
- Artificial Intelligence in Hepatocellular Carcinoma: Current Applications, Clinical Performance, and Barriers to Implementation.#12801
- Predicting Immunotherapy Response and Survival of Liver Cancer Patients Using Artificial Intelligence and Radiomics (Radiology-AI-Liver)#383564
Record · ID 1137293
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